esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed PBFT Architecture and Model
### Consensus Process of Proposed PBFT
#### 3.3.2 Consensus Stage
\[T_{Commit}=(\frac{(N_{g\_node}-1)\times S_{block}}{BW}+L_{intra})+T_{setBlock} \tag{5}\]  
(6) Pre-Reply Phase: In this phase, the consensus results of groups within the cluster are aggregated. All Group Primary Nodes (GPNs) that have received more than \(2f+1\) Commit messages forward the consensus results of each group to their respective Cluster Primary Nodes (CPNs). The CPN considers the block valid and temporarily records it on the blockchain or ledger when it receives approval from over 50% of the total groups within the cluster. If we assume that all GPNs of each group sequentially transmit the inter-group consensus results, then the estimated time required for the CPN to complete the collection of intra-cluster consensus results is given in Equation (6). \[T_{\text{Pre-Reply}}=\frac{(N_{\textit{node}}/N_{\textit{g,node}})/2\times S_{ \textit{Reply}}}{BW}+L_{\textit{intra}} \tag{6}\]  
(7) Reply Phase: The Reply phase is the final step, where the consensus results of each cluster are finally aggregated. Each cluster's consensus result is transmitted to the client via the CPN, indicating whether the consensus message has been recorded on the ledger for each cluster. The client updates the blockchain or ledger only if a consensus is reached, with \(f_{\textit{total}}\) or more of the participating network nodes in agreement. \(f_{\textit{total}}\) represents the minimum number of nodes required for the consensus. If the consensus is not achieved, the transaction details from the message may be deferred for inclusion in subsequent blocks during the consensus process for eventual recording on the ledger. The estimated time required for the Reply phase is provided in Equation (7). \[T_{\textit{Reply}}=(\frac{S_{\textit{Reply}}}{BW}+L_{\textit{inter}})\times N_ {\textit{cluster}} \tag{7}\]  
We have provided a detailed explanation of the seven phases that constitute the consensus process of the proposed PBFT method. In addition, we have described models that can calculate the expected execution time for each phase. In the consensus process of the proposed PBFT method, the total expected consensus time, denoted as Equation (8), can be calculated. As shown in Equation (8), the total expected consensus time is the summation of all Equations (1)-(7). The time required for consensus varies depending on the total number of layers in the blockchain network, the number of nodes in each group, and the maximum number of child nodes a GPN can have. Therefore, in the proposed PBFT method, Equation (8) can be utilized to roughly estimate the time required for consensus, taking into account various blockchain network configurations. This calculated expected consensus time can be used to dynamically change the grouping and hierarchical structure when the number of nodes participating in the cluster changes or when there is a change in the hierarchical structure. \[T_{\textit{total}}~{}=~{}T_{\text{Pre-Request}}+T_{\textit{Request}}+T_{ \text{Pre-Prepure}}+T_{\textit{Prepare}}+T_{\textit{Commit}}+T_{\text{Pre-Reply}}+T _{\textit{Reply}} \tag{8}\]
## Appendix A
### Model Description and Parameter Values  
FORMIND is an individual-based, spatially explicit and process-based model designed to simulate the dynamics of species-rich forests [25]. The model simulates the processes of establishment, growth, competition and mortality of trees on a regular grid of patches with the dimensions of a typical treefall gap (20 m \(\times\) 20 m). Within each patch, the trees do not have explicit spatial positions following the gap-model concept ([PERSON], 1984). By combining many patches, large forest areas up to hundreds of hectares can be simulated. Tree species with similar ecological traits are aggregated into plant functional types (PFT) to facilitate parameterization for diverse forests and reduce computation time. The PFTs may represent different successional types (from pioneers to climax species) and size classes (from understory to emergent species).  
In each simulated time step (1 year), the following main processes take place: (1) Establishment: Seeds are distributed over the forest area. If light conditions are suitable, new saplings can establish and compete for light and space in the patch. (2) Competition: The main driving factor of the model is light. Radiation intensity within each patch decreases from the top to the ground according to a light extinction function. The light extinction depends on the combined vertical leaf area profile of all trees in the patch. The productivity of each tree is determined by the available light in its height layer. (3) The growth of each tree depends on its gross primary productivity (GPP), respiration and PFT-specific physiological and allometric parameters. (4) Mortality: Trees die stochastically according to a PFT-specific mortality rate. If a tree falls, it can damage neighboring trees in adjacent patches. Besides these core processes, FORMIND offers the following feature: Carbon Cycle: Gross primary production, respiration and net primary production are calculated for each individual tree. Based on this, the carbon balance for a whole forest can be derived, including soil respiration and net ecosystem productivity.  
