esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Extraction of the Final Regenerative Areas  
The extraction of vegetation reproduction areas using satellite imagery aims to evaluate the correct area by focusing on correcting all distortions related to image quality. Distortion was removed using three vegetation indices, and the exact boundary was extracted through clustering analysis. The intersection of the regions derived from the three vegetation indices is judged as the vegetation regeneration region.  
Figure 7: Cluster analysis of vegetation indices: (**a**) Normalized Difference Vegetation Index (NDVI), (**b**) Enhanced Vegetation Index (EVI), and (**c**) Soil-Adjustment Vegetation Index (SAVI).
# Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
## 4 Cooperative Multi-Objective Optimization Model
### Inner-Loop Optimization
#### 4.4.1 Energy Transaction Cost  
The energy transaction cost is the sum of RESs cost, energy exchange cost between MMGS and EVs, MMGS and distribution network, and the additional cycle cost of EV batteries. \(P_{m,I}^{EV}\), \(P_{m,I}^{PV}\), \(P_{m,I}^{WT}\), and \(P_{m,I}^{G}\) are the optimization variables.  
\[C_{exm}=C_{resm}-C_{evm}+C_{gm}+C_{cym} \tag{18}\]  
\[C_{resm}=C_{PVm}+C_{WTm} \tag{19}\]  
\[C_{PVm}+C_{WTm}=\sum_{t=1}^{T}P_{m,I}^{PV}C_{m,I}^{PV}\Delta t+\sum_{t=1}^{T}P _{m,I}^{WT}C_{m,I}^{WT}\Delta t \tag{20}\]  
\[C_{evm}=\sum_{t=1}^{T}\sum_{n=1}^{N}P_{m,n,t}^{EV}\bullet\gamma_{CEV}C_{m,I}^{ CEV}\Delta t\ \,\ \ \ if\ \ \ P_{m,n,t}^{EV}\geq 0 \tag{21}\]  
\[C_{evm}=\sum_{t=1}^{T}\sum_{n=1}^{N}P_{m,n,t}^{EV}C_{m,I}^{DEV}\Delta t\,\ \ \ if\ \ \ P_{m,n,t}^{EV}<0 \tag{22}\]  
\[C_{gm}=\sum_{t=1}^{T}P_{m,t}^{G}C_{m,I}^{G}\Delta t \tag{23}\]  
\[C_{cym}=\sum_{n=1}^{N}k_{cy}C_{cym}^{EV} \tag{24}\]  
where \(C_{resm}\) is the cost of RESs of the \(m\)-th MG in a day, \(C_{PVm}\), and \(C_{WTm}\) are the cost of PVs and WTs. \(P_{m,t}^{PV}\) is the power output of PVs in the \(m\)-th MG, at \(t\)-th hour, \(C_{m,I}^{PV}\) is the PV power generation cost, \(P_{m,I}^{WT}\) is the power output of WTs, \(C_{m,I}^{WT}\) is the WT power generation cost. \(C_{evm}\) is the cost of energy exchange between MMGS and EVs, \(C_{m,I}^{CEV}\) is the charging price of EVs in \(m\)-th MG, \(C_{m,I}^{DEV}\) is the discharging price, \(\Delta t=1\) h, \(T=24\) h. \(C_{gm}\) is the energy exchange cost between the MG and the distribution network through the DC/AC converters, \(P_{m,I}^{G}\) is the active power output between the MG and the distribution network through the DC/AC converters. If \(P_{m,I}^{G}\geq 0\), MG purchases electricity from the distribution network. If \(P_{m,I}^{G}<0\), MMGS sells electricity to the distribution network. \(C_{m,I}^{G}\) is the electricity price that MG purchases/sells to the distribution network. \(C_{cym}\) is the additional cycle cost of EV batteries, \(C_{cym}^{EV}\) is the additional battery charging/discharging cycle cost of \(n\)-th EV, \(k_{cy}\) is the number of additional charging/discharging cycles, \(N\) is the number of EVs.
