esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Multi-Objective Optimization Framework
### Analysis of Optimization Result  
The optimization results demonstrate notable improvements in electromagnetic performance, driven by strategic adjustments to the key design variables (\(b_{r}\), \(b_{m}\) and \(l_{m}\)). These variables collectively influence the air gap flux density distribution and thus efficiency and torque characteristics, as detailed below.  
The flux barrier thickness \(b_{r}\) increased from 3.00 mm to 4.99 mm. A thicker \(b_{r}\) enhances magnetic isolation between rotor poles, reducing leakage flux and concentrating more magnetic flux in the air gap. This is reflected in the 1.98% increase in the average air gap flux density magnitude (Figure 14a), which directly contributes to the 4.62% improvement in \(T_{av}\).  
Increasing \(b_{m}\) from 4.00 mm to 4.96 mm strengthens the magnetomotive force (MMF) of the permanent magnets. This elevates the fundamental component of the air gap flux density, which can be seen in Figure 14b, further boosting \(T_{av}\). While thicker PMs could elevate eddy current losses (due to larger conductive volume), the \(\eta\) gain may primarily stem from reduced iron losses.  
The PM width \(l_{m}\) expands from 50.00 mm to 52.90 mm, effectively increasing the effective pole-arc coefficient. This adjustment may improve the spatial distribution of the air gap flux density, reducing harmonic distortion. As shown in Figure 14b, a notable suppression of the 5 th, 7 th and 17 th harmonics can be seen. The total harmonic distortion (THD) of the air gap flux density decreases from 37.67% to 35.37%. This change is likely to reduce iron losses to counter the potential increase in permanent magnet eddy current losses due to increased \(b_{r}\) to maintain \(\eta\) at a high level. Smoother flux distribution probably contributes to the 10.48% reduction in \(\Delta T\). The expanded \(l_{m}\) also enhances flux linkage, synergizing with \(b_{r}\) and \(b_{m}\) to elevate \(T_{av}\).  
The above analysis and the validation of finite element simulation (Table 8) prove that the results obtained from the optimization framework proposed in this paper are robust, reasonable and effective.
## 3 System and Problem Definition for Scheduling Problem
### Proposed Approach Based on Reinforcement Learning
#### 3.4.3 Proposed Algorithm for Confidence-Based Approach
Experimentation for different levels of confidence using the \(\delta\) parameter can give insight into how the variation in energy consumption estimates affects the learning algorithm. \(\mathit{Energy}_{total}\) for the full system can be still be recorded accurately for comparison. ```
Input: Incoming Jobs, HPC Data Center \(HDC\). Output: Resource Allocation for Incoming Jobs. 1 for\(i\) in possible single allocationsdo
2
3 end for
4\(\hat{t}_{i}\Leftarrow 0\);
5\(\hat{e}_{i}\Leftarrow 0\);
6\(C_{i}\Leftarrow 0\);
7 while\(1\)do
8 ifany running_job(s) have finished or job(s) arrivethen
9 Update data center resources;
10 Update \(\hat{t}_{i}\), \(\hat{e}_{i}\) and \(C_{i}\) for all jobs finished;
11 Update jobQueue;
12 whileAny possible allocationsdo
13\(selected\ Allocation\Leftarrow null\);
14\(maxI\Leftarrow 0\);
15 for\(j\) in jobQueuedo
16 for\(c\) = \(maxAvailableCores\) to \(1\)do
17\(i\Leftarrow allocation(j,c)\);
18 if\(C_{i}=0\)then
19\(selected\ Allocation\Leftarrow i\);
20 go to \(31\);
21
22 end if
23\(curDelay\Leftarrow curTime-jarrival\)Time;
24\(\vartheta_{i}\Leftarrow j.getValueAtTime(curDelay+\hat{t}_{i})\);
25\(\hat{r}_{i}\Leftarrow\frac{\hat{e}_{i}}{\hat{e}_{i}}\);
26\(u\Leftarrow\hat{r}_{i}+5\sqrt{\frac{2\log(\frac{1}{i})}{C_{i}}}\);
27 if\(u>maxI\)then
28\(selected\ Allocation\Leftarrow i\);
29\(maxI\Leftarrow u\);
