esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 QoS Aware MAC Protocols for WMSNs
### Conclusion and Future Trends  
To design MAC protocols for WMSNs, one must take into account the existence of heterogeneous traffic and the nature of bursty and voluminous multimedia data traffic. We found that in the current literature there exists mainly three types of MAC protocols depending on the underlying medium access mechanisms. The CSMA/CA based protocols [40; 41] boost good scalability and adaptability to varying traffic conditions as it requires no synchronization. However, CSMA/CA suffers from lacking of QoS support and energy inefficiency due to its high collision rate and high control overhead especially when under heavy traffic. This can be alleviated by using traffic type prioritization and service differentiation techniques such as adaptive contention window and dynamic duty cycling. The hybrid protocols of CSMA/CA and TDMA [39; 43; 44] in essential provide better QoS support and energy efficiency while at the same time maintain a low control overhead. With dynamic slot allocation techniques, the hybrid protocols are also more adaptive to dynamic traffic conditions compared to traditional TDMA. Nevertheless, channel under utilization still exists when traffic is light and scalability of the hybrid protocols also needs to be further improved. On the other hand, the slotted CSMA/CA (IEEE 802.15.4) based protocols [42; 45] often employ a mutli-channel design, with a dedicated channel for control messages and a set of data channels for data communications. However, although the multi-channel design improves channel efficiency, it also brings on channel switching costs. And a complete GTS slot based protocol [42] lacks scalability and adaptability to the dynamic traffics, while an IEEE 802.15.4 based protocol suffers from high collision rate. Despite the respective pros and cons of each category of MAC protocols, it is to be noted that, however, there existsthe common drawback of these MAC protocols being not considering the nature of multimedia data characteristics.  
To accommodate the bursty, heterogeneous, and voluminous varying traffic patterns, and to guarantee QoS provision, we believe the best MAC protocol for WMSNs should be a hybrid of CSMA/CA and TDMA with traffic prioritization, dynamic slot allocation, and adaptive contention window mechanisms without imposing high energy cost. Furthermore, to reduce channel access delay and to better accommodate multimedia traffic, the message passing technique should also be considered.
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 3 Virtual Network Embedding Problem Formulation
### Network Model
#### 3.1.1 Substrate Network  
The notations and meanings of the virtual network embedding problem are shown in Table 1. The substrate network is modeled as a weighted, undirected graph \(G_{s}=(N_{s},L_{s},A_{n}^{s},A_{l}^{s})\) where \(N_{s}\) represents the sets of substrate nodes, \(L_{s}\) represents the sets of substrate links, and \(A_{n}^{s}\) represents the sets of substrate nodes attributes, which mainly refers to the computing resources (such as CPU resources) and storage resources (such as flow table resources) of substrate nodes. \(A_{l}^{s}\) represents the sets of substrate links attributes, which mainly refers to the bandwidth and delay of substrate links.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(G_{s}/G_{v}\) & substrate/virtual network \\ \(N_{s}/N_{v}\) & the set of substrate/virtual nodes \\ \(L_{s}/L_{v}\) & the set of substrate/virtual links \\ \(n_{l}^{s}\) & substrate node \\ \(n_{l}^{v}\) & virtual node \\ \(l_{sm}^{s}\) & substrate link from node \(m\) to node \(n\) \\ \(l_{pq}^{v}\) & virtual link from node \(p\) to node \(q\) \\ \(p_{mm}^{s}\) & substrate path from node \(m\) to node \(n\) \\ \(R_{c}(n_{l}^{s})\) & available computing resource of substrate node \(n_{l}^{s}\) \\ \(c(n_{l}^{s})\) & computing resource of substrate node \(n_{l}^{s}\) \\ \(c(n_{l}^{s})\) & computing demand of virtual node \(n_{l}^{v}\) \\ \(R_{s}(n_{l}^{s})\) & available storage resource of substrate node \(n_{l}^{s}\) \\ \(s(n_{l}^{s})\) & storage resource of substrate node \(n_{l}^{s}\) \\ \(s(n_{l}^{s})\) & storage demand of virtual node \(n_{j}^{v}\) \\ \(R_{b}(p_{mm}^{s})\) & available bandwidth resource of substrate path \(p_{mm}^{s}\) \\ \(R_{b}(l_{sm}^{s})\) & available bandwidth resource of substrate link \(l_{sm}^{s}\) \\ \(b(l_{pq}^{s})\) & bandwidth demand of virtual link \(l_{pq}^{v}\) \\ \(delay(p_{mm}^{s})\) & path propagation delay of substrate path \(p_{mm}^{s}\) \\ \(delay(l_{mm}^{s})\) & link propagation delay of substrate link \(l_{sm}^{s}\) \\ \(delay(l_{pq}^{v})\) & link propagation delay constraint of virtual link \(l_{pq}^{v}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations in VNE.  
