esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Guideline in the Creation of the New Design Model
#### 3.3.1 Objectives  
* **Modular design as a design objective:** It has been detected, both in design methods and in real cases of application, that modular design tends to be used as a tool to solve a problem during product development. In other situations, the product is modularized to make manufacturing more flexible and standardized or to make the production process more economical. Thus, many projects are not conceived as modular design from the beginning, but this design appears as a consequence. It is necessary to consider modular design as a critical Product Design Specification (PDS), conceiving the project as a modular product from the start and, above all, in the conceptualization phase. A correct definition and understanding of the modular  
Figure 4: Relationship between modular design and the prosumer field.  
design will make it easier for the user of the model to apply and evaluate it correctly, ensuring that the result is a product based on the modular design process.
* **Conceptual phase of product design (preliminary design):** As shown in Figures 2 and 3, the current presence of prosumer and modular design methods and tools in the preliminary design phase is limited. There are various reasons for this, such as the search for economic benefits for the manufacturer or the frequent intervention of prosumers and makers in the manufacture and consumption of their products. To cover this niche, the model must be conceived for its application in the conceptual phase of product design. This is the most suitable due to the absence of other methods and the need to apply the modular design from the initial stages. In addition, the conceptual phase allows the prosumer to bring more significant and innovative changes and transformations to the final result than during the development, manufacture and use of the product.
* **Relationship with the end user (prosumer):** As has been shown in previous studies [51], prosumers and makers participate in the design and/or manufacture of their products and, subsequently, they continue to evolve them during their use. For this reason, the model should not only take into account the design, manufacturing and assembly phases, but also the later phases of the life cycle. In this regard, modular design gives the products the ability to be upgradable, repairable and adaptable--characteristics directly related to the prosumers and that report important sustainable benefits [10]. Thus, a high usability of the modular design by the end user must be empowered and eased, making it accessible and universal. The objective is that the user can use modular design to intervene throughout the process of creating, producing and using their product, which also implies the design and manufacture of their desired modules.
# Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering
## 3 Model
### SVD++  
SVD++ is the most widely used latent factor model for MF because of its superior prediction accuracy. In SVD++, two vectors \(p_{u}\) and \(q_{i}\), which respectively contain the latent features of the users and items, are learned by minimizing the regularized mean squared error on the set of known ratings:  
\[min\sum_{u,i\in R}\left(r_{u,i}-\mu-b_{u}-b_{i}-q_{i}^{T}\left[p_{u}+|I_{u}|^{ -\frac{1}{2}}\sum_{j\in I_{u}}y_{j}\right)\right)^{2}+\ \lambda(\parallel q_{i}\parallel^{2}+\parallel p_{u}\parallel^{2}+\sum_{j \in I_{u}}\parallel y_{j}\parallel^{2}+b_{u}^{2}+b_{i}^{2}) \tag{2}\]  
where \(R\) contains user-item \((u,i)\) pairs for which rating \(r_{u,i}\) is known; \(\mu\) is the mean rating, \(I_{u}\) is user \(u\)'s item vector, \(y_{j}\) is the \(j\)th rating in \(I\), and \(\lambda\) is the regularization constant. In contrast to the regular MF model defined in Equation (1), SVD++ takes the perspective of implicit feedback.  
Figure 1: Rating matrix approximation with latent features.  
Then, the predictions are made using the learned latent features:  
\[\widehat{r_{u,j}}=\mu+b_{u}+b_{i}+q_{i}^{T}\left(p_{u}+|I_{u}|^{-\frac{1}{2}}\sum \limits_{j\in I_{u}}y_{j}\right), \tag{3}\]  
where \(|I_{u}|^{-\frac{1}{2}}\sum\limits_{j\in I_{u}}y_{j}\) captures the user's implicit feedback.
