esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Smart Contract Cost  
We used Ganache and Truffle to implement a private blockchain and develop the contracts. Ganache is a personal Ethereum blockchain which enables developers to create smart contracts, dApps, and test software, and inspect state while controlling how the chain operates. Truffle is a development environment, testing framework and asset pipeline for blockchains based on the Ethereum Virtual Machine (EVM). The first point observed during the building of the network was the limit amount of gas for each block. By default, Ganache uses blocks of 6,721,975 gas, while Main-net currently uses blocks of 8,000,000 gas. To set the limit amount of gas per block on our blockchain, we first verified the gas cost of the smart contract developed.  
As we can see in the algorithm shown in Figure 3, the system performs an address mapping that can change the privacy preferences of a contract. If the gateway verifies that the contract does not include the request address, it cannot be changed. We consider that one or more government agencies can request monitoring, and therefore each agency must have a registered address.  
A smart contract consumes an amount of gas to be stored into the Ethereum blockchain. To assess the gas cost of each contract, we performed experiments by varying the number of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Keys Recovery/DB Size** & **100 K** & **1 KK** & **10 KK** \\ \hline
1 & 1.57 ms & 1.60 ms & 1.66 ms \\
10 & 1.68 ms & 2.09 ms & 1.91 ms \\
100 & 1.80 ms & 7.13 ms & 9.18 ms \\
1000 & 4.12 ms & 10.03 ms & 12.05 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key recovery query execution time varying database size.  
Figure 3: Privacy preference smart contract.  
addresses authorized to change the privacy preferences of each user. The results obtained are shown in Table 3. As we can observe, the gas cost increases with the number of addresses mapped in the contract. As the Ethereum network becomes expensive to store values, we chose to use ten addresses for each contract.  
Based on the value obtained in the previous experiment, we set the limit amount of gas for each block to store 20 contracts. Table 4 presents the comparison of contracts stored in our network with Main-net and the standard Ganache network. The results show a small difference between the number of contracts stored in our network with Main-net and Ganache. This difference does not change the operation of the network compared to Main-net as the gas size of each block in Main-net varies over time.  
In our network, we set the limit amount of 6,005,800 gas per block, enabling the storage of exactly 20 contracts per block, considering the structure shown in Figure 3. This value does not compromise the network performance because we use a private blockchain.
## 2 Methodology
### Fusion Processing
#### 2.3.1 3D Coordinate Transformation  
After the object detection is completed, the coordinate information obtained from the detection can be used to quickly locate the target area, optimize the KD-Tree search process, and improve the efficiency of target point cloud retrieval. To achieve this, the 2D center coordinates need to be transformed into 3D coordinates. Since the coordinates of the object are identified in the RGB image and the resolutions of the RGB and depth images are different, the RGB image must first be normalized. This process requires the use of the depth camera's intrinsic matrix:  
\[intrinsic\ matrix=\begin{pmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{pmatrix}, \tag{1}\]  
where \(f_{x}\) and \(f_{y}\) are the focal lengths in the \(x\) and \(y\) directions of the image plane (measured in pixels), which determine the image scaling. \(c_{x}\) and \(c_{y}\) represent the coordinates of the principal point, which is typically located at the center of the image.  
Then, the 3D coordinate transformation is performed using the following equation:  
\[TargetPoint_{x}=\frac{Depth\cdot(C_{x}-c_{x})}{f_{x}\cdot Depth_{scale}}, \tag{2}\]  
\[TargetPoint_{y}=\frac{Depth\cdot(C_{y}-c_{y})}{f_{y}\cdot Depth_{scale}}, \tag{3}\]  
\[TargetPoint\_z=\frac{Depth}{Depth_{scale}}, \tag{4}\]  
where \(TargetPoint_{x}\), \(TargetPoint_{y}\), and \(TargetPoint_{z}\) denote the 3D coordinates of the target point after coordinate transformation, corresponding to the horizontal, vertical, and depth positions in the camera coordinate system, respectively; \(Depth\) denotes the raw depth value acquired by the depth camera, typically measured in millimeters (mm); \(Depth_{scale}\) takes a value of 1000 and is used to convert the depth unit from millimeters to meters to ensure consistency with the camera coordinate system; and \(C_{x}\) represents the horizontal and vertical coordinates (in pixels) of the center point of the detected bounding box after alignment with the RGB image. The target point coordinates obtained from the 2D RGB image and the corresponding depth map can thus be accurately converted to 3D coordinates using the intrinsic parameters of the depth camera.
