esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety
## 3 Methodology
### Evaluation Criteria  
#### 3.4.1 Evaluation Metrics  
To measure the effectiveness of the YOLOv5x algorithm, five major metrics are used: accuracy, precision, recall, F1 score, and mAP. All performance metrics can be calculated using the four parameters of the confusion matrix, i.e., True Positive (T.P.), False Positive (F.P.), True Negative (T.N.), and False Negative (F.N.). T.P. represents the correct prediction of a person wearing a safety helmet, F.P. represents the incorrect prediction of a person wearing a safety helmet, T.N. represents the accurate prediction of a person without a safety helmet, and F.N. represents the inaccurate prediction of a person without a safety  
Figure 5: Architecture of YOLOv5 for Safety Helmet Detection.  
helmet. Accuracy, precision, recall, F1 score, and mAP are mathematically explained in Equations (4)-(8). F1 score is the harmonic mean of precision and recall.  
\[\text{Accuracy}\ =(\text{TP}+\text{TN})/(\text{TP}+\text{FP}+\text{TN}+\text{ FN}) \tag{4}\]  
\[\text{Precision}\ =\ \text{TP}/(\text{TP}+\text{FP}) \tag{5}\]  
\[\text{Recall}\ =\ \text{TP}/(\text{TP}+\text{FN}) \tag{6}\]  
\[\text{F1 score}\ =2*((\text{Precision}\ \ast\ \text{Recall}))/((\text{ Precision}\ +\text{Recall})) \tag{7}\]  
\[\text{mAP}\ =\frac{\sum_{\text{c}=1}^{\text{C}}\text{Average Precision}\ (\text{c})}{\text{C}} \tag{8}\]  
where C is the total number of output classes. In this study C = 2 (Helmet and Head).
## 2 Applications and Background of Uwsn
### Applications of Uwsn
#### 2.1.4 Underwater Pipeline Monitoring  
[PERSON] et al. propose a sensor network model for monitoring underwater pipelines which can be used for monitoring underwater marine cables, gas and oil pipelines [18]. Authors also compare different architectures for pipeline monitoring. This work also addresses the challenges like reliability, power consumption and also physical security of the network. [PERSON] et al. propose a Uwsnarchitecture for pipeline vandalization and oil spillage monitoring and detection [19]. This work also explored the viability, challenges and feature of these applications for implementing in Nigeria. The Security and deployment challenges of the wireless sensor nodes in the Niger-Delta oil and gas sector is addressed in [20].  
Figure 1 presents a generalized underwater pipeline monitoring system with UWSN. Figure shows that, the static sensor nodes remain attached to the pipelines that are to be monitored. These sensor nodes collect the structural data of the pipelines and relay them to the dynamic floating nodes. Even though these floating nodes are allowed to move, they remain attached to the sea bed or floating buoy with strings to keep them in range. These floating nodes relay the data to the surface sink from where it reaches to the control center with RF communication.
# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 2 Proposed Approach
### Compactness-Based Saliency Map  
#### 2.2.1 Compactness Calculation  
Compactness is the measurement of the spatial variance of low-level feature. In [8], the compactness is defined as:  
\[D_{i}=\sum_{j=1}^{N}\bigl{|}|p_{j}-\mu_{i}|\bigl{|}2w(c_{i},c_{j}) \tag{2}\]  
where \(w(c_{i},c_{j})\) describes the similarity of color \(c_{i}\) and color \(c_{j}\) of segments \(i\) and \(j,p_{j}\) is again the position of segment \(j\), and \(\mu_{i}=\sum_{j=1}^{N}w(c_{i},c_{j})p_{j}\) defines the weighted mean position of color \(c_{i}\).  
Figure 4: The proposed Graph model. (**a**) Input image. (**b**) Ground truth. (**c**) A diagram of the connections of one of the nodes. A node (illustrated by a yellow dot) connects to its adjacent nodes (green dot and local connection) and the most similar node (pink dot and local connection) sharing common boundaries with its adjacent nodes. Additionally, each node connects to the “true-foreground” nodes (red dot and nonlocal connection) and the “true-background” nodes (black dot and nonlocal connection). Each pair of boundary nodes connects to each other (blue dot and local connection).  