## 3 Results
### Simplified Model  
Although the UVE-PLSR model established above has screened 117 effective characteristic wavelengths and achieved ideal prediction performance, there are still many shortcomings for wavelength-limited spectrometers and actual production requirements for fast and real-time detection. Therefore, this study proposes a multi-level wavelength optimization strategy. On the basis of UVE preliminary screening, SPA is further introduced to realize the secondary screening of spectral variables, so as to minimize data redundancy and model complexity. At the same time, a simplified universal model is established by combining a simpler and more efficient version of multiple linear regression (MLR) to enhance the applicability of the model under different production demands.
## 1 Introduction
Similar to the ZA-LMS and RZA-LMS algorithms, the zero attracting MCC (ZA-MCC) and reweighting ZA-MCC (RZA-MCC) algorithms [33] were obtained within the zero attracting framework. Then, the normalized MCC (NMCC) algorithm was also presented [34; 35] by referring to the normalized least mean square (NLMS) algorithm. Recently, [PERSON] proposed a correntropy-induced metric (CIM) penalized MCC algorithm in [33], and [PERSON] presented a soft parameter function (SPF) constrained MCC algorithm [34]. The CIM and SPF are also one kind of \(l_{0}\)-norm approximation to form sparse MCC algorithms, and the SPF-MCC is given in the appendix. As for these improved MCC algorithms, the \(l_{0}\)-norm, CIM and SPF penalties are incorporated into the MCC's cost function to devise desired zero attractors. In the ZA-MCC algorithm, the zero attractor gives uniform penalty on all the channel coefficients, while the \(l_{0}\)-norm approximation MCC algorithms will increase the complexity. In this paper, a group-constrained maximum correntropy criterion (GC-MCC) algorithm based on the CS concept and zero attracting (ZA) techniques is proposed in order to fully exploit the sparseness characteristics of the multi-path channels. The GC-MCC algorithm is derived by incorporating a non-uniform norm into the MCC's cost function and the non-uniform norm is split into two groups according to the mean of the magnitudes of the channel coefficients. For the large channel coefficients group, the \(l_{0}\)-norm penalty is used, while the \(l_{1}\)-norm penalty is implemented for the small channel group. Then, a reweighting technique is utilized in the GC-MCC algorithm to develop the reweighted GC-MCC (RGC-MCC) algorithm. The performance of the GC- and RGC-MCC algorithms is evaluated and discussed for estimating mix-noised sparse channels. The GC- and RGC-MCC algorithms achieve superior performance in both steady-error and convergence for sparse channel estimations with different sparsity levels. Simulation results show that the GC- and RGC-MCC algorithms can effectively enhance the sparse channel estimation by using the proposed group constraints and can provide smaller steady-state errors and faster convergence for application in a mixed noise environment. The structure of this paper is summarized as follows. In Section 2, the MCC and its related sparse algorithms are briefly reviewed. Section 3 introduces the GC-MCC and RGC-MCC algorithms, and mathematically derives them. In Section 4, simulations that show the effectiveness of the proposed GC-MCC and RCG-MCC algorithms are presented. Finally, our work is summarized in Section 5.
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 5 Simulation Results
### The Simulation Results of the Gaussian Approximation of the BP Algorithm and the TDMP Algorithm  
Gaussian approximation algorithm is a simplified density evolution algorithm. Next, the correctness of the TDMP algorithm was verified from the point of view of Gauss approximation. Through Gaussian approximation analysis, the performance curves of the BP algorithm and the TDMP algorithm at six code rates are shown in Figure 7a,b, respectively. It can be seen from Figure 7 that the BER curve of the TDMP algorithm converges faster. When the code rate is 2/3B and the SNR is 2.0 (dB), the relationship between the BER and the number of iterations of the two algorithms is shown in Figure 8. When the expected BER reaches to \(10^{-5}\) at the code rate 2/3B under different SNRs, iterations needed for the BP algorithm and the TDMP algorithm are shown in Figure 9.  
The analysis of Figures 7-9 is similar as that of Figures 2-4 in Section 5.1. Thus, the performance of TDMP algorithm for multi-dimensional density evolution and one-dimensional Gaussian approximation process was similar.  