# A QoS-Enabled Medium-Transparent MAC Protocol for Fiber-Wireless 5G RAN Transport Networks
## 3 The QoS-Aware MT-MAC Protocol  
Section 3.1 summarizes the general operational rules of the SoA MT-MAC protocols presented in [12; 19; 20] that are in direct relation to the medium arbitration process, using the more recent gMT-MAC protocol as an example [20]. A more detailed and in-depth description of these protocols is provided in the respective references. Sections 3.2 and 3.3 proceed with describing the qMT-MAC protocol proposed in this paper, which, as opposed  
Figure 1: System model depicting the Point-to-Multipoint topology of the qMT-MAC A-RoF transport network: the MNO centralized equipment is connected to the qMT-MAC Central Office, while the MNO remote equipment is connected to the qMT-MAC nodes.  
to the works presented in [12; 19; 20], introduces additional features capable of offering flow prioritization and QoS features for delegation of X-Haul traffic.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### Intelligent Algorithm-Based Methods
#### 5.2.6 Reinforcement Learning-Based Methods  
Reinforcement learning (RL) was proposed by [PERSON] _et al._ in 1996 [21], and [PERSON] and [PERSON] in 1998 [22], respectively. RL is a sub-area of machine learning. RL uses a computer program to generate patterns or rules from a large data set. RL is inspired by animal learning. In reinforcement learning, if the behavior strategy has a positive effect on the environment, then the agent will reinforce this behavior strategy in future. The object is to find the optimization strategy in every discrete state and summarize the maximum reward. RL regards learning as a process of exploration. The agent chooses an action to act on the environment, and the environment changes according to the action. Meanwhile, the environment will generate a reinforcement signal for the agent. The agent chooses the next step action according to the environment and the reinforcement signal. The principle of action selection is to maximize the positive reward probability. The action can not only affect the value of the reinforcement signal, but also the environment status. In RL, the reinforcement signal is just an evaluation of the agent action. Due to the fact the environment can only provide a little information, the agent must be able to rely on its own experience. Based on this method, the agent can learn knowledge from the action, and modify the action strategy to adapt to the environment.  
In [96], based on the machine learning concept, the authors proposed an energy efficient and lifetime extending routing protocol. In [96], the nodes calculate the Q-values of their neighbors and choose the neighbors with highest Q-value as the next hop forwarders. By making use of the RL technique, this protocol can effectively adapt the dynamic network topology. Like [96], in [97], the nodes choose the next hop relay nodes based on the node ID and the Q-value. The neighbors whose ID is the same with source node and the Q-value is highest among the neighbor nodes will be chosen as the relay nodes. The work in [98] uses reinforcement learning to provide a proactive link failure resilient routing protocol for MANETs (Mobile _Ad-Hoc_ Networks). In [99], nodes in a cooperative node group will be considered as opponents to each other; therefore, each node will maintain a Q-value which reflects the payoff that would have been received if that node selects one action and the other nodes jointly selected the other action. After that, the node with the highest total payoff will be elected to forward the data packet to the next cooperative node group towards the sink node. More reinforcement learning-based routing protocols can be found in [100; 101].
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 3 Dynamic RID Model with Sliding Time Window
### Dynamic Path Calculation Using Fuzzy Logic
For example, for Arc turns, the range [0 15] is defined as very low, range [5 45] and so on as listed in Table 2.  