In addition, \(P_{s}\) represents the set of all loop-free paths in the substrate network. A path contains one or more substrate links. \(P_{mn}^{s}\) represents a path from substrate node \(n_{m}^{s}\) to substrate node \(n_{n}^{s}\). \(P_{mn}^{s}\) represents the set of paths from substrate node \(n_{m}^{s}\) to substrate node \(n_{n}^{s}\).
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### Crack Representation  
To allow the evaluation of the enrichment functions as well as the definition of the nodal sets involved in the enriched approximation, some kind of geometrical representation of the crack is necessary. In early XFEM works, as well as some GFEM publications, crack surfaces were explicitly represented as a series of linear segments (2D) or triangles (3D) [44; 131; 132]. However, the combination of this kind of representation to the XFEM can render the implementation quite involved by requiring for instance the computation of intersections of the crack with elements of the FE mesh.
###### Abstract  
Unmanned Aerial Vehicles (UAVs) face significant challenges in global path planning within complex terrains, as traditional algorithms (e.g., A*, PSO, APF) struggle to balance computational efficiency, path optimality, and safety. This study proposes a Voronoi-A* fusion algorithm, combining Voronoi-vertex-based rapid trajectory generation with A* supplementary expansion for enhanced performance. First, an adaptive DEM layering strategy divides the terrain into horizontal planes based on obstacle density, reducing computational complexity while preserving 3D flexibility. The Voronoi vertices within each layer serve as a sparse waypoint network, with greedy heuristic prioritizing vertices that ensure safety margins, directional coherence, and goal proximity. For unresolved segments, A* performs localized searches to ensure complete connectivity. Finally, a line-segment interpolation search further optimizes the path to minimize both length and turning maneuvers. Simulations in mountainous environments demonstrate superior performance over traditional methods in terms of path planning success rates, path optimality, and computation. Our framework excels in real-time scenarios, such as disaster rescue and logistics, although it assumes static environments and trades slight path elongation for robustness. Future research should integrate dynamic obstacle avoidance and weather impact analysis to enhance adaptability in real-world conditions.  
**Keywords:** UAV path planning; Voronoi diagram; A* algorithm; DEM layering; complex terrain; fusion algorithm
# Optimization of Stop Plan for Skip-Stop Operation on Suburban Railway Line
## 5 Case Study
### Calculation Results and Analysis
#### 5.3.4 Sensitivity Analysis  
Calculations were carried out to analyze the effects of \(t_{z}\) on the optimization results for AS mode, E/L mode with overtaking condition and E/L mode without overtaking condition at a \(t_{min}\) of 90 s and a load factor of 100% while the other conditions were kept unchanged. Figure 10 shows the comparison results of different \(t_{z}\) values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operating Mode** & \(t_{wait}\) **(h)** & \(t_{onboard}\) **(h)** & \(t_{total}\) **(h)** & \(C_{total}\) \\ \hline AS & 861.42 & 7791.86 & 8653.28 & 20 \\ Optimal E/L (overtake) & 994.05 & 6158.83 & 7152.88 & 23 \\ Comparison results & +15.40\% & \(-\)20.96\% & \(-\)17.34\% & +15.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the results for the optimal E/L (overtake) and AS modes for Jiangjin Line.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operating Mode** & \(t_{wait}\) **(h)** & \(t_{onboard}\) **(h)** & \(t_{total}\) **(h)** & \(C_{total}\) \\ \hline Optimal E/L (no overtake) & 1593.02 & 6849.58 & 8442.60 & 15 \\ Optimal E/L (overtake) & 994.05 & 6158.83 & 7152.88 & 23 \\ Comparison results & \(-\)37.60\% & \(-\)10.08\% & \(-\)15.28\% & +53.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the results for E/L mode with the overtaking condition and E/L mode without the overtaking condition.  
The following conclusions can be drawn from Figure 10.  