Figure 6: The relationship of BER and iterations: (**a**) the 802.16 standard; (**b**) the DTMB standard.  
Figure 7: BER performance comparison at six code rates of Gaussian Approximation: (**a**) BP algorithm; (**b**) TDMP algorithm.  
Based on Gaussian approximation theory, the BP and TDMP algorithms of LDPC codes with 1/2 code rate under two standards are simulated. Figure 10 shows the number of iterations required for the two algorithms to achieve the desired BER at different SNRs, where the expected BER is \(10^{-5}\). When the code rate is 1/2 and the SNR is 2.0 dB, the relationship between the BER and the number of iterations is as shown in Figure 11. The standard adopted and the corresponding degree distribution function are the same as Section 5.1.  
Figure 8: The relationship between BER and iterations.  
Figure 10: The relationship of SNR and iterations: (**a**) the 802.16 standard; (**b**) the DTMB standard.  
Figure 9: Iterations needed for reaching to the expected BER under different SNRs.  
The analysis of Figures 10 and 11 was similar to that of Figures 5 and 6 in Section 5.1. Combined with the simulation results of Sections 5.1 and 5.2, we can get the following conclusions. For LDPC codes under different standards, we only need to know the degree distribution of LDPC codes under the current standard. Therefore, for other standard LDPC codes, the density evolution theory can be used to analyze according to the degree distribution function. In terms of LDPC code structure, the TDMP algorithm is applicable to structured layered LDPC codes [32; 33]. The currently known standard LDPC codes conform to this feature, so we can conclude that the density evolution and Gaussian approximation of the proposed TDMP algorithm are applicable to different standards.
## 5 The Simulation Results and Discussion
### Simulation Setup
#### 5.1.3 Intervals of the Optimization Problem Variables  
All the variables of inputs and outputs in this study need to be bounded according to the real robot's parameters for the optimization process. This optimization problem works with the distance (\(D\)), angle (\(\Delta\theta\)), and the wheel's angular velocities (\(\omega_{r},\omega_{l}\)). The interval of each variable is selected based on the robot parameters, the path, the environment, etc. So, based on the parameters of the DDR and the paths used in the experiments of this study,  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters (Notation)** & **Value** \\ \hline The mass of the robot (\(m\)) & 1.733 kg \\ The moment of inertia of the DDR (\(l\)) & 0.08 kg\(\cdot\)m\({}^{2}\) \\ The torque constant (\(K_{t}\)) & 0.001 Nm/A \\ The back emf constant (\(K_{e}\)) & 0.03 V/m/s \\ The electrical resistance (\(R_{a}\)) & 0.7 \(\Omega\) \\ The electrical inductance (\(L_{a}\)) & 0.01 H \\ The damping coefficient (\(B\)) & 0.004 N-m\(\cdot\)s \\ The moment of inertia of the motor (\(J\)) & 0.037 kg\(\cdot\)m\({}^{2}\cdot\)s\({}^{-2}\) \\ The gear ratio of the DC motor (\(N\)) & 30 \\ The distance between two drive wheels (\(2b\)) & 0.2 m \\ The drive wheel’s radius (\(r\)) & 0.0425 m \\ \hline \hline \end{tabular}
\end{table}
Table 6: The main parameters of the DDR used for simulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Paths**} & \multicolumn{5}{c}{**Waypoints (\(x\), \(y\))**} \\ \cline{2-5}  & **Starting Point** & **1 st Point** & **2 nd Point** & **3 rd Point** & **Goal** \\ \hline Zigzag path & (0, 0) & (2, 0) & (2, 2) & (4, 2) & (4, 4) \\ Square path & (0, 0) & (2, 0) & (2, 2) & (0, 2) & (0, 0) \\ Sharp turn path & (0, 0) & (2, 0) & (1, 2) & - & - \\ Straightforward path & (0, 0) & (2, 1.5) & (4, 3) & - & - \\ Diamond-shape path & (0, 0) & (2, -1) & (4, 0) & (2, 1) & (0, 0) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters of the paths used for simulation.  
the intervals of the input and output variables used for optimization problem are selected and listed in Table 8.  