The detailed model description was published with [PERSON] et al. [25]. Parameters for the study site Mt. Kilimanjaro (plot FLM 3), Tanzania are listed in Tables A1-A3 below. All model parameters are explained in [PERSON] et al. [25]. The description of all parameters can be found in the handbook; only the parameter values for the current study are specified here. All parameter values for the original parameterization (M\({}_{6}\)) are already published in [PERSON] et al. [30]. All parameter values for the parameterization versions with a reduced number of PFTs (M\({}_{3}\) and M\({}_{1}\)) are the result of averaging the parameters of M\({}_{6}\).  
Figure 13: Aboveground biomass per tree (total biomass divided by stem numbers) over time for all parameterization versions. Mean biomass per tree is lower in M\({}_{6}\) (with six PFTs) compared to M\({}_{1}\) (with one PFT). In particular, in late-successful phase, trees have 15%-20% less biomass in M\({}_{6}\) compared to M\({}_{1}\).
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 4 Experimental Results
### Reflection Coefficient Comparison  
The simulated and measured reflection coefficient of the non-transparent and transparent antennas are shown in Figure 11. The impedance matching of the manufactured transparent antenna was not well matched; this may be justified among other things by the process inaccuracies in the manufacturing of the mesh antenna, the electrical losses of the glass substrate, and the increase in the resistance value of the sheet through the use of the mesh technique. Additionally, the metal thickness \(t=370\,\mathrm{nm}\) was so thin that it was comparable to the skin depth value of \(0.9\,\mathrm{\SIUnitSymbolMicro m}\) (calculated using the skin depth equation [24]); high loss was present, which reduced the performance of the antenna.  
To minimize the losses due to the skin depth, a metal thickness greater than \(3\times 0.9=2.7\,\mathrm{\SIUnitSymbolMicro m}\) should be used. However, the metal deposition technique used does not make it possible to obtain this thickness. However, the proposed antennas still exhibited acceptable impedance matching over the WLAN frequency band.  
Figure 11: Measured and simulated return loss of the transparent and non-transparent antennas.  
Figure 10: Manufactured antennas: (**a**) non-transparent and transparent antennas; (**b**) antenna measurement chamber setup.
# A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
## 6 Gasification of Plastic Waste: An Overview
### Polyolefin
#### 6.1.2 Polyethylene
The conical spouted bed reactor is used by [PERSON] and [PERSON] and consist of a reactor with conical section. Despite the endothermic nature of the steam gasification process, in this reactor bed, isothermicity is ensured by the vigorous solid circulation of the sand, which also promotes high heat transfer rates. Commonly, it is used to reduce tar production. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reactor** & **Feedstock** & **Bed Material** & \begin{tabular}{c} **Gasification** \\ **Temperature** \\ \end{tabular}  & **Tar Content** & \begin{tabular}{c} **Gasifying** \\ **Agent** \\ \end{tabular}  & **Heating Value** & **Reference** & **Year** \\ \hline bubbling fluidized bed & Recycled PE & \ \begin{tabular}{c} quartz sand or \\ olivine \\ \end{tabular}  & 783–898 \({}^{\circ}\)C & 14.6–6.2 kg/h & Air, steam & \begin{tabular}{c} LHV \\ 5500-9200 \\ kJ/Nm\({}^{3}\) \\ \end{tabular}  & [PERSON] et al. [84] & 2008 \\ \hline \multirow{4}{*}{Conical spouted bed} & \multirow{4}{*}{Virgin HDPE} & \multirow{4}{*}{Sand or olivine} & \multirow{4}{*}{800–900 \({}^{\circ}\)C} & \multirow{4}{*}{16.7g/Nm\({}^{3}\)} & \multirow{4}{*}{stream} & \multirow{4}{*}{1.55 MJ/kg at 900 \({}^{\circ}\)C} & \multirow{4}{*}{[PERSON] et al. [67]} & \multirow{4}{*}{2013} \\  & & or \(\gamma\) -Al\({}_{2}\)O\({}_{3}\) & & & & & \\ \cline{1-1} \cline{5-10}  & & & & & & & \\ \cline{1-1} \cline{5-10}  &
## 4 Static Network Performance of a TFBN
### Hop Distance Parameters  
The hop distance is the distance between two directly linked nodes, and it is an essential metric to consider while evaluating the production of any interconnection network architecture in its graph-theoretic model. All distinct sets of nodes in a network contains the highest and the average hop distance using the shortest-path algorithm are termed as diameter and average distance, respectively. These two static network performance parameters are crucial for any interconnection network because they help to understand the possible effect on traffic congestion (message traffic density) as well as predicting the dynamic performance (nature of throughput vs. latency). The diameter is the latency's upper bound, i.e., the latency at saturation throughput. The average distance, on the other hand, is the latency at no load and no network throughput. A lower value of the hop distance is preferred, as it enhances the dynamic performance communication of the network. Different interconnection networks tend to perform differently on distinct traffic patterns and their respective traffic density. Especially, it is crucial to estimate the zero traffic load latency and possible increase in latency for high traffic load.  