## 3 The Optimal Selection Model of the Backhaul Links
### A Heuristic Algorithm for the Optimization Problem
```
0: UAV set \(U\), satellite set \(S\), candidate satellite set \(H=H_{1},H_{2},\ldots,H_{N}\), number of UAVs \(N\), number of satellites \(M\), constraint number of connected UAVs \(P_{max}\);
0: associated satellite set \(F\)
1: transfer \(H\) to \(H^{\prime}\)
2:for\(j=1\) to \(M\)do
3:\(b_{j}\)=subsets of \(H^{\prime}_{j}\)
4:\(p_{j}=\)Size\((b_{j})\)
5:if\(p_{j}\leq P_{max}\)then
6:\(Su^{l}_{j}=b_{j}\)
7:endif
8:endfor
9:functionFesset\((fes,j,k)\)
10:\(j=j+1\)
11:for each \(Su^{l}_{j}\)do
12: add \(Su^{l}_{j}\) into \(fes\)
13:if\(j<M\)then
14: Fesset\((fes,j,k)\)
15:endif
16:if\(j=M\)then
17:if\((fes=U)\) & (Uni\((fes)=fes\)) then
18:return\(fes\)
19:endif
20:endif
21:endfor
22:endfunction
23:\(k=1\)
24:for each \(Su^{l}_{1}\)do
25:\(fes=null,j=1\)
26: add \(Su^{l}_{1}\) into \(fes\)
27:\(F^{k}=\)Fesset\((fes,j,k)\)
28:\(k=k+1\)
29:endfor
30: calculate \(C^{k}_{sys}\) using 5
31: calculate \(T^{k}_{sys}\) using 3
32:\(\overline{C}^{k}_{sys}\)=normalize \(C^{k}_{sys}\) using 7
33:\(\overline{T}^{k}_{sys}\)=normalize \(T^{k}_{sys}\) using 6
34: calculate \(Q^{k}\) using 8
35:\(F^{m}=argmax(Q^{k})\)return\(F^{m}\)
```  
**Algorithm 1** A Heuristic Algorithm for Adjustable Association.
# How to Optimize the Implementation of ITIL through a Process Ordering Algorithm
## 2 State of Art
### Non-Fixed Sequences  
A fuzzy logic-based proposal is presented in [2]. This model is based on the fact that the decision of sequencing is dependent on organizational and technical factors, which are essentially \"abstract factors\". It proposes to select the criteria that may influence the sequence through the opinion of experts and fuzzy techniques, to express numerically the ambiguity of such opinions. The main disadvantage of the model stands on the fact that is presented in an absolute manner (it does not consider the type of company, the industry, the size, its resources, and other aspects). A very similar idea was already exposed in [27], where an ITIL process selection model is exposed to be implemented also based on fuzzy logic.  
The proposal [28] is oriented to early wins: The implementation proposes, in the first place, the processes that provide \"quick wins\", based on satisfaction surveys. Similarly, and depending on the needs, it proposes a strategy where the processes to be implemented are proposed for the medium and long term. The proposal of this author gives a solution for the problem of the order of implementation, but it is easy to verify that the implementation strategy is led by the clients opinions.  
The approach of [29] describes an ITIL adoption based on the technology adoption model (TAM). For these authors, the sequence of processes depends on the requisites exhibited by the organization, but the critical processes are known and should have priority over the rest. This idea of dependence on such issues (industry, organization, size, etc.) is also explained in [21]. The authors developed a complete study on the implementation of the ITIL. In this study they show the reasons that make organizations implement the ITIL and define the three definitive elements for the level of implementation of the ITIL: The region or geographical area, the size of the company, and the industry in which it operates.  
A compilation work is presented in [13], which is the basis for the sequence proposed: A sequence of processes defined from the validation of a series of variables that may affect the ITIL implementation. These variables take in consideration internal elements of the ITIL: The set of tasks needed to implement an ITIL process, the distribution of data, and the correct flow of information among processes.
## 2 Methodology
### Definition and Formulation of KPI  
This stage considers a deep review of the European standards EN-15341, \"Maintenance Key Performance Indicators\" [26], and EN-17007, \"Maintenance Process and Associated Indicators\" [27] in their current versions. Moreover, the formulation and definition of indicators will be based on these standards with the aim of identifying the best-fitting indicators for the decision-making process to address effective measurement.  