Therefore, the optimization parameters of input1 MFs (from base1 to base11) will be in the range [from 0 to 10] m. The optimization parameters of input2 MFs (from base12 to base22) will be in the range [from \(-\pi\) to \(\pi\)] rad. The optimization parameters of output1 MFs and output2 MFs (from base23 to base44) will be in the range [from 0 to 30] rad s\({}^{-1}\). In addition, for the optimization the FRs, the values of the genes represent the outputs (from Out_R1 to Out_R25) and (from Out_L1 to Out_L25) will be selected in the range [from 0.0 to 1.0]. The value of these genes including (from Out_R1 to Out_R25) and (from Out_L1 to Out_L25) will define the notation of each output (VSR/VSL, SR/SL, MBR/MBL, BR/BL, and VBR/VBL) as in Table 9.
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 3 Problem Formulation
### One-Hop Problem Formulation  
We define \(\alpha_{j}\) as a binary indicator variable to show whether location \(i\) is selected to deploy a donor (1 for selected, 0 otherwise). Similarly, a binary variable \(\alpha_{j}\) is defined to show node deployment in location \(j\) (1 for selected, 0 otherwise). \(Y_{ij}\in\{0,1\}\) is a backhaul service indicator when a donor deployed at \(i\) can provide the required backhaul service to a node placed at location \(j\). Similarly, \(C_{jk}\in\{0,1\}\) and \(C_{ik}\in\{0,1\}\) are access service indicators when nodes placed at \(j\) and donors placed at \(i\) may serve \(k\), respectively. The SNR is the basis for the indications of \(C_{ik}\), \(C_{jk}\). As the received power \(P_{r}\) is a function of distance, the required access/backhaul service is ensured when nodes/donors are positioned appropriately and \(P_{r}\) exceeds the threshold \(SNR_{0}\).
## 4 Starlink as Case Study
### Stage 2: Partitioning of Mass Budget in Accordance with ESA LCI Database  
Table 3 indicates that the mass of a Starlink V1 satellite is 260 kg. However, it is necessary to predict the propellant mass and dry mass in terms of a detailed subsystem/ component-level mass budget for the V1 satellite, consistent with the ESA LCI database. The average mass budget for low Earth orbit (LEO) satellites is derived from [25] and is discussed in detail in Section 2.2.1. The European Space Agency (ESA) Life Cycle Inventory (LCI) database is examined extensively in Section 2.3. For an accurate life cycle assessment  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Version** & **No. of Satellites** & **Mass (kg)** & **Reference** \\ \hline V0.9 (Launched) & 60 & 217 & [35] \\ V1 (Launched) & 1665 & 260 & [60] \\ V1.5 (Launched) & 2139 & 306 & [61] \\ V2 (Planned launch) & 29,988 & 2000 & [62] \\ Total & 33,852 & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Starlink versions.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & **PL** & **PF** & **PD** & **PM** & **RB** & **RF** & **RD** & **RM** & **UI** & **Total** \\ \hline LEO & 10136 & 5747 & 115 & 227 & 953 & 3274 & 40 & 579 & 83 & 21154 \\ GEO & 800 & 3 & 3 & 9 & 64 & 0 & 0 & 0 & 33 & 912 \\ EGO & 521 & 1 & 1 & 49 & 203 & 82 & 3 & 4 & 1880 & 2753 \\ GTO & 51 & 28 & 1 & 10 & 235 & 202 & 12 & 51 & 626 & 1216 \\ NSO & 282 & 0 & 0 & 1 & 96 & 0 & 0 & 2 & 38 & 419 \\ MEO & 77 & 0 & 4 & 49 & 25 & 42 & 1 & 4 & 415 & 617 \\ LMO & 83 & 138 & 5 & 46 & 246 & 590 & 22 & 215 & 965 & 2300 \\ MGO & 68 & 65 & 1 & 3 & 177 & 1968 & 4 & 0 & 1178 & 3464 \\ HEO & 29 & 13 & 0 & 1 & 54 & 113 & 0 & 0 & 1101 & 1311 \\ Other & 45 & 0 & 0 & 5 & 5 & 0 & 0 & 0 & 97 & 152 \\ \hline Total & 12092 & 5995 & 130 & 400 & 2058 & 6271 & 82 & 855 & 6415 & 34298 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Starlink versions.  