In this paper, we adopt the similar constituents with the Equation (2). However, instead of defining \(w_{ij}\) as a Gaussian filtering function, we explain the similarity using:  
\[a_{ij}=e^{-\frac{||i-i||}{\sigma^{2}}} \tag{3}\]  
As proposed in [16], in order to describe the similarity more precisely, we propagate the similarity using manifold ranking [9] on the 2-layer sparse graph:  
\[S=\left(D-\alpha W\right)^{-1}A \tag{4}\]  
where \(A=\left[a_{ij}\right]_{N\times N}\), \(S=\left[s_{ij}\right]_{N\times N}\) is the similarity matrix after the diffusion process, and \(\alpha\) specifies the relative contributions to the ranking scores from the neighbors and the initial ranking scores.  
As proposed in [16], we finally define the compactness as:  
\[ComVal(i)=\frac{\sum\limits_{j=1}^{N}||p_{j}-\mu_{i}||\cdot s_{ij}\cdot n_{j}}{ \sum_{j=1}^{N}s_{ij}\cdot n_{j}} \tag{5}\]  
where \(n_{j}\) is the number of pixels that belong to superpixel \(v_{j}\), \(p_{j}=\left[p_{j}^{x},p_{j}^{y}\right]\) is the centroid of the superpixel \(v_{j}\), and \(\mu_{i}=\left[\mu_{i}^{x},\mu_{i}^{y}\right]\) is defined as:  
\[\mu_{i}^{x}=\frac{\sum_{j=1}^{N}s_{ij}\cdot n_{j}\cdot p_{j}^{x}}{\sum_{j=1}^ {N}s_{ij}\cdot n_{j}} \tag{6}\]  
Similarly, \(\mu_{i}^{y}\) is defined as:  
\[\mu_{i}^{y}=\frac{\sum_{j=1}^{N}s_{ij}\cdot n_{j}\cdot p_{j}^{y}}{\sum_{j=1}^ {N}s_{ij}\cdot n_{j}} \tag{7}\]  
Salient regions generally have a low compactness value due to the concentrated distribution, whereas the background regions usually spread over the whole image, which means a high compactness value. Therefore, we calculate the initial saliency map using:  
\[ComSal(i)=1-norm(ComVal) \tag{8}\]  
The superpixels can be roughly divided into foreground seeds and background seeds based on the mean value of \(ComSal\). We refer the two cluster sets of foreground seeds and background seeds as \(\mathbf{FG}\) and \(\mathbf{BG}\), respectively.
## 3 The Proposed Optimization Procedure
### Random Sampling Local Search  
Once a new population has been generated through crossover and mutation operators, a Biased Random Sampling (BRS) search procedure [35] is applied in order to search for better solutions in the neighbourhood of the best-performing chromosomes created so far. To this end, a sub-population having size \(N_{best}\leq N_{pop}\) made by the best individuals obtained after each generation, is selected. For each chromosome \(C_{r}\) (\(r=1\), \(2\),..., \(N_{best}\)) belonging to the sub-population, a sample of \(N_{BRS}\) neighbour solutions is generated by modifying the sequence \(\Omega\) of groups, _i.e._, the last sub-chromosome. In the present research \(N_{BRS}\) has been set equal to 4. For each neighbour chromosome \(NC^{k}_{r}\) (\(k=1\), \(2\),..., \(N_{BRS}\)) of \(C_{r}\), the sequence of groups \(\overline{\Omega}^{k}\) is obtained as follows.  
The first group \(\overline{\Omega}^{k}_{1}\) of \(\overline{\Omega}^{k}\) is drawn from the elements of \(\Omega\) according to the following distribution of probabilities:  
\[p_{q,g}=\alpha^{g}\times\frac{1}{\sum\limits_{g}\alpha^{g}} g=1,2,...,G \tag{2}\]  
where \(P_{1,g}\) is the probability to select \(\Omega_{g}\), _i.e._, the \(g\)-th group of \(\Omega\), as first element of \(\overline{\Omega}^{k}\). Such probability is \"biased\" in the sense that it favours the first group of \(\Omega\) to the second, the second to the third, and so on. The parameter \(\alpha\) is used to control how the probability decreases when moving from a group of \(\Omega\) to the next. Conforming to [PERSON] and [PERSON] [35], a value of \(\alpha=0.8\) has been selected. Thus, supposing to have \(G=3\), the first element of the new sub-chromosome \(\overline{\Omega}^{k}\) will be drawn from \(\Omega\) according to probabilities 0.410, 0.328, and 0.262 for the first, the second, and the third group of \(\Omega\), respectively.  