Appropriate fuzzy rules are synthesised considering all parameters with the relevant importance in disaster environments. Some of the synthesized rules are presented below: \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Fuzzy Variables/** & & & & \\
**Values** & & & & \\ \hline Road Condition & Very poor & Poor & Average & Good \\ \hline  & [0 20] & [10 55] & [40 80] & [75 100] \\ \hline Traffic Condition & Low & Average & Heavy & Obstructed \\ \hline  & [0 20] & [10 50] & [35 70] & [65 100] \\ \hline Number of Turns & Very low & Low & Medium & High \\ \hline  & [0 15] & [5 45] & [30 70] & [60 100] \\ \hline Weight Factor & Very low & Low & Medium & High \\ \hline  & [0 25] & [10 45] & [35 80] & [60 110] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of fuzzy variables and values. Figure 2: The plot of fuzzy variables with linguistic sets. Example of the Fuzzy rules  
If Arc_condition = V-poor and Arc_load = Obstructed and Arc_turns = High Then Weight_factor = V_low. If Arc_condition = Poor and Arc_load = Average and Arc_turns = Average Then Weight_factor = Low. If Arc_condition = Average and Arc_load = Low and Arc_turns = V_low Then Weight_factor = High. If Arc_condition = Good and Arc_load = Average and Arc_turns = Average Then Weight_factor = Medium. If Arc_load = Average or Arc_turns = Average Then Weight_factor = Low. Defuzzification is applied to get a value of weight factor by applying an inferred fuzzy reasoning. The standard Centre of Gravity method is used for the approximation of the distribution of the fuzzy sets. After defuzzification, each crisp arc length in the distance matrix is changed into a fuzzified distance matrix. This fuzzified distance matrix is used to find the shortest equivalent route between any two links by applying the Floyd-Warshall algorithm as this algorithm has better performance in finding the all-pairs shortest paths in the dense graph [56]. The fuzzified distance and its impact on travel time are illustrated in the following example. Example:  
Distance between supply point 30 to disaster region 1: 20.75 km  
Vehicle speed: 40 km per hour  
Travel Time (based on: traditional distance/vehicle speed): 31.12 min  
Assume fuzzy variables as:  
Arc-Condition = Poor, Arc-Load = Heavy, Arc-Turns = High Arc-Weight-Factor (Applying fuzzy variables and rules): 0.357  
Effective length: 20.75 + 20.75 + (1 - 0.357): 34.092 km  
(based on the synthesised fuzzy system)  
Travel Time (based on: fuzzified distance/vehicle speed): 46.68 min  
From the above example, it has been clearly observed that the travel distance and, hence, the travel time is changed because of the changed condition of the routes. This signified that for an effective travel plan, all the routes' travel time needs to be calculated as an effective travel time.
# Design and Implementation of 24-GHz and 48-GHz VCOs Using Noise Filtering Technique in 90-nm CMOS
## 4 Conclusions  
Two low phase noise voltage-controlled oscillators were developed and implemented using CMOS 90-nm process. One is implemented in the 24-GHz frequency band, and the other is based on a push-push topology with a \(\lambda/4\) transmission line that ranges up to the 48-GHz band. The good agreement between the simulated and measured results demonstrates the feasibility of the design concept. In both circuit architectures, adding the LC filtering technique at the tail current source can effectively reduce circuit phase noise; furthermore, in the push-push VCO, the use of the quarter-wavelength transmission line architecture can effectively reduce power consumption. This paper also proves that the push-push architecture can achieve a high-frequency oscillation in even a standard process, which can be applied to the RF transceiver system in the millimeter wave frequency band and to short-range radar applications to provide stable local signals.  
Conceptualization, C.-C.K.; methodology, C.-C.K.; software, C.-C.K.; validation, C.-C.K.; formal analysis, C.-C.K.; investigation, C.-C.K. and S.W.; resources, S.W.; data curation; writing--original draft preparation, S.W.; writing--review and editing, S.W.; visualization, C.-C.K.; supervision, S.W.; project administration, S.W.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Taipei University of Technology-King Mongkut's Institute of Technology Ladrakang Joint Research Program (NTUT-KMITL No. NTUT-KMITL-112-03) and National Science and Technology Council under NSTC 113-2218-E-A49-020-.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 The Algorithm
### Determining Critical Regions  
The right and the left beam lengths and their curvatures are obtained for each path point. Critical regions are formed from the path points whose right or left beam lengths are under a predefined magnitude and whose curvatures are above a predefined magnitude. For example, in Figure 7, there are three critical regions. The first critical region is the region numbered as 2 on the right starting from the point numbered as 1 and ending at the point numbered as 3. The second critical region is the one numbered as 8 on the left starting from the point numbered as 7 and ending at the point numbered as 9. The last critical region is the one numbered as 12 on the left starting from the point numbered as 11 and ending at the point numbered as 13.  