(1) With the increase in stop time \(t_{z}\), the total travel time of passengers \(t_{total}\) increases. The increase in \(t_{z}\) has a greater impact on the \(t_{total}\) in AS mode than other modes. As \(t_{z}\) increases from 30 to 60 s, the \(t_{total}\) in AS mode increases by 1025.6 h (12.60%), from 8141.54 to 9167.14 h, while the \(t_{total}\) in E/L mode with the overtaking condition increases by 523.7 h (7.53%) from 6950.92 to 7474.62 h, the \(t_{total}\) in E/L mode without the overtaking condition increases by 977.12 h (12.27%) from 7960.92 to 8938.04 h.  
(2) Increasing \(t_{z}\) enhances the superiority of E/L mode over AS mode in terms of \(t_{total}\). At \(t_{z}\) = 30 s, the \(t_{total}\) in E/L mode with the overtaking condition is 14.62% less than that in AS mode, and the number grows to 18.46% as \(t_{z}\) increases to 60 s. Therefore, the formulation of a train operating schedule requires the determination of a reasonable \(t_{z}\) based on the actual operational conditions on the route in conjunction with the passenger flow.
## 3 Results and Discussion
### Diameter  
The value used for the diameter analysis was the average of the values obtained in the two measurements of each hole. Figure 11 shows the values obtained in the three repetitions of the tests for each combination of parameters. As in the roughness, there is little variation between test repetitions, with the largest deviation found in test 2, with a standard deviation of 8 \(\upmu\)m.  
Figure 10: Main effects plot for Ra.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \(\mathbf{V_{c}}\) & \multirow{2}{*}{**n (Rpm)**} & \(\mathbf{f_{z4}}\) & \(\mathbf{V_{fft}}\) & \(\mathbf{f_{za}}\) & \(\mathbf{V_{fha}}\) & \multirow{2}{*}{**Strategy**} \\  & **(m\(\cdot\)Min\({}^{-1}\))** & & **(mm\(\cdot\)Tooth\({}^{-1}\))** & **(mm\(\cdot\)Min\({}^{-1}\))** & **(mm\(\cdot\)Tooth\({}^{-1}\))** & **(mm\(\cdot\)Min\({}^{-1}\))** & \\ \hline
1 & 80 & 6366 & 0.0150  
It can be seen that depending on the parameters used we can find deviations above and below the programmed diameter. However, other researchers only find deviations in one of the senses. It was found by [18] that all deviations are above the desired diameter, but on the contrary [11; 14; 28] obtained lower values than those programmed. In all cases, good accuracy is attributed to the process, with values between IT6-IT11 according to ISO 286-1:2010.  
The influence of each parameter or combinations of them on the diameter of the hole is reflected in the Pareto chart of the standardized effects graph (Figure 12), where it is verified that the axial feed rate per tooth (f\({}_{\text{2a}}\)) is the parameter with the greatest influence, followed by a combination of two factors and to a lesser extent the tangential feed rate per tooth (f\({}_{\text{2a}}\)). Figure 13 shows the main effects of the parameters on the diameter, where it is observed that lower axial feed rate generates smaller hole diameters.  
The diameter obtained may be closely related to the forces during milling, since an increase in these forces in the radial direction may cause them to deflect. At the same time, temperature can also play an important role, which can be related to the thickness chip.  
Figure 11: Graph of diameter results.  
Figure 12: Pareto chart of the standardized effects for diameter.  
Table 11 shows that both the axial feed rate per tooth (f\({}_{\rm za}\)), two parameter combinations and to a lesser extent tangential feed rate per tooth (f\({}_{\rm zt}\)) present \(p\)-values below 0.05, with the axial advance being the one that presents greater evidence next to a greater \(f\)-value, indicating its significance.
# A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique
## 5 Results Analysis
### Feature Extraction and CAI Development  
To build our desired CAI for the RC beam, we used five different features (peak amplitude, rise time, decay time, AE counts, and AE energy). There was a total of 872 points for each feature, representing a time history of 872 s during the bending test. Each feature is expected to have a very significant impact on the CAI. Therefore, features that are not correlated with cracks can make the CAI unreliable. Figure 10 shows an example of a reliable and an unreliable feature. Figure 10a shows the trend of the decay time over the entire failure history of the concrete specimen. The value of the decay time changes over time, and it drastically falls at 855 s, representing the failure of the beam. This trend can be used to represent the failure history of the beam. On the other hand, the time history plot of the feature and peak amplitude do not provide us with much insight about the initial crack occurrence and crack expansion and occurrences. Even though the 300-600 s represent the formation of the micro-cracks, the trend in Figure 10b does not provide us with significant fluctuations in that early time period. The peak amplitude feature is therefore not beneficial for our CAI. To solve this issue, we used the Boruta algorithm described in Section 3.3. Figure 11 shows the resulting ranking of the features considered in our study. The three features having the most importance are selected to be fused using the MD combining together all the fault information from each.  