Figure 5: **(a)** Allele Swapping (ASO); and **(b)** Block Swapping (BSO) mutation Operators.  
Once \(\overline{\Omega}_{1}^{k}\) has been drawn, the second group of \(\overline{\Omega}^{k}\), _i.e._, \(\overline{\Omega}_{2}^{k}\), will be extracted from the remaining ones. More in general, the \(q\)-th group of \(\overline{\Omega}^{k}\) will be drawn from the remaining elements of \(\Omega\) according to the following distribution of probabilities:  
\[p_{q,g}=\alpha^{g}\times\frac{1}{\sum\limits_{g}\alpha^{g}}\hskip 56.905512 ptg=1,2, \ldots,G+1-q \tag{3}\]  
After a total amount of \(N_{BRS}\) neighbor solutions are originated from chromosome \(C_{r}\), the best one is used for replacing \(C_{r}\) in the current population, just in case it leads to a lower total flow time value. Then, the same procedure is executed for the next chromosome of the selected sub-population.
## 2 Materials and Methods
### Energy Output Flow  
Energy output flow (\(EOF\)) of crop production is the energy generated by a product and a byproduct. For cereal crops, EOF is calculated as the sum of the calorific values of grains and straw:  
\[EOF=Yg\cdot LHVg+Ys\cdot LHVs,\ \text{MJ}/\text{ha}, \tag{6}\]  
where \(LHVg\) is the specific calorific value of grain, MJ/kg; \(LHVs\) is the lower heating value of straw, MJ/kg.  
The yield of straw is calculated based on the grain yield and a residue-to-crop ratio:  
\[Ys=Yg\cdot RCR,\ \text{kg}/\text{ha}, \tag{7}\]  
where \(RCR\) is the residue-to-crop ratio of a certain crop.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & **Unit** & **Value** & **Reference** \\ \hline
**Fuels:** & & & \\ Diesel fuel & MJ/kg & 56.8 & [61] \\ Petrol & MJ/kg & 60.2 & [61] \\ Natural gas & MJ/kg & 54.6 & [61] \\
**Electricity:** & & & \\ Coal power plant & MJ/MJ & 3.14 & [61] \\  & MJ/kWh * & 11.30 & \\ Natural gas power plant & MJ/MJ & 2.66 & [61] \\  & MJ/kWh * & 9.36 & \\ Nuclear power plant & MJ/MJ & 3.26 & \\  & MJ/kWh * & 11.74 & \\ Hydro power plant & MJ/MJ & 1.05 & \\  & MJ/KWh * & 3.78 & \\ Solar power plant & MJ/MJ & 1.17 & [61] \\  & MJ/kWh * & 4.21 & \\ Wind power plant & MJ/MJ & 1.05 & \\
**Fertilizers:** & & & \\ Nitrogen & MJ/kg & 52.02–121.21 & [61; 62; 63; 64] \\ P\({}_{2}\)Og & MJ/kg & 12.60–63.02 & [61; 62; 63; 64] \\ K\({}_{2}\)O & MJ/kg & 6.70–16.77 & [61; 62; 63; 64] \\
**Maure**: & MJ/kg & & \\ Poultry manure & MJ/kg & 0.30 & [61] \\ Cattle manure & MJ/kg & 16.1 & [61] \\ pig & MJ/kg & 19.4 & [61] \\ layer & MJ/kg & 11.9 & [61] \\ broiler & MJ/kg & 17.9 & [61] \\ Chemicals & & & \\ Pesticides & MJ/L & 447–820 & [61] \\ Herbicides & MJ/L & 454.20 & [65] \\ Insecticides & MJ/L & 184.20 & [65] \\ Fungicides & MJ/L & 97.10 & [65] \\
**Machinery:** & & & \\ Tractor & MJ/kg & 138 & [61] \\ Implement & MJ/kg & 69 & [66] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Embodied energy in the main inputs.