Figure 5: Current number of orbiting objects per type and orbital regime [56].  
(LCA), it is crucial to partition the mass budget in alignment with the categories and structure outlined in the ESA LCI database. Table 4 represents the average dry mass distribution of subsystems and its relationship with the ESA's LCI data. The ESA's LCI database is more detailed for propulsion and power systems. LCI is further divided into detailed components regarding power, harness, thruster, and tank. The detailed partitioning is only necessary for the power, harness, thruster, and tank propulsion, as the rest of the LCI database matches with the average mass budget, as we can see in Table 4 below. Detailed partitioning is performed with space systems engineering budget allocation techniques for power, harness, thruster, tank, and propulsion subsystem mass.
## 7 Protocol Comparisons
### Architecture Comparison
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Architecture** & \begin{tabular}{c} **Overlappin** \\ **g nodes** \\ \end{tabular} & \begin{tabular}{c} **Uses other routing** \\ **protocols** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **messages** \\ \end{tabular} & **New node cluster’s selection** & **Purpose** & \begin{tabular}{c} **Node Fault** \\ **Tolerance** \\ \end{tabular} & **Cluster Head selection** \\ \hline [PERSON] _et al._[35] & Yes & Just one at a time & 16 & Proximity & Routing & No & n/a \\ \hline CRBP [6] & Yes & No & 6 & nodeID & Routing & No & Lowest node ID \\ \hline [PERSON] _et al._[56] & No & Just one at a time & n/p & n/p & Security & Yes, but very weak & \begin{tabular}{c} Trusted node, but not explained \\ what happens if there are several \\ trusted nodes. \\ \end{tabular} \\ \hline KCLS [37] & No & No & \begin{tabular}{c} 13 + GPS \\ protocol \\ \end{tabular} & \begin{tabular}{c} Distance to the head node less than \\ k hops \\ \end{tabular} & \begin{tabular}{c} Location \\ Service \\ \end{tabular} & Yes & Mobility threshold \\ \hline Chunlung R. Lin [9] & No & Just one at a time & n/p & nodeID & Bandwidth & Yes & Lowest node ID \\ \hline LEACH [34] & No & No & n/p & received signal strength & Energy & No & Random rotation \\ \hline BCDCP [38] & No & No & n/p & Location & Energy & No & Random \\ \hline CLACR [39] & No & No & 11 & Location & Routing & \begin{tabular}{c} Just for \\ location \\ servers \\ \end{tabular} & Closet to the cluster center & \\ \hline CBLARHM [40] & No & No & 17+ GPS & \begin{tabular}{c} Relative distance, velocity and \\ time \\ \end{tabular} & Routing & No & Node-Weight heuristic \\ \hline WCA [41] & No & Just one at a time & n/p & Degree difference and proximity & \begin{tabular}{c} General \\ purpose \\ \end{tabular} & No & Combined weight metric \\ \hline CLTC [42] & No & Just one at a time & 10+ GPS & Coverage area & \begin{tabular}{c} Topology \\ control \\ \end{tabular} & No & n/p \\ \hline Our proposal & No & \
\begin{tabular}{c} Yes and it could use \\ many simultaneously. \\ \end{tabular} & 14 & Proximity + capacity parameter &
\begin{tabular}{c} Create parallel \\ networks \\ \end{tabular} & Yes & Promotion Parameter \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cluster architectures comparison. WCA uses a combined weight metric based on the ideal node degree, transmission power, mobility and the battery power of the nodes. It is a good idea and seems very useful, but they propose this metric for a cluster-based general purpose algorithm, and maybe these parameters are good for a specific case, but not for all cases, as some parameters could be missing such as the node's position or the node's load. Our metric does not take mobility into account since the entire cluster could be moving and avoids continually selecting the motionless nodes. On the other hand, it does take into account the more stable node in the cluster and its energy, thus making the system very simple and practical.