30
31 end if
32
33 end if
34
35 end for
36
37 Allocate job according to selectedAllocation;
38 Update data center resources;
39
40 end if
41
42 end for
43
44 end for
```  
**Algorithm 1**CBA Resource Allocation.
## 3 Results  
### Load-Displacement Curve  
Figure 4 shows the load-displacement curves for DTT and BST at different loading angles. The load-displacement curves for the DTT and BST specimens could be divided into four distinct stages: compaction, elastic deformation, critical failure, and post-peak stages. The maximum peak loads for the DTT and BST specimens were 9.60 kN and 22.31 kN, respectively, at \(\beta\) = 90\({}^{\circ}\). At \(\beta\) = 0\({}^{\circ}\), the corresponding peak loads were 4.06 kN and 13.49 kN, respectively. Notably, the BST specimens exhibited significantly higher peak loads compared to the DTT specimens. At the same loading angle, the maximum difference in peak load between DTT and BST specimens reached 12.83 kN.
## 4 Results and Discussion
### Discussion
#### 4.5.1 Empirical Equations for Ancient Braided Rivers Based on Sedimentary Outcrops
's braided-meandering transition rivers [17] further validates these mechanisms: their intermediate vegetation cover (15-25%) and discharge variability (5.7) result in a \(\mathrm{W_{b}}\)-\(\mathrm{d_{m}}\) exponent (0.67) that falls between our study (0.9656) and [PERSON]'s results, consistent with the linear relationship between vegetation cover, discharge variability, and exponent values (R\({}^{2}\) = 0.82). The \(\mathrm{W_{b}}\)-\(\mathrm{d_{m}}\) relationship for coarse sandy-gravelly braided rivers established in this study relies on only the existing 15 sets of data. To enhance broader applicability, more ancient and modern braided river sediment data in this environment still need to be verified. Additionally, the empirical relationships derived from this study can be scaled up to larger scales (e.g., basin or regional scales) by accounting for spatial heterogeneity. For instance, when extending to a basin scale, sub-basins are first classified based on lithological and tectonic characteristics; coefficients of the empirical equations are then adjusted according to the average slope and sediment supply rate of each sub-basin, ensuring the relationships adapt to variations in macro-geomorphic settings. Meanwhile, integration with numerical models follows a structured, iterative process: (1) The empirical relationships constrain key input parameters of numerical models (e.g., initial channel width-depth ratios in the Delft3D model); (2) model simulations are conducted to generate dynamic evolution results (e.g., channel migration distance over a 100-year period); (3) simulated outputs are compared with field-measured data (e.g., historical aerial images or sediment core records) to verify consistency; (4) discrepancies prompt revisions to the empirical relationships--such as reweighting the influence of variables (e.g., increasing the weight of sediment supply in width equations)--until model outputs align with observations. This iterative loop enables these relationships to robustly support numerical simulations of river behavior across diverse temporal and spatial scales, thereby enhancing the predictive capacity for long-term river system evolution.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 3 State Representation
### State Format
Disjunctive graphs [272,274,277,278,279] use both types of arcs and can encode structures with complex relationships, which is especially useful for multi-entity interactions such as scheduling. Graph node features [81,246,278,282] allow for considering multiple features per resource regardless of the problem size, while edge features [19,113,150,239] are more focused on relationships between entities. These are both features used in graph neural networks (GNNs) [188,246,271,278], which process graph-based states more generally at the cost of higher computational complexity. GNNs have the advantage of being size-agnostic. Other approaches also allow for **variable-sized** state formats [141,181], which are applicable to complex problems where the number of features can change. An example is recurrent neural networks (RNNs) [47,178,184,238], useful for capturing temporal dependencies for sequential decision making problems. These bring great flexibility and scalability to the problem, again at the cost of extra computational effort. Lastly, **fuzzy** approaches [83,246] can be used to handle uncertainty and imprecise states.
## 2 Materials and Methods
### Simulation and Experiment  
To analyse the performances and the capabilities of the robot, a trajectory in the open air that emulates an inspection task was designed and simulated in MATLAB(r) 2019b. The task consists of performing a visual inspection of some points of interest, avoiding a circular obstacle and passing through a narrow entrance. Given a reference frame fixed to the robot base and whose origin is on \(J_{1}\), as shown in Figure 2, the centre of the obstacle is located at \([0\;\mathrm{m},3.25\;\mathrm{m}]\) and its radius is \(0.75\;\mathrm{m}\). The entrance is modelled as two circular obstacles, both of which have a radius of \(0.25\;\mathrm{m}\) and whose centres are located at \([-0.65\;\mathrm{m},1.5\;\mathrm{m}]\) and \([0.65\;\mathrm{m},1.5\;\mathrm{m}]\), respectively. The ground is modelled as a line located at \(y=-0.8\;\mathrm{m}\). The points of interest are the following: \(\mathrm{P}_{1}=[-0.076\;\mathrm{m},1.572\;\mathrm{m}]\), \(\mathrm{P}_{2}=[0.414\;\mathrm{m},1.850\;\mathrm{m}]\)  
Figure 11: Control algorithm input output relation.  