# Deep Reinforcement Learning for Energy-Efficient 6G V2X Networks
## 3 System Model and Problem Formulation
### Energy-Efficient Deep Reinforcement Learning Allocation Framework
#### 3.5.4 PPO Algorithm  
Our proposed PPO-based algorithm uses an artificial neural network (NN) with an actor-critic framework. The PPO algorithm is selected for its stability and efficiency as a policy-based RL algorithm. The training process of the proposed algorithm is illustrated in Algorithm 1. The critic NN is based on a value function, where the state of the environment changes when the actor NN takes an action \(a_{t}\). The critic NN takes the state \(s_{t}\) as input and returns the state-value function as  
\[V^{\pi}(s_{t})=\mathbb{E}_{\pi}\Bigg{[}\sum_{\tau=0}^{\infty}\gamma\cdot r_{ \tau}(s_{\tau},a_{\tau})\mid s_{0}=s_{t}\Bigg{]}=\mathbb{E}_{\pi}\Big{[}r_{t}( s_{t},a_{t})+\gamma\cdot V^{\pi}(s_{t+1})\mid s_{0}=s_{t}\Big{]}, \tag{23}\]  
where \(\mathbb{E}_{\pi}\) is the estimator of the policy \(\pi\) and \(\gamma\) is the discount factor. The critic NN returns the state-action value as  
\[Q^{\pi}(s_{t},a_{t})=\mathbb{E}_{\pi}\Bigg{[}\sum_{\tau=0}^{\infty}\gamma\cdot r _{\tau}(s_{\tau},a_{\tau})\mid s_{0}=s_{t},\,a_{0}=a_{t}\Bigg{]}. \tag{24}\]  
The advantage function that represents the difference between the current \(Q\) value and the average can be calculated as  
\[A^{\pi}(s_{t},a_{t})=Q^{\pi}(s_{t},a_{t})-V^{\pi}(s_{t},a_{t}). \tag{25}\]  
The actor network is updated using PPO by the ratio of the new policy and the old policy to ensure stability. The loss function of the actor network can be determined as  
\[L(\theta)=\mathbb{E}_{t}\Bigg{[}\frac{\pi_{\theta}(a_{t}\mid s_{t})}{\pi_{ \theta_{old}}(a_{t}\mid s_{t})}\,\hat{A}_{t}-\beta\,\text{clip}\Big{[}\pi_{ \theta_{old}}(\cdot\mid s_{t}),\pi_{\theta}(\cdot\mid s_{t})\Big{]}\Bigg{]}, \tag{26}\]  
where \(\pi_{\theta}\) is a random policy function, \(\beta\) is a penalty coefficient, and \(\hat{A}_{t}\) is an estimator of the advantage function at time \(t\).  
```
1 Initialize the V2X environment parameters.
2 Initialize critic and actor networks.
3 Set the policy update period T
4 for\(t=1,2,...\)do
5 Assignment agent interacts with V2X environment under the policy \(\pi\).
6 if\(t\) mod \(T=0\)then
7 Calculate the advantage value in Equation (25).
8 Update the critic network.
9 Calculate the loss function in Equation (26).
10 Update the actor network.
11 end if
12
13 end for
```  
**Algorithm 1**Resource Allocation Algorithm Based on PPO
## 5 Analysis of Hydrodynamic Coefficients
### UMaine TLP  
The mesh discretization of the UMaine TLP wetted hull with triangular panels is presented in Figure 13. The UMaine TLP features an upper hull over a lower hull connected with four tendon arms. The panel properties for each structure are introduced in Table 4.  
The properties of added-mass and radiation damping coefficients with different panel numbers in the surge, heave, and pitch directions are illustrated in Figure 14a-f, respectively. It is shown that the peak values of these two hydrodynamic coefficients are occurred at different locations in surge, heave and pitch directions. The difference of added-mass coefficients \(\mu_{11}\) and \(\mu_{33}\) for the UMaine TLP is less than the one for the MIT/NREL TLP because the displaced water and the wetted surface in the surge mode are only slightly larger than those in the heave mode. Since the radius and diameter of the pontoon for the UMaine TLP are larger than the ones for the MIT/NREL TLP, the value of added-mass coefficient in the pitch mode \(\mu_{55}\) is found to be considerably larger than the results in the heave and pitch modes. The increases in the radius and diameter of the pontoon shift the resonance peak of the radiation damping coefficient in the surge mode to the high frequency, whereas the decrease in the diameter of the column shortens the resonance peak frequency of the radiation damping coefficient in the heave mode.  