The hop distance parameters were simulated using a dimension order routing algorithm. Both diameter and average distance have been enlisted in Table 1, which shows that the hop distance parameters of the proposed TFBN [23] outperforms the 2D and 3D mesh and torus networks and hierarchical TTN and TESH network for 256 nodes as well as for 4096 nodes. Furthermore, as the number of nodes increases, the diameter and average distance of hierarchical networks improve significantly than conventional mesh and torus networks. Among them, TFBN yields substantially low hop distance parameters compared to TTN and TESH networks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **Node Degree** & **Wiring Connectivity** & **Diameter** & **Average Distance** & **Static Cost** & **Fault Diameter** \\ \hline \multicolumn{8}{c}{256 Node} \\ \hline
2D-Mesh & 4 & 480 & 30 & 10.67 & 120 & 32 \\
2D-Torus & 4 & 512 & 16 & 8 & 64 & 18 \\ TESH & 4 & 416 & 21 & 10.47 & 84 & 24 \\ TTN & 6 & 544 & 15 & 7.44 & 90 & 18 \\ TFBN & 8 & 800 & 10 & 5.75 & 80 & 14 \\ \hline \multicolumn{8}{c}{4096 Node} \\ \hline
2D-Mesh & 4 & 8064 & 126 & 42.67 & 504 & 128 \\
2D-Torus & 4 & 8192 & 64 & 32.00 & 256 & 66 \\
3D-Mesh & 6 & 11,520 & 45 & 16.00 & 270 & 47 \\
3D-Torus & 6 & 12,288 & 24 & 12.00 & 144 & 26 \\ TESH & 4 & 6680 & 32 & 17.80 & 128 & 35 \\ TTN & 6 & 8736 & 24 & 12.60 & 144 & 27 \\ TFBN & 8 & 12,832 & 19 & 10.61 & 152 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of the static network performance of various networks.
## 3 Synthetic Coumarin Derivatives in Plant Protection
_Ralstonia solanacearum_ is one of the most devastating plant bacterial pathogens that affect tobacco production [37]. 3-Acetyl coumarin and benzo-4-methyl coumarin have shown bactericidal activity against bacteria _Erwinia amylovora_ and _R. solanacearum_[124]. Novel chalcone derivatives containing a coumarin moiety exhibited excellent antibacterial activity against _R. solanacearum_[125]. [PERSON] et al. [126] designed and synthesized an isopropanolamine-decorated coumarin derivative that exhibited better activity against _Xanthomonas oryzae_ pv. _oryzae_ (_Xoo_) than standard bactericide, bismerthiazol. Plant parasitic nematodes (PPN) cause yield losses by disrupting water and nutrient transport and acting as vectors for viruses [127]. Due to environmental side effects and health concerns, many synthetic nematicides have been banned (Directive 91/414/EEC) [128], against the phytopathogenic nematode, _Bursaphelenchus xylophilus_, and industry hardly supports the development of novel nematicides. Nematicides that have been used before the establishment of the Environmental Protection Agency (EPA) were soil sterilants, organophosphates or carbamates (neural toxins), and animal health drugs (abancetin), therefore many of them have been banned [129]. Currently, there are only a few nematicides left in use, and repeated applications of the same formulation are inevitable, leading to resistance in nematodes. Several new coumarin derivatives reported activities against plant-parasitic nematodes. Alkoxycumarins, and especially 5-ethoxycoumarin, exhibit high nematicidal activity phytopathogenic nematode _B. xylophilus_[130]. It has been shown that tin(IV) complexes are more effective than their parent ligands, 3-formyl-4-chlorocoumarin semicarbazones (L1H) and 3-formyl-4-chlorocoumarin thiosemicarbazones (L2H) [131]. Also, newly synthesized complexes of lanthanide(III) with 3-formyl-4-chlorocoumarin hydrazinecarbothioamide (L1H) and 3-formyl-4-chlorocoumarin hydrazinecarboxazole (L2H) have been found to be more active against _M. incognita_ than the parent ligands themselves [132]. A series of coumarin derivatives were synthesized with targeted derivatization of the C-4 and C-7 hydroxyl groups. They were assayed for nematicidal activity against plant-parasitic nematodes. The modification of the hydroxyl at C4 and C7 positions led to the identification of promising lead compounds against _M. incognita_, _Ditylenchus destructor_, _Bursaphelenchus mucoratus_,  