Standard EN-15341 [26] recognises all maintenance activities associated with maintenance management as \"the maintenance function\", which is defined as \"an integration of 6 Sub-functions with the addition of methodology of Physical Asset Management and hardware and software of the Information and Communication Technology\" [26]. Figure 1 shows the 6 sub-functions within physical asset management supported by information technologies. Consequently, there will be 5 different classifications for the formulated KPI, which are:  
1. KPIs for \"maintenance within physical asset management\";
2. KPIs for the sub-function of \"maintenance management\";
3. KPIs for the sub-function of \"maintenance engineering\";
4. KPIs for the sub-function of \"organisation and support\";
5. KPIs for the sub-function \"administration and supply\".  
Neither the sub-functions \"people competence\" and \"health-safety-environment (HSE)\" nor the support function \"information and communication technology\" will be considered for the KPI formulation, as they are not directly related to the scope of the analysed scheduling framework.  
The formulation and definition performance indicators for each sub-function are presented next. All indicators comparing different scenarios (or strategies) are used to compare a base scenario against a current scenario to be defined by the decision maker. In  
Figure 1: Maintenance functions and core framework (source: CEN [26]).  
particular, for the opportunistic grouping strategy presented in [9], different scenarios (or strategies) are defined by the chosen tolerance.
# Benefit Analysis of Gamified Augmented Reality Navigation System
## Discussion
### Research Limitations  
* Since this study is still based on mobile devices that are easily available to users and support general AR technology, devices that can enhance AR positioning quality or experience such as Bluetooth beacons, smart glasses, or edge-based SLAM applications are not within the scope of this study.
* The objective of this study was to represent the real behavior of exhibition visitors using a gamified AR navigation system. Thus, no guidance or assistance from staff members was provided, which means interruptions or other behaviors beyond exhibition viewing were unavoidable. These behaviors were not within the scope of this study.
* Exhibitions manifest in a myriad of forms. This study focused on AR-aided navigation in which visitors moved from exhibit to exhibit using a gamified approach and by following the guidance of a virtual AR guide outdoors. The results provide reference for other exhibitions and studies employing a similar approach but cannot be generalized to all types of AR navigation.
* The limitations of existing AR and GPS technologies mean that not all of the participants had optimal AR guide quality and effects. These limitations included varying smartphone hardware specifications and external environmental factors such as internet connection quality, weather, and overhead cover. These may affect the persistence and promotion of AR navigation system use.
# FCN-Based 3D Reconstruction with Multi-Source Photometric Stereo
## 2 Materials and Methods  
### The Multi-Source Photometric Stereo  
The goal of multi-source Photometric Stereo is to recover the original 3D information of the object surface from a set of images with different light source directions. Assume a fixed orthographic camera and directional lighting with multiple equal angle intervals from a fixed latitude line in the upper hemisphere. We assume that a light source from the direction of \(\overset{\rightarrow}{l}\in\mathbb{R}^{3}\) illuminates a point on the object surface, and that the surface normal of the point is represented by \(\overset{\rightarrow}{n}\ \epsilon\ \mathbb{R}^{3}\). Then, its pixel intensity can be determined as \(I=\overset{\rightarrow}{\rho}E\cdot\overset{\rightarrow}{n}\cdot\overset{ \rightarrow}{l}\), where \(\overset{\rightarrow}{\rho}\) is the sensitivity coefficient and \(E\) is the light source pre-calibrated brightness, which needs to be obtained through a specific light source calibration method. For the \(k\) different light directions \(L=\left[\overset{\rightarrow}{l}_{1},\overset{\rightarrow}{l}_{2},\ldots, \overset{\rightarrow}{l}_{k}\right]^{T}\in\mathbb{R}^{k\times 3}\), the light intensity can be expressed as \(I=\left[\overset{\rightarrow}{I}_{1},\overset{\rightarrow}{I}_{2},\ldots, \overset{\rightarrow}{I}_{k}\right]^{T}\in\mathbb{R}^{k}\), and so the image formation model can be expressed as  
\[\left[\begin{array}{c}I_{1}\\ I_{2}\\ \vdots\\ I_{k}\end{array}\right]=\overset{\rightarrow}{\rho}E\cdot\left[\begin{array}[ ]{c}\overset{\rightarrow}{l}_{1}\\ \overset{\rightarrow}{l}_{2}\\ \vdots\\ \vdots\\ \vdots\\ \overset{\rightarrow}{l}_{k}\end{array}\right]\cdot\overset{\rightarrow}{n}. \tag{1}\]  
By solving the above equations, the surface normal direction \(\overset{\rightarrow}{n}=\left(n_{x},n_{y},n_{z}\right)\) corresponding to each pixel position will be obtained.  