By means of the grid-independence test shown in Figure 14c, it is exhibited that the difference ratios of 1322 panels and 1834 is caused by the mesh distribution over different parts of UMaine TLP. However, the convergence is considered to be reached because the difference ratios of 1322 panels and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Length/Width (m/m)** & **Panel Type** & **Panel Number** \\ \hline
**Upper Hull** & 0.79/1 & Triangular & 648 \\ \hline
**Lower Hull** & 2/0.25 & Triangular & 332 \\ \hline
**Tendon Arm (Pontoon)** & 5/0.6 & Triangular & 340 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Panel properties of the UMaine TLP wetted hull.  
Figure 13: The mesh discretization of the UMaine TLP with triangular panels. (Unit: m.)  
1834 panels for all hydrodynamic coefficients are still less than 1%. Thus, the number of 1322 panels is refined enough for the subsequent simulation of the UMaine TLP as shown in the grid-independence test. In addition, the aspect ratios of triangular panels for upper hull, lower hull and tendon arms are set to be 0.79/1 for 648 panels, 2/0.25 for 332 panels, and 5/0.6 for 340 panels, respectively. Eventually, all panels can be grouped into three families of the same size and proportion by panel clustering. By considering the same underwater meshes for OC3-Hywind and UMaine-Hywind spars in different water depths, it is found that the water depth does interfere with the hydrodynamic coefficients in the heave and pitch modes due to the boundary effect.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Experimental Evaluation
#### 5.4.3 Impact of Number of QoS Parameters  
In this experiment, we varied the number of additive link parameters, \(k\), while maintaining the same setup as before (60 nodes within \(100\times 100\) m\({}^{2}\), a communication range of 20, and a fixed \(L=10\)). The link parameters were assigned \(2,3,4\), or 5 uncorrelated values from the interval \((0.0,5.0)\) (as depicted in the first column of Table 7). The results are also averaged across 20 runs. The second column in the table indicates the average all-inclusive number of feasible paths during the computation (regardless of their use, the feasible and theoretically possible paths). This column illustrates that increasing the number of uncorrelated parameters decreases the number of feasible solutions.  
The results are illustrated in Figure 6. The second part shows the performance of the three algorithms. In the observed cases, the heuristic \(H_{1}\) based on the derived auxiliary length covers 90% of the feasible nodes. For the heuristic \(H_{2}\), this ratio is 96%. Following the results in Table 7, the number of uncorrelated QoS parameters influences the number of feasible solutions. Increasing this number from 2 to 5 shows that overall feasible paths decrease.  
Table 8 further demonstrates that, as the number of QoS parameters increases, the non-linear path lengths from source nodes to the BRs also increase. This indicates that fewer paths can simultaneously satisfy all QoS constraints.  
**Key Observations:**
* Increasing the number of QoS parameters from 2 to 5 significantly reduces the number of feasible solutions (Table 7; Figure 6).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(L\)} & **Average NL** & **Average NL** & **Average of** & & & **Average NL** & & **Average of** \\  & **with** \(EX\) & **with** \(H_{1}\) & **Max NL** & **with** \(H_{1}\) & **with** \(H_{2}\) & **with** \(H_{2}\) & **Max NL** & **with** \(H_{2}\) \\ \hline
9 & 0.8668405 & 0.7970677 & 1.7778945 & 2.70073 & 0.7702697 & 1.7467435 & 2.62914 \\
10 & 0.86217155 & 0.7661964 & 1.6855505 & 1.70626 & 0.7437832 & 1.600896 & 3.07739 \\
11 & 0.8869539 & 0.6627487 & 1.4499615 & 2.06338 & 0.6373038 & 1.3756695 & 2.01094 \\
12 & 0.8927291 & 0.5788719 & 1.28033855 & 2.29483 & 0.5586699 & 1.2328301 & 2.36877 \\
13 & 0.8978134 & 0.59460195 & 1.2450855 & 2.34292 & 0.5738024 & 1.21144645 & 2.35253 \\
14 & 0.90484325 & 0.52155955 & 1.2165917 & 1.81337 & 0.5088827 & 1.1766933 & 1.80848 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average of non-linear lengths (NLs) and maximal non-linear lengths by varying the QoS requirement \(L\).  