Figure 5: Global path generated in the middle of the workspace and basic definitions.  
Figure 6: Beam definitions.  
Note that the unused beams are not drawn in the figure for the sake of clarity. The critical region numbered as 2 is on the right side while the next critical region numbered as 8 on the left side. To put it another way, these critical regions are in the opposite directions. Therefore, a direction change is mandatory here. This is one of the two crucial steps developing the algorithm. See Figure 7, the direction change occurs in the middle of the distance numbered as 5 between the two regions numbered as 4 and 6. Since the distance between the points 3 and 7 is very short with respect to the link length, the direction change should be at the midpoint of the distance numbered as 5. However, there are more than one point that works around the point 5. Another matter here is that switching from one array to another makes programming more difficult. Hence, the right array values are copied to the left array values or vice versa, which results in all the values being in a single array.  
Recalling Figure 7, consider the second critical region numbered 8 and the third critical region numbered 12. Since they are both at the left side, no direction change is entailed at the point 10. The last direction change in the figure takes place at the point 14. One more thing: when the distance between critical regions is longer than the link length, the direction change occurs earlier, not in the middle of the distance. Again, it is possible to choose a direction change value out of possible several values.
## 3 LCI System Design Framework
### Lateral Resolution
#### 3.2.1 Operating outside the Region of Focus
Several methods of improving the lateral resolution of an OCT profilometry measurement have been shown in literature [50; 51; 52]. A promising technique is the use of synthetic-aperture radar (SAR) [53] which has been used for improving the lateral resolution of radar systems for decades; due to LCI operating characteristics being analogous to radar, the same principles can be applied whilst performing line scan measurements to drastically improve the measurement resolution. This may circumvent the issue of lateral resolution whilst operating with an un-lensed system or outside of a systems lateral field of view. Application of SAR to LCI has been demonstrated with some success in literature with a technique named 'Interferometric Synthetic Aperture Microscopy' (ISAM) [51].
# Finite Element Simulation of HELICOIL(r) Inserts
## 6 Simulation Model for the HELICOIL(r) Insert  
The simulation model consists of three parts: A screw, a sample, and an interface. The interface includes the screw threads, the HELICOIL insert, and the sample threads. The situation is depicted in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Configuration** & **H1** & **H2** & **H3** & **V1** & **V3** & \\
**Dimensions** & **a** & **b** & **a** & **b** & **a** & **b** & **a** & **b** & **a** & **b** \\ \hline
[mm] & 13.67 & 2.12 & 18.18 & 1.80 & 18.05 & 2.25 & 14.03 & 2.66 & 14.84 & 1.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of dimensions a and b for the experiments depicted in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \(k\) & **[-]** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** \\ \hline \(t_{k}\) & [s] & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 \\ \(u_{k}^{\text{EXP}}\) & [mm] & 0 & 0.023 & 0.042 & 0.059 & 0.075 & 0.095 & 0.116 & 0.140 & 0.164 & 0.189 & 0.214 & 0.244 & 0.278 & 0.313 \\ \(F_{k}^{\text{EXP}}\) & [N] & 0 & 297.6 & 586.3 & 849.4 & 1094.6 & 1313.9 & 1517.9 & 1727.0 & 1974.7 & 2218.9 & 2437.7 & 2666.7 & 2872.3 & 3045.9 \\ \hline \(k\) & [-] & **15** & **16** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **24** & **25** & **26** & & \\ \hline \(t_{k}\) & [s] & 14 & 15 & 16 & 17 & 18 & 19 & 20 & 21 & 21.49 & 22 & 23 & 24 & & \\ \(u_{k}^The dimension \(\mathsf{c}\) denotes the modelled part of the specimen. The behaviour of the interface is determined by the shapes, materials, and imperfections of the HELICOIL(r) insert and threads. We assumed non-linear behaviour of the specimen material, threads, its failure, and so on, to describe the interface. Therefore, the screw model and the specimen were simulated using a linear elastic material model; see Appendix C. For simulation of the interface, a cohesive contact [3] was used, with behaviour represented by a multi-linear curve. The description of the model is given in Section 6.4.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 1 Introduction
The error correction performance obtained with the CPP interleavers is better than that of the Fourth-Degree Permutation Polynomial (4-PP) and QPP interleavers. Meanwhile, studies of chaotic maps have been conducted. In [27], a chaotic interleaver is introduced to substitute the random interleaver generally used in conventional turbo codes. The results of computer simulations reveal that the scheme proposed delivers better BER performance compared with conventional block interleaving. In [28], a new chaotic interleaver is proposed for correcting burst errors in an effective way. The results of computer simulations are shown in terms of the optimal solution observed, together with the value of BER against \(E_{b}/N_{0}\). In the cases simulated, a good performance is obtained with the chaotic golden interleaver when transmitting data both in short block (L = 400 bits) and long block lengths (L = 1024). In [29], a chaotic interleaver has been used over a low-density parity-check code and applied to a power line communication (PLC), thereby proposing a new interleaver scheme based on the chaotic baker map. Images transmitted via computer simulations demonstrate that the proposed algorithms work well in the PLC channel. From these recent studies on interleavers for turbo codes, it is noticed that new improvements for turbo codes continue to be proposed. However, the analysis and comparison of turbo code performance are still necessary for diverse interleavers with short block lengths. This is the motivation of this work, in which such an analysis and comparison is conducted from the perspective of the interleaver's action. BER versus the SNR is assessed while maintaining the iterative coder and decoder features. Additionally, the performance results for the parallel turbo code for short block lengths are reviewed, particularly for lengths between 64 bits and 512 bits, and a new interleaver based on Duffing map is proposed. This document is structured as follows. Section 2 describes the selected turbo code and how its performance is quantified. Section 3 introduces interleavers used by turbo codes. Test results are presented in Section 4, and Section 5 deals with the conclusions of this work.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Year** & **Instrument\({}^{\bf 1}\)** & **Difference** & **Slope** & **R\({}^{2}\)** & **Range** & **Points** & **Comment** & **Ref.** \\ \hline \multicolumn{9}{c}{FTIR connected at the dilution tunnel} \\
1966 & Prototype & - & 1.01 & 0.99 & 0.3–5.5 ppm & 78 & G (methanol) & [24] \\
1990 & Mattoon Horiba & - & 1.03 & 0.99 & 0.8–ppm & 25 & G, fuels M85 & [112] \\
1992 & Nuclei REGA 7000 & 17\% & 1.14 & 0.99 & 0.4–5 mg/mi & 6 & G & [166] \\
1993 & Mattoon REA & 18\% (–60\%...98\%) & 1.25 & 0.91 & 0–4 ppm & 266 & 2.5 y (G, CNG) & [170] \\
1993 & Nuclear REGA & 34\% (-33\%...96\%) & 1.04 & 0.95 & 0–4 ppm & 229 & 2.5 y (G, CNG) & [170] \\