\(\mathrm{P_{3}}=[0.836\;\mathrm{m},2.257\;\mathrm{m}]\), \(\mathrm{P_{4}}=[1.186\;\mathrm{m},3.151\;\mathrm{m}]\), and \(\mathrm{P_{5}}=[1.343\;\mathrm{m},3.525\;\mathrm{m}]\). Figure 12 shows the environment, the robot initial configuration, and the simulation of the robot in the configurations to inspect the points of interest. Once the simulation confirmed the feasibility of the trajectory, the same task was performed at an industrial site using the SLIM robot. The robot was subject to the same external conditions found in a real environment but did not enter the high temperature containment vessel. Figure 13 shows pictures of the robot performing the inspection task on-sight at an industrial facility.  
Figure 12: Simulation of SLIM performing the above–described inspection task. (**a**) shows SLIM at its initial configuration, H; (**b**) shows SLIM at \(\mathrm{P_{1}}\); (**c**) shows SLIM at \(\mathrm{P_{2}}\); (**d**) shows SLIM at \(\mathrm{P_{3}}\); (**e**) shows SLIM at \(\mathrm{P_{4}}\); (**f**) shows SLIM at \(\mathrm{P_{5}}\).  
Figure 13: Pictures of SLIM performing the inspection task. (**a**) shows SLIM at \(\mathrm{P_{1}}\); (**b**) shows SLIM at \(\mathrm{P_{2}}\); (**c**) shows SLIM at \(\mathrm{P_{4}}\).
## 3 Problem Description and Formulation  
### Problem Description and Characteristics  
The multi-trip heterogeneous vehicle routing problem with prioritized customer and incompatible cargoes (MTHVRP-PCIC) is formally defined as follows. Let \(G=(N,A)\) with \(N=(0,1,\cdots n)\) be a complete directed graph in which node 0 represents the depot from where special vehicles start and the remaining nodes correspond to the locations of customers (such as factories or research institutes). Each arc \((i,j)\in A\) has an associated travel distance \(d_{ij}\) and travel cost \(c_{ijv}\) for special vehicles \(\
u\) per kilometer. The locations of customers is represented by \(\mathcal{C}\). The delivery for a customer \(i\in C\) is characterized by a demand quantity \(d_{i}\).  
In the real world, a customer may require different kinds of goods that are not permitted to be loaded into the same vehicle (such as inflammable gas and combustion-supporting chemical reagents), which results in more than one delivery for such customers. To model the problem succinctly, we divided customer \(i\in C\) with demand quantity \(d_{i}^{a}\) for cargo \(a\) and \(d_{i}^{b}\) for cargo \(b\) into two customers \(i\) with demand quantity \(d_{i}=d_{i}^{a}\) and customer \(n+1\) with demand quantity \(d_{n+1}=d_{i}^{b}\), and then \(n\to n+1\). It should be noted that these two customers are in the same location. The above process is repeated for the remaining customers with different kinds of cargo that are permitted to be loaded into the same vehicle. Therefore, each customer will then be visited only once per vehicle.  
The MFHVRP-PCEC developed in this paper has the following characteristics:  
* The vehicles are heterogeneous;
* Some of customers have priority over others;
* More than one kind of cargo is transported and some cargoes are not permitted to be loaded into the same vehicle;
* Each vehicle starts and ends at the depot;
* Each vehicle cannot exceed the capacity limit;
* Each customer is visited only once;
* Each vehicle can continue to serve customers again after returning to the depot within the transport planning period.
## 1 Introduction
### Background
#### 1.1.3 Security  
Data protection security refers to any procedures that use suitable technological or organizational methods to safeguard digital information from unauthorized or illegal access, corruption, destruction or damage, or theft across its full life cycle [46]. The GDPR is the world's strongest privacy and security regulation. Despite the fact that it was designed and passed by the EU, it puts duties on all enterprises that target or collect data about people in the European Union [46].