Figure 5: Number of feasible/unfeasible source nodes with different QoS requirements \(L\).  
The proposed method remains consistently superior, maintaining higher feasibility even as dimensionality increases.  
Table 8 shows that both average and maximum path lengths increase with \(k\), indicating higher routing complexity.
# Light-Activating PROTACs in Cancer: Chemical Design, Challenges, and Applications
## 4 Photoswitches in PROTACs
### Photoswitchable PROTACs in Immunophilins  
To enhance the spectrum of optical control to targeted protein degradation, [PERSON]'s and [PERSON]'s research groups utilized photoswitchable PROTACs for prolyl _cis_-_trans_ isomerase (FKBP12 protein [70]) [50]. They synthesized six structures (PHOTAC-II-1-6, as shown in Figure 14), where a lenalidomide-based structure was used as an E3 ligase ligand (in the case of five photoswitchable PROTACs **PHOTAC-II-1-5**), while a pomalidomide-based structure was used as an E3 ligase ligand in the chemical design of **PHOTAC-II-1-6**. The authors kept POI inhibitor as **SLF** (a synthetic ligand of FKBP12) common for **PHOTAC-II-1-6**. However, the azobenzene photoswitch varied in its positioning, wherein it was tethered adjacent to the E3 ligase ligand in the case of **PHOTAC-II-1-5**, whereas it was placed in the middle of **SLF** and the E3 ligase ligand in the case of **PHOTAC-II-6**. On the basis of the biological evaluation, **PHOTAC-II-5** and **PHOTAC-II-6** showed effective activity among six photoswitchable PHOTACs. Upon pulse irradiation (\(\lambda\) = 390 nm) by **PHOTAC-II-5**, significant cellular FKBP12 protein levels were reduced, but the same observation was not found in the dark condition. A general observation of attaining comparatively higher protein degradation activity for **PHOTAC-I-1-8** (for bromodomain proteins) and **PHOTAC-II-5** (for FKBP12 protein) than other photoswitchable PROTACs, and on the basis of their structural resemblance, the role of the positioning of azoswitch tethering to the E3 ligase ligand could be directed. Conversely, with **PHOTAC-II-6**, the azobenzene photoswitch positioned in the middle of the linker region showed a pattern similar to **PHOTAC-II-5** of strong light-dependent FKBP12 protein degradation and a mild activity in the dark (at 24 h), suggesting (_E_)-isomer presence in the solution. The competitive assay of **PHOTAC-II-5** and **PHOTAC-II-6** with **MLN4924** showed their inability to degrade the FKBP12 protein, suggesting the mechanism of CRBN mediation. Increased concentration of the PROTACs leads to a pronounced _hook effect_ during experiments.  
Figure 14: Chemical design of photoswitchable PROTACs for FKBP12 protein.
# DRL-Based Load-Balancing Routing Scheme for 6G Space-Air-Ground Integrated Networks
## 2 Related Work  
There are no agreed-upon values for the orbit altitude and orbit period of LEO satellites. However, many sources claim that the orbit altitude of these satellites changes from 160 km to 2000 km, while their orbit period, mainly depending on the orbit altitude, ranges between 90 min and 2 h [8]. The configuration of the inter-satellite links topology is closely related to the constellation design. Therefore, in this section, we provide a comprehensive review of the current state of research on LEO satellite routing technology, load-balancing technology, and machine-learning-based satellite routing technology. Specifically, we discuss the latest developments in these areas and present the relevant literature to help readers better understand the challenges and opportunities facing LEO satellite networks. By doing so, we aim to lay the foundation for the proposed DQN-based load-balancing routing algorithm for LEO satellites in the following sections.
## 2 Theoretical Framework
### Uncertainty in Component Reliability Data
#### 2.2.2 Component Repair Time  
A component's repair time represents the time required to restore a component to operational status after a failure. In 10 kV distribution networks, this parameter varies significantly depending on several factors, including the location's accessibility, the type of component, the availability of spare parts and personnel, and the complexity of the repair. This study defined a specific range for each component, within which the repair time value was varied. Since repair time directly influences network reliability, incorporating it as a stochastic parameter in the MCS ensures a more accurate assessment of reliability indicators in the 10 kV distribution network.