1994 & Nuclear REGA 7000 & 9\% & - & - & 10 ppm & 1 & G,fuel M85 & [171] \\
1994 & Sesam II & \(-13\%\) (–21\%...6\%) & 0.88 & 0.88 & 2–16 ppm & 6 & G & [180] \\
1990 & Nicolet Steam & - & 0.85 & 0.94 & 3–72 mg/mi & 15 & G,D2 & [166] \\
1991 & Bio-Rad Digits FTS-60 & - & 0.84 & 0.97 & 0–8 ppm & 20 & Mary G, fuels & [169] \\
1998 & VW Seam & - & 0.68 & 0.57 & 0–4 mg/mi & 65 & G40 & [113] \\
1998 & VW Seam & 15\% & - & - & 2 mg/mi & 2 & G & [113] \\
2006 & MEXA 4000 FT & - & 1.01 & 0.91 & 0–6 ppm & 26 & D & [176] \\
2013 & MEXA 6000 FT & \(-0.5\%\) (–2.2\%...24\%) & - & - & 2 mg/km (\(<\)60 ppm) & 6 & G42 (B0–10–20, M15–30) & [179] \\
2016 & AVL Seam & \(-5\%...27\%\) & - & 0–4 mg/km & 3 & Motorcycle (flex) & [205] \\
2017 & Gasnet CR2000 & - & 1.32 & 0.98 & 0–20 mg/km & 20 & Dbz, G2, CNG & [197] \\
2017 & MKS, Sesam, MEXA & 8\% (–4\%...32\%) & - & 0.82–0.94 & 0–8 mg/km & 8 & FFV & [168] \\ \hline \end{tabular}
\end{table} 
Table 8: Comparisons of FTIR with formaldehyde (CH\({}_{3}\)CHO) methodology (DNPH + HPLC).
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 2 Methods of Synthesizing Coordinated Polymer-Inorganic Hybrids
### General Synthetic Route for Supramolecular Coordination Complexes (SCCs)
#### 2.2.2 Synthesizing Metallacycle/Metallacage-Crosslinked Polymer Networks (MPNs)  
Similar to the synthesis of MSPs, two approaches, post-assembly polymerization and post-polymerization assembly, are employed for constructing MPNs. In the post-assembly polymerization method (as shown in Figure 2A), metallacycles or metallacages with multiple reaction sites are first created through coordination-driven self-assembly. These structures then undergo crosslinking at their reaction sites, forming the final polymer networks with metallacycles or metallacages acting as linkers. In the post-polymerization assembly method (Figure 2B), polymer chains with coordination sites are synthesized first, followed by coordination-driven self-assembly, to form MPNs [52]. The varied structures in MPNs present unique topologies and serve as valuable platforms for studying polymer networks and their physics [53]. Additionally, due to the well-defined pores or cavities in metallacycles and metallacages, MPNs are excellent candidates for applications such as gas separation, molecular sieving, and controlled release. These networks also provide a promising solution for improving the processability of widely used MOF materials, offering significant potential for practical applications [54].  
Figure 1: Synthetic approaches for the MSPs. (**A**) Post-assembly polymerization approach: metallacycles or metallacages are first formed via coordination-driven self-assembly, followed by polymerization of chains from the core. (**B**) Post-polymerization assembly approach: pre-synthesized macroligands with polymer chains and coordination sites undergo coordination-driven self-assembly to form MSPs. Red represents the metal ion, green represents the organic linker and blue represents the polymer. Adapted with permission from the Royal Society of Chemistry [52].  
Synthesizing Polymer-Inorganic Hybrids Through Vapor Phase Infiltration (VPI) and Liquid Phase Infiltration (LPI)  
Hydrophobic and hydrophilic polymers can undergo infiltration, where inorganic precursors can be selectively introduced into specific domains of the polymer melt. Based on our findings from the available literature, the most common method for post-modification of coordination-based polymer-inorganic hybrids involves vapor phase infiltration (VPI) and liquid phase infiltration (LPI). The following section will explore these techniques in detail, discussing their processes and how they facilitate the formation of coordinated hybrid materials.