###### Abstract
When assigning each data point an appropriate fuzzification coefficient \(m\) based on the density of that point to the surrounding points, the clustering quality was improved. This study is based on the concept of utilizing multiple fuzzification coefficients to apply to semi-supervised fuzzy clustering. When placing a supervised element \(i\) into cluster \(k\), the appropriate fuzzification coefficient \(m_{ik}\) can be selected, which will be different from other fuzzification coefficients. This affects the calculation of \(U_{ik}\) which increases, and at the same time, \(U_{ik}\)'\(m_{ik}\) changing also affects the determination of cluster \(k\) center. Similarly, when preventing a supervised element \(i\) from being in cluster \(k\), its parameter \(m_{ik}\) will be different from other \(m_{ij}\) values. The adjustment of the fuzzification coefficient is similar to using the auxiliary matrix similar to other semi-supervised clustering methods, and it is also needed to ensure the convergence of the clustering algorithm. The contribution of this study is the proposal of a novel semi-supervised FCM algorithm using multiple fuzzification coefficients (sSMC-FCM) as well as the method to determine the appropriate fuzzification coefficient values for semi-supervision. Section 2 of the paper outlines the FCM algorithm and the semi-supervised fuzzy clustering algorithm using auxiliary matrices. Section 3 presents the novel sSMC-FCM algorithm. Section 4 shows a numerical example using the proposed method and the subsequent results, while Section 5 presents concluding remarks.
## 6 Results and Discussion
### Impact of Rotating Brushes on Vibration
It is noteworthy that the count of smaller \(\Delta z\) values (below 1.0 mm) increases, appearing to the left of the cut-off line. This implies that while the multi-suspension unit does not entirely eliminate vibrations, it effectively reduces their amplitude. Moreover, the placement of the cut-off line significantly determines the extent of vibration reduction. Specifically, the ratio between the distance separating the two cut-off lines and the width of the horizontal axis is inversely related to the level of vibration. Placing the SPCR at the long edge of the PV panel results in the most effective vibration suppression. In contrast, when the SPCR is positioned near the PV panel's short edge, close to the supporting bar, the difference in the count of significant \(\Delta z\) values between the SPCR with and without the multi-suspension unit becomes negligible, as depicted in Figure 18b. This observation indicates that the multi-suspension unit strongly influences vibrations characterized by larger amplitudes. Figure 17: Raw data of PV panel deflection under rotating brushes: (**a**) SPCR placed at the center of the PV panel; (**b**) SPCR placed along the long edge of the PV panel; (**c**) SPCR placed at the short edge of the PV panel.
## 3 Data-Driven Solutions Applied for the Detection of NTLs
### Supervised Methods
#### 3.1.2 Support Vector Machine  
The SVM method is one of the most widely used techniques for detecting NTLs due to its good performance and adaptability, resilience to class imbalance, ease of use, and tendency to yield results with fewer adjustments [15; 16; 66].  
It is a binary classifier that has gained much popularity in recent years, as it achieves results when the separation boundary between classes is almost linear; otherwise, its classification performance drops sharply [25; 26; 31; 32; 33; 34; 42]. To address this issue, the algorithm uses a dimensionality increase with a Kernel. A Kernel (Linear Kernel, Polynomial Kernel, or Gaussian Kernel) is a function that provides the response of a dot product between two vectors, executed in a recent dimensional space compared with the original space where the vectors are located [31]. An example can be seen in Figure 4. Given a set of training samples, we can label the classes and train an SVM model with them, which will serve to predict the class of a new sample.  
An SVM represents the points of a sample in space and separates the classes into two spaces as widely as possible through a separation hyperplane defined by the two points of the two closest classes, which are called support vectors. When we use a new sample on this model, we can determine which class it belongs to.  
An SVM model using energy data from users in Malaysia achieved detection rates ranging from 3% to 60%. The same authors combined the SVM model with induction rules  
Figure 3: Representation of a dataset classified into four different classes: Class A (red circles), Class B (purple triangles), Class C (blue squares), and Class D (yellow stars). (**a**) Original distribution of the data in two-dimensional space. (**b**) Classification process of a new point (in gray) using the K-NN (K nearest neighbors) algorithm, where the closest points are considered to determine its class.  
(IF-THEN) in a subsequent study, achieving detection rates of 72%. In [36], the authors trained an SVM model by using 20 input attributes, including socio-economic variables and statistical variables related to customer consumption, achieving success rates of 70%. Alongside these models, the use of sensors and meters is an option; however, this increases costs, making it potentially impractical for some distribution companies [42].