Figure 7: Coumarinyl Schiff bases that inhibited the radial growth of the fungal colonies of _Macrophumina phaseolina_[36]. and _B. xylophilus_. The most effective analog was one whose structure combines a coumarin moiety, bromine atoms, and a butyl chain [41]. In Table 2, we summarized the biological activities of synthetic coumarins related to plant protection.
## 3 Classifier Training and Results
### Head Gesture Recognition Rates for T1 and T2 Testing Scenarios
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Classifier** & **Recognition of** & **Recognition of Raw** \\
**Parameterized Signals** & **IMU Signals** & **IMU Signals** \\ \hline
1. & Decision tree & \(\sim\)1 ms & \(\sim\)1 ms \\
2. & Decision tree with a minimum of 5 samples & \(\sim\)1 ms & \(\sim\)1 ms \\
3. & Random forest & \(\sim\)10 ms & \(\sim\)6 ms \\
4. & Random forest with a minimum of 5 samples in the leaf & \(\sim\)6 ms & \(\sim\)7 ms \\
5. & \(k\)-NN for k e (1, 3, 5, 7, 9, 11, 13, 15, 17, 19] & \(\sim\)5 ms & \(\sim\)2 ms \\
6. & SVM with an RBF kernel for & \(\sim\)10 ms & \(\sim\)5–11 ms \\
7. & SVM with a 3 rd degree polynomial kernel & \(\sim\)10 ms & \(\sim\)4–10 ms \\ \hline \end{tabular}
\end{table} 
Table 8: Recognition times of a gesture for a single input data pattern. \begin{table}
\begin{tabular}{c c c c} \hline
**Pitch** & **Roll** & **Yaw** & **Immobility** \\ \hline Sensitivity & 95\% & 96\% & 94\% & 99\% \\ Specificity & 99\% & 99\% & 99\% & 97\% \\ F1-score & 97\% & 97\% & 95\% & 96\% \\ \hline \end{tabular}
\end{table} 
Table 6: Statistical measures showing detection results of head gestures for the SVM classifier with a 3 rd degree polynomial and \(C=1.0\) (the classifier trained on raw IMU signals). The computation times required for processing the data by the trained classifiers (for a PC with Intel Core i5-7500 processor) varies from 1 ms for the decision tree classifier to 11 ms for the SVM classifier with an RBF kernel. It should be noted, that for the parametrized approach, we must first compute the statistical parameters; for the shortest time window consisting of 10 signal samples, we need to wait for 0.1 s before we feed these parameters to the classifiers.
## 4 Experiment Evaluation
### Experimental Analysis
#### 4.3.2 Drift Detection Performance Analysis
While a conservative strategy is important, excessively conservative strategies can lead to less-than-ideal detection efficiency. Conversely, MWDDM has high detection efficiency but suboptimal false positive rate performance. Although its detection delay ranks among the top, it comes at the cost of a higher false positive rate. In summary, VFDDMs demonstrate excellent performance in balancing detection delay and false positive rate. As shown in Figure 10, VFDDMs maintain a low false positive rate while ensuring high-ranking detection delay, placing them at the forefront among all compared algorithms. Experimental results confirm that our proposed VFDDMs possess good adaptability and can provide suitable drift detection strategies for different types of data streams, meeting our expectations. Figure 11 illustrates the comprehensive performance of the drift detectors tested in the experiment across four key metrics: classification accuracy, CPU time consumption, detection delay, and false positive rate, under different base classifier scenarios. The algorithms extending further toward the edges of the graph exhibit superior overall performance. From Figure 11, it can be observed that VFDDMs consistently rank within the top three in both NB and HT scenarios, demonstrating the excellent overall performance of our VFDDM.