After that, we suppose \((p,\ q,-1)=\left(n_{x}/n_{z},\ n_{y}/n_{z},-1\right)\). Here, \(p\) and \(q\) are the gradients of a point on the three-dimensional surface in the \(X\) and \(Y\) directions respectively, and will be formed into the matrices \(\overset{\rightarrow}{P}\) and \(\overset{\rightarrow}{Q}\) in the two-dimensional space. Then, the depth of the 3D surface is defined as \(Z(x,\ y)\), and the two gradient values in the directions \(X\) and \(Y\) are \(\triangle Z_{x}\) and \(\triangle Z_{y}\). Lastly, we use the method of [PERSON] et al. [32] for reference to approximate the actual values \(\triangle Z_{x}\) and \(\triangle Z_{y}\) of the gradient with the \(\overset{\rightarrow}{P}\) and \(\overset{\rightarrow}{Q}\) obtained above. Through the classic two-dimensional integration path algorithm (path integration algorithm, PI), we can obtain a three-dimensional surface with the depth \(Z(x,\ y)\).
# Two-Stage Genetic Algorithm for Optimization Logistics Network for Groupage Delivery
## 5 Discussion
### Advantages of the Approach  
One of the key advantages of the GA is its flexibility in configuration. It can be adjusted to meet specific business needs, ensuring that resource utilization levels do not exceed predefined limits. This approach helps avoid excess costs, minimize idle capacity, and maintain a balance between resource utilization and operational efficiency.  
The use of GA eliminates the reliance on human judgment, reducing the risks associated with individual assessments, errors, and inexperience in logistics planning. The algorithm makes decisions based on objective data and pre-set parameters, ensuring a high accuracy and reliability. This minimizes errors, improves the quality of planning, and enhances the overall efficiency of logistics operations.  
The two-stage GA proposed in this study offers an approach for solving complex optimization problems, such as groupage delivery, where minimizing transportation costs is crucial for carriers. By dividing the problem into two stages--the initial facility location problem generation and the subsequent VRP--the algorithm demonstrates effectiveness in finding near-optimal solutions while accommodating the intricacies of logistics.  
The application of the two-stage GA extends beyond groupage delivery and can be adapted to various optimization scenarios in transportation and logistics. For instance, in inventory management [44], the algorithm can optimize warehouse locations and inventory allocation to minimize storage costs and meet demand fluctuations effectively.  
Additionally, the algorithm may identify opportunities for consolidating shipments to reduce the number of vessel trips and optimize fuel consumption [45; 46]. It may also prioritize deliveries based on urgency and demand [47], allocating resources efficiently to meet critical needs while optimizing the overall route efficiency.  
Furthermore, the flexibility and scalability of the algorithm make it suitable for dynamic environments where operational conditions may change rapidly [48]. By integrating real-time data and adaptive mechanisms, the algorithm can adjust routes and schedules dynamically, responding to changes in demand, traffic conditions, and other external factors.