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 2 Progresses in QKD Networks  
### Researches on Architecture of The Networks  
In 2016, [PERSON] et al. discussed the impact of SDN on QKD-device deployment, and proposed a quantum sensing SDN architecture by dividing the network into three layers, i.e., application layer, control layer, and infrastructure layer [16]. In 2017, [PERSON] et al. proposed a distributed NFV MANO architecture by combining NFV orchestration with QKD technology through the scheduling of SDN controller, and integrating IDQ QKD system with experiments [10]. In the same year, as shown in Figure 1a, [PERSON] et al. proposed a three-layer architecture of QKD networks named software-defined QKD network to solve the problem of complex management caused by excessive resource consumption [17]. In addition, as shown in Figure 1b, [PERSON] et al. proposed a four-layer architecture from the perspective of how to use QKD to enhance the security of SDONs, including application layer, control layer, QKD layer, and data layer [10].  
Figure 1: The architecture of optical networks secured by QKD [17].
## 4 Experimental Results
### Evaluation
#### 4.2.1 Evaluate under Noise-Free Conditions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Reindeer**} & \multicolumn{4}{c}{**Laundry**} & \multicolumn{4}{c}{**Dolls**} \\ \cline{2-13}  & **2x** & **4x** & **8x** & **16x** & **2x** & **4x** & **8x** & **16x** & **2x** & **4x** & **8x** & **16x** \\ \hline Bicubic & 1.92 & 2.80 & 3.99 & 5.86 & 1.60 & 2.40 & 3.45 & 5.07 & 0.89 & 1.29 & 1.95 & 2.62 \\ JID [34] & 0.91 & 1.47 & 2.19 & 4.15 & 0.72 & 1.19 & 1.77 & 3.47 & 0.73 & 0.96 & 1.26 & 2.06 \\ SRCNN [9] & 0.81 & 1.87 & 3.87 & 5.64 & 0.67 & 1.74 & 3.45 & 5.04 & 0.61 & 0.95 & 1.52 & 2.54 \\ LapSRN [35] & 0.80 & 1.31 & 1.92 & 4.56 & 0.78 & 1.12 & 1.67 & 3.79 & 0.77 & 0.98 & 1.42 & 2.28 \\ AR [36] & 2.99 & 3.09 & 4.33 & 4.99 & 2.39 & 2.43 & 3.01 & 4.47 & 1.01 & 1.23 & 1.65 & 2.23 \\ VDSR [37] & 1.01 & 1.50 & 2.28 & 4.17 & 0.72 & 1.19 & 1.59 & 3.20 & 0.63 & 0.89 & 1.31 & 2.09 \\ MSG-Net [23] & 0.66 & 1.47 & 2.46 & 4.57 & 0.79 & 1.12 & 1.51 & 4.26 & 0.68 & 0.92 & 1.47 & 1.86 \\ MFR-SR [24] & 0.65 & 1.23 & 2.06 & 3.74 & 0.61 & 1.11 & 1.75 & 3.01 & 0.60 & 0.89 & 1.22 & 1.74 \\ DRN [15] & 0.59 & 1.11 & 1.80 & 3.11 & 0.52 & 0.92 & 1.52 & 2.97 & 0.58 & 0.91 & 1.31 & 1.87 \\ RDN-GPC [38] & 0.51 & 1.17 & 2.05 & 3.58 & 0.48 & 0.96 & 1.63 & 2.86 & 0.56 & 0.88 & 1.21 & 1.71 \\ PAG-Net [39] & 0.31 & 0.85 & 1.46 & 2.52 & 0.30 & **0.71** & 1.27 & 1.88 & - & - & - & - \\ PMBANet [40] & 0.41 & 1.17 & 1.74 & 2.17 & 0.38 & 0.99 & 1.54 & 1.78 & **0.36** & 0.96 & 1.24 & **1.42** \\ PDR-Net [41] & - & 1.44 & 1.67 & **1.99** & - & 1.29 & 1.52 & 1.90 & - & 1.03 & 1.22 & 1.45 \\ ours & **0.27** & **0.79** & **1.38** & 2.17 & **0.25** & 0.23 & **1.21** & **1.74** & 0.37 & **0.84** & **1.15** & 1.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative Analysis of Noise-Free DSR Results (RMSE) on Dataset C.