# Deep Q-Network Approach for Train Timetable Rescheduling Based on Alternative Graph
## 1 Introduction
### Preliminary TTR Based on Alternative Graph
If the length of the arcs originating from \(0\) is set as the first entry time of the train and the length of the arcs terminating at \(n\) is the negative value of the planned exit time of the train, \(l^{S}(0,n)\) corresponds to a maximum consecutive delay propagated by the initial delay. We refer to [7] for a more-detailed description of the alternative graph model. Therefore, our problem can be formally stated as the problem of finding a selection that minimizes \(l^{S}(0,n)\) in \(\mathcal{G}(S)\), i.e., a revised train schedule, formulated as a mixed-integer linear programming (MILP) problem as proposed by [19] as follows:  
**MILP:**  
\[\min \qquad l^{S}(0,n)\] (1) s.t. \[t_{j}-t_{i}\geq b_{ij}, \text{for }(i,j)\in\mathcal{F} \tag{2}\] \[t_{y}-t_{x}+M(1-x_{xywz})\geq b_{xy}, \text{for }[(x,y),(w,z)]\in\mathcal{A}\] (3) \[t_{z}-t_{w}+M(x_{xywz})\geq b_{wz}, \text{for }[(x,y),(w,z)]\in\mathcal{A}\] (4) \[x\in\{0,1\}^{|A|} \tag{5}\]  
where:  
* \(x_{xywz}=1\) if alternative arc \((x,y)\) is selected and \(x_{xywz}=0\) otherwise, i.e., \((w,z)\) is selected;
* \(t_{i}\) is the entry time block \(B(i)\) of train \(T(i)\);
* \(M\) has a sufficiently large positive value. Based on the alternative graph model, we reformulated our problem as a sequential decision-making problem such that we determined the alternative arc to add selection \(S\) as trains progress along their routes. This sequential decision-making problem can be modeled as a Markov decision process (MDP) in which the state space, the action space, the transition function, and the reward are defined in the next section.
# A Two-Stage Anomaly Detection Method Based on User Preference Features and the Deep Fusion Model
## 3 Proposed Method
### Deep Fusion Detection Methods
The construction of the deep fusion models is illustrated in Figure 5. DAG depicts a logical flow of operations on the partitions in a resilient distributed dataset (RDD), which makes it possible for the multiple worker nodes to perform in parallel. The pseudocode of the proposed method is given in Algorithm 2:  
```
input: network traffic packages output: detection results
1:When packets arrived:
2: Packets_File_append(Packet)
3:EndWhen
4: Initialize Flow_File
5:For Packet in Packets_File:
6:If: (Packet_srcIP,Packet_destIP) in Flow_File:
7:Flow_File[Packet.srcIP,Packet.destIP]]append(Packet)
8:Else:
9:Flow_File[Packet.srcIP,Packet.destIP] = Packet
10:End
11:End
12:Send Flow_File to HDFS
13:Sc = sparkContext()
14:Workers: Sc.read(Flow_File)
15:Features_File = Extract_Features(Flow_File)
16:Detection_Model = DeepFusionClassifier
17:Detection_Model_fit[Training_File]
18:Prediction = Detection_Model.predict(Features_File)
19:For ele in Prediction:
20:If ele = 0:
21:Output: Normal Traffic
22:If ele == 1:
23:Output: Attack Traffic
24:End
```  
**Algorithm 2**Anomalous network detection methods based on deep fusion models  
Figure 5: Construction of deep fusion.