Figure 10: Time history of (**a**) the decay time (a reliable feature) and (**b**) peak amplitude (an unreliable feature).  
A plot of the BMS (selected features and MD) for the concrete's minor crack formation to fail is presented in Figure 12a. It puts together the degradation information from each of the AE features selected using the Boruta algorithm and comprehensively reflects the crack formation in the beam. Figure 12a shows a clearer crack formation trend compared to any individual feature, since it has less noise. However, the BMS plot still contains noise due to minor oscillations before it starts to fully grow. This can create unwanted maintenance alarms. To achieve our final CAI, the BMS plot is further processed using NRS (the CUMSUM algorithm). Figure 12b contains no noise, and the time history of the crack formation is clearly visible. For the first 300 s, there was no crack, and thus, it denoted the normal condition of the RC beam. Cracks started forming at around 301 s, and we could see the macro-cracks/fractures occurring from 600 s onward. The RC beam went into complete failure at around 855 s.  
Figure 11: Ranking the features using the Boruta algorithm.  
Figure 12: Time history of the RC beam: (**a**) Boruta-Mahalanobis system (BMS) plot and (**b**) final crack assessment indicator (CAI).
## 2 Related Works  
### Structure-Based Approach  
Existing structure-based knowledge graph completion methods can be classified into two main categories: (1) distance-based methods and (2) tensor-decomposition-based methods.  
In distance-based methods, TransE [1] is one of the earliest proposed models. It projects entities and relations into a vector space and treats relations as translation operations from head entities to tail entities. However, TransE's scoring function is too simplistic to effectively represent the semantic differences between different entities under the same relation. To address this issue, TransH [6] introduces the concept of relation hyperplanes, which allow for different representations of the same entity under different relations. TransR [7] defines different mapping matrices for different relation categories, but this leads to a large number of model parameters. To tackle the parameter explosion problem, TransD [8] decomposes the relation mapping matrix. Addressing the inability of TransE to handle symmetric relations, RotatE [2] represents relations as rotational operations in the complex space, enabling better learning and differentiation of symmetric relations. PairRE [9] projects relations into the embeddings of head and tail entities and further models sub-relations based on RotatE while simplifying the complexity of extending the complex space.  
Tensor-decomposition-based methods were initially proposed by RESCAL [10]. They optimize the product of head entity vector \(v_{h}\), relation matrix \(M_{r}\), and tail entity vector \(v_{t}\) to satisfy the equation \(v_{h}^{T}M_{r}v_{t}=T_{irt}\), and the loss function is optimized to maximize the scoring function \(v_{h}^{T}M_{r}v_{t}\). The drawback of the RESCAL algorithm is that it sets a separate matrix for each relation, resulting in a large number of parameters. To address this parameter issue, DistMult [11] constrains the relation matrix to be a diagonal matrix, reducing the model parameters and significantly improving computational efficiency. To handle asymmetric relations, ComplEx [12] introduces complex operations in matrix multiplication. HolE [13] computes circular correlation values between head and tail entities, where circular correlation operations provide advantages in computational efficiency and space utilization. SimplE [14] addresses the inability of DistMult to model asymmetric relations by introducing separate distributed representations for entities and relations, resulting in fewer parameters compared to RESCAL. Inspired by automated machine learning, [PERSON] et al. propose an algorithm called AutoBLM [15] for automatically designing and discovering better scoring functions in knowledge graph embeddings.  
As shown in Table 1, both methods only learn some structural information in the knowledge graph through triples. For long-tail entities and relations, embedding representations learned through structure-based methods often produce poor completion results.