# Comparison of Selected Parameters of a Planetary
## 5 Discussion  
The geometry of the partial pinion-gear pairs of the Wolfrom stage gearbox are presented in Figure 6 for the involute teeth flanks and in Figure 7 for the convex-concave teeth flanks. The pinion center of each presented gear pair (O\({}_{1}\), O\({}_{2}\), O\({}_{4}\)) is defined in Figure 6a,b,c, as well as in Figure 7a,b,c using the coordinates \(x=0\) and \(y=0\). The diameters of the addendum and dedendum circle is the same in both cases. The involute teeth flanks' path of contact is represented in Figure 6 by the lines declined by the pressure angle \(\alpha_{w}=20^{\circ}\) passing through the pitch point C. The convex-concave teeth flanks path of the contact is represented in Figure 7 using circles, whose parts belong to the path of contact consisting of two circular arcs tangentially connected at the pitch point C. The pressure angle, which varies along the path of contact, takes on the value \(\alpha_{C}=20^{\circ}\) at the pitch point C lying on the pinion and gear centers' link. The points A and E represent the start and end point of the path of contact. Figure 6: Geometric models of the gear pairs with involute teeth flanks: (**a**) gear pair 1-2, (**b**) gear pair 2-3, and (**c**) gear pair 4-5. Figure 7: Geometric models of the gear pairs with convex-concave teeth flanks: (**a**) gear pair 1-2, (**b**) gear pair 2-3, and (**c**) gear pair 4-5. The contact Hertzian pressure \(p\) at the teeth flanks of the partial pinion-gear pairs depends on the angle of the pinion rotation \(\phi_{pinion}\) between the pressure angles of two arbitrary points (A-E), which is shown in Figure 8 for the involute and convex-concave teeth flanks. The gear pair contact pressures dropped by the convex-concave teeth flanks are shown in Figure 8 and Table 3, where the values of the contact pressures are described in detail. The points A and E represent the start and the end of the contact path, the points B and D represent the start and the end of the single pair teeth engagement (indexed with 1 for the single and 2 for the double pair engagement), and the point C represents the pitch contact point lying on the gear pair centers' link. Table 3 also presents the flank type contact pressure ratio \(p_{C\text{-}C}\)/\(p_{INV}\) between the convex-concave (\(p_{C\text{-}C}\)) and the involut  
Figure 8: Hertzian pressure \(p\), which depends on angle \(\phi_{pinion}\) in the gear pairs with involute and convex–concave teeth flanks: (**a**) gear pair 1-2, (**b**) gear pair 2-3, and (**c**) gear pair 4-5. The specific sliding ratios \(v_{\text{ss}}\) of the partial pinion-gear pairs that depend on the angle of the pinion rotation \(\phi_{pinion}\) between the pressure angles of two arbitrary points (A-E) are shown in Figure 9 for the involute and the convex-concave teeth flanks.
# Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network
## 2 Notations and Definitions  
In order to easily describe model algorithm, we give some definitions as follows:  
**Definition 1**.: _R-pair._  
Drawing on the idea of C-pair proposed by [PERSON] [12], we define R-pair in our situation. If a pair of mRNAs are regulated by the same miRNA, there is a co-regulation relationship between the mRNA pair, i.e., CR (co-regulation interactions). Given two mRNAs, if they are regulated by the same miRNA and they also have interactions with each other, then we define this triangle structure as R-pair structure shown in Figure 1. We believe that the mRNAs in R-pair structure have more functional consistency than the mRNAs that are not. In fact, there are several studies showing that miRNA tends to target highly connected mRNAs or proteins in PPI networks, and that the R-pair structure plays important roles in cell function [9; 10; 11; 13]. So, the R-pair structure can be regarded as the core of a CMFM. By identifying R-pairs in the composite network, we can easily detect meaningful biological modules.  
**Definition 2**.: _miRNA-mRNA regulatory module density. To filter out the regulatory modules with low density values, we propose a density function shown in Equation (1)._  
\[R\_density(C)=\frac{E(Rc)}{N\times M} \tag{1}\]  
_Here, N and M represent the number of miRNAs and mRNAs in the module C respectively. \(E(R_{c})\) denotes the number of miRNA-mRNA edges in the module C. Obviously, the larger is the R_density value, the stronger are the miRNA-mRNA regulation relationships._  
**Definition 3**.: _mRNA-mRNA interaction density. To judge the density of mRNA-mRNA interaction in composite miRNA functional modules, we apply Equation (2) to compute density of these modules._  
\[I\_density(C)=\frac{2\times E(Ic)}{M\times(M-1)} \tag{2}\]  
_Here, M represents the number of mRNAs in module C. \(E(I_{c})\) denotes the number of mRNA-mRNA edges in the module C. The larger is the 1_density value, the better is the consistency of mRNAs._  
Figure 1: PPI, CR and R-pair. If two mRNAs are regulated by the same miRNA and there are also interactions between them, they form an R-pair.  
**Definition 4**.: _miRNA-mRNA expression correlation. To judge the expression correlation of miRNA-mRNA in a composite miRNA functional module, we design a correlation function shown in Equation (3)._  
\[\text{miMEC}=\frac{\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{M}cor(miRNA, mRNA, mRNA)}{N} \tag{3}\]  
_Here, \(N\) and \(M\) represent the number of miRNA and mRNAs in module \(C\) respectively. \(cor(miRNA,mRNA)\) denotes the expression correlation value of miRNA-mRNA by employing Pearson correlation coefficient. Because miRNAs repress the expression of mRNAs in a module, the more negative is \(miMEC\), the stronger is the expression of miRNA-mRNA._