# Coordinated Mitigation Control for Wideband Harmonic of the Photovoltaic Grid-Connected Inverter
## 3 Resonance Suppression Method of LCL Filter  
This section uses the capacitive current to construct a feedforward control branch. This measure redefines the LCL-type filter equivalent function. The feedforward loop increases active damping at the high-order resonant frequency. It reduces the amplitude gain of the specific resonance point without missing the control system's dynamic characteristics and stability.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 7 Routing for Multi-hop WSNs with a Single Immobile Sink
### Important Results  
The number of iterations required for convergence of the partially distributed algorithm in [2] is around 600 iterations while the number for the fully distributed algorithms is around 3500 iterations as shown in Figure 12. Therefore, quicker convergence is achieved in partial distribution at the expense of higher communication overhead due to larger number of variable exchanges every iteration of the algorithm as compared to the fully distributed algorithm. On the other hand the fully distributed algorithm has variable exchanges between each node and its neighbors only and hence has lower communication overhead.
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## 4 Proposed User Pairing Solutions
### Gale-Shapley Algorithm  
The user pairing problem is formulated as follows:  
\[Maximize\sum_{u_{c}=1}^{U/2}\sum_{u_{c}=U/2+1}^{U}F_{u_{c},u_{c}}\text{EE}. \tag{14}\]  
where  
\[F_{u_{c},u_{c}}=\begin{cases}1&;\text{if the $u_{c}$ is already paired with $u_{c}$}\\ 0&;\text{if the $u_{c}$ is not paired with $u_{c}$}\end{cases}\]  
The Gale-Shapley algorithm considers the stable marriage criteria to obtain pairs. Every user in the first group forms its own preference list of the other group members [37]. The preferences of each member of the first group are arranged in descending order to guarantee that they propose to the most preferred users of the second group. Members of the second group have the right to accept the proposal if they are free or decline if they prefer their current partners. Members of the second group have the right to choose between their current partners and the new proposals. For users of the first group who did not get partners in the first round of proposals, they propose again according to the sequence of their preference list. The values of the preference list are computed according to (14). The output of the pairing process is stable if there is no user from the first group and a user from the second group who are not paired to each other, but both prefer each other over their current partners. For the Gale-Shapley algorithm, the output is stable for the group that offers the proposals. The Gale-Shapley algorithm is presented in Algorithm 1. An example of the preference list for six users is introduced in Table 1.  
**Step 1**:  
Each cell-center user forms a list of its preferences in cell-edge users in descending order according to EE calculations.  
**Step 2**:  
(a) Each unpaired cell-center user gives a pairing request to the cell-edge user he prefers most.  
(b) Each cell-edge user replies \"yes\" to the cell-center user he most prefers and \"no\" to all other cell-center users. The cell-edge user is then provisionally \"paired\" to the cell-center user he most prefers so far, and that cell-center user is temporarily \"paired\" to that cell-edge user.  
**Step 3**:  
(a) Each unpaired cell-center user gives a pairing request to the most-preferred cell-edge user to whom he has not yet given a pairing request (regardless of whether that cell-edge user is already paired)  
(b) Each cell-edge user replies \"yes\" if he is currently not paired or if he prefers this cell-center user over his current temporary partner (in this case, the cell-edge user rejects his current temporary partner who becomes unpaired).  
**Step 4**:  
This process is repeated until everyone is paired.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**First Group Preferences**} \\ \hline \(u_{c1}\) & \(u_{c1}\) & \(u_{c3}\) & \(u_{c2}\) \\ \hline \(u_{c2}\) & \(u_{c3}\) & \(u_{c1}\) & \(u_{c2}\) \\ \hline \(u_{c3}\) & \(u_{c1}\) & \(u_{c3}\) & \(u_{c2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The preference list of the cell-center users.