## 7 Overall Evaluation  
dNAS demonstrated that any state transition on a wine record along the supply chain could only be validated and completed automatically if, (1) the involved registered nodes and its distributed nodes, such as blockchain nodes and the IPFS nodes, are cryptographically authenticated, (2) the related wine record and the transactions for specific process are cryptographically verified, and (3) the consensus of such state transition is reached with the related transactions packed in a mined block, by blockchain nodes owned by other consortium members. The immutable states of transactions in different blocks are also available on the network and can be accessed via different tools developed in dNAS, such as the blockchain explorer. The wine record verification and product provenance abilities of dNAS were confirmed to be strengthened and benefited with decentralization, under which no more single-point failure and control were found in the proposed dNAS. The resiliency and availability of the validated wine records, with the history of transitioned states, are also enhanced with the persistent volume on the states of blockchain and the related transactions, alongside the wine record data stored in dNAS.  
Though dNAS was found to be less scalable than its centralized counterparts, as we expected, the advantages brought by decentralization have enabled different nodes along the supply chain of wine industry to work collaboratively with wine records flowing downstream, with state transitions validated automatically in a decentralized and immutable fashion. dNAS proved to function not only as a verification service, but also as a notary service, providing a proof-of-existence for wine records owned by different supply-chain participants. Further qualitative analysis on dNAS as a whole was also performed, with limitations and risks identified to further improve different aspects, such as the system security, partial decentralization and scalability of dNAS.  
In accordance with the research objective, it is confirmed that it is feasible to decentralize an anti-counterfeiting application of the supply chain industry to better improve the situation of counterfeiting attacks in the wine industry, and even the supply chain industry as a whole. With the flexibility of the architecture of decentralized system components, the decentralized solutions and operations proposed in dNAS are indeed industry-agnostic, and should be available for any industry with supply chain systems looking to combat product counterfeit.
# A Mathematical Model of Industrial Waste-Derived Fuel Droplet Combustion in High-Temperature Air
## 2 Materials and Methods
### Background of the Model Condition
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Component** & **\(\mathbf{W^{a}}\) (\%)** & **\(\mathbf{A^{d}}\) (\%)** & **\(\mathbf{V^{daf}}\) (\%)** & **\(\mathbf{Q^{a}}\)s,v (MJ/kg)** & **Specific Heat Capacity** & **Thermal Conductivity** \\  & & & & & **(k/l/(kg\cdot K))** & **(W/(m\(\cdot K\)))** \\ \hline Filter cake C (dry) & - & 26.50 & 23.10 & 24.83 & 1.30 & 0.366 \\ Wood & 20.00 & 2.00 & 83.10 & 16.45 & 1.55 & 0.200 \\ Rubber & 2.00 & 1.80 & 67.40 & 33.50 & 1.38 & 0.150 \\ Plastic & 2.00 & 0.20 & 99.50 & 22.00 & 2.30 & 0.260 \\ Cardboard & 5.00 & 3.00 & 96.57 & 17.50 & 2.30 & 0.130 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics and properties of composite liquid fuel components [28]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Component** & **\(\mathbf{C^{daf}}\) (\%)** & **\(\mathbf{H^{daf}}\) (\%)** & **\(\mathbf{N^{daf}}\) (\%)** & **\(\mathbf{S^{daf}}\) (\%)** & **\(\mathbf{O^{daf}}\) (\%)** \\ \hline Filter cake C (dry) & 87.20 & 5.10 & 2.10 & 1.10 & 4.50 \\ Wood & 50.30 & 6.00 & 0.20 & 0.10 & 43.40 \\ Rubber & 97.90 & 1.20 & 0.30 & 0.60 & - \\ Plastic & 66.70 & 7.90 & - & - & 25.40 \\ Cardboard & 46.30 & 6.30 & 0.30 & 0.20 & 46.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ultimate analysis of composite liquid fuel components [28]. In the research, the numerical simulation was carried out for single droplets (with the initial diameter \(d_{p}=1\) mm) of five different CLF compositions based on wet FC with MSW: No. 1--FC 100%; No. 2--FC 90% + wood 10%; No. 3--FC 90% + rubber 10%; No. 4--FC 90% + plastic 10%; No. 5--FC 90% + cardboard 10%. Adding 10% of MSW corresponds to the experimental findings [11; 23; 28; 37] that established that 5-20% of typical combustible MSW added to the CLF composition does not affect the its mechanisms or energy characteristics. At the same time, based on the estimates [38], adding 10-20% typical MSW to the CLF composition will reduce the area of land excluded from agricultural use due to landfill site arrangements by 20-30%. It will also save depleting fossil fuels burned for heat and power generation.