esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Development Based on Co-design  
This study involved eighteen (\(n=18\)) health professionals who work at the HU-UFMA, specifically with breastfeeding. Professionals who worked in the specialized breastfeeding ambulatory clinic for at least two years were included, and those who worked in different sectors of ambulatory care for mothers were excluded. The study was approved by the scientific committee and institutional review board of the HU-UFMA (number 5.770.812).  
A co-design approach [42] was used to develop the proposed chatbot, which consisted of listening to health professionals and using their requirements and insights in the design and improvement of the conversational flow. Also, since the participants interacted with the chatbot continuously in each new version launched, they produced texts (i.e., conversations simulating mothers) used to train DL-based NLP pipelines to build intent classification models. Figure 1 presents the timeline of the co-design procedure step by step, described as follows.  
1. Development: At this stage, the first version of _Lhia_ was developed and made available to be used by the participants;
2. Interaction and Partial Results/Refinement:this stage took place in four rounds, as explained in Table 1, the first three lasting seven days and the last one hundred twenty minutes during a face-to-face brainstorming workshop. During this interactive process, we performed several training sessions with different DL-based NLP pipelines to identify the one with the best accuracy result to be deployed in _Lhia_. Also, this iterative development process allowed us to improve the conversational flow based on participants' suggestions. The flow was improved in the following manners: content improved by adding figures and better explanations, adjustment of used vocabulary, and increase in the number of chatbot answers (i.e., utterances). During each round, we collected a number of fallback triggers--i.e., a fallback is triggered by a low confidence score (\(\leq\)0.4; the chatbot is not able to classify a user intent) on the user intent classification--the confidence scores on the user intent classification, the number of user interactions, the number of participants interacting with the chatbot and, in the last two rounds, the Net Promoter Score (NPS) [43] on a 3-answer Likert scale;
3. Production: finally, after the interactive process, _Lhia_ reached a version that could be put into production.  
Figure 1: Co-design procedure timeline.  
As we have collected the number of fallback triggers, we were able to calculate the fallback index, which is the percentage of messages that the chatbot was not able to understand. Fallback index is obtained using the Equation (1). The lower the percentage, the lower the chances of the chatbot not understanding the user's message.  
\[\mathit{Fallback\ index}=\frac{\mathit{number\ of\ fallback\ triggers}}{\mathit{total\ number\ of\ chatbot\ of\ chatbot\ interactions}} \tag{1}\]
###### Abstract  
An accurate prediction of freight volume at the sanitary facilities of seaports is a key factor to improve planning operations and resource allocation. This study proposes a hybrid approach to forecast container volume at the sanitary facilities of a seaport. The methodology consists of a three-step procedure, combining the strengths of linear and non-linear models and the capability of a clustering technique. First, a self-organizing map (SOM) is used to decompose the time series into smaller clusters easier to predict. Second, a seasonal autoregressive integrated moving averages (SARIMA) model is applied in each cluster in order to obtain predicted values and residuals of each cluster. These values are finally used as inputs of a support vector regression (SVR) model together with the historical data of the cluster. The final prediction result integrates the prediction results of each cluster. The experimental results showed that the proposed model provided accurate prediction results and outperforms the rest of the models tested. The proposed model can be used as an automatic decision-making tool by seaport management due to its capacity to plan resources in advance, avoiding congestion and time delays.  
maritime transport; container forecasting; support vector regression; self-organizing maps; machine learning; hybrid models +
Footnote †: journal: Applied  
_Article_
## 3 Performance Analysis
### Distribution of the SINR  
According to (1), we have  
\[\mathbf{H}_{b}\mathbf{w}_{b}=\sqrt{\frac{K_{b}}{1+K_{b}}}\mathbf{H}_{b}^{o} \mathbf{w}_{b}+\sqrt{\frac{1}{1+K_{b}}}\mathbf{H}_{b}^{r}\mathbf{w}_{b}. \tag{12}\]  
The distribution of \(|\mathbf{H}_{b}\mathbf{w}_{b}|\) has been analyzed in [22] for a general \(\mathbf{w}_{b}\). It has been shown that \(\mathbf{H}_{b}^{o}\mathbf{w}_{b}\) is deterministic and \(\mathbf{H}_{b}^{r}\mathbf{w}_{b}\) is a complex Gaussian random variable with zero mean and unit variance. Hence, \(|\mathbf{H}_{b}\mathbf{w}_{b}|\) follows a Rician distribution with the parameters [22]:  
\[\tilde{K}_{b}=K_{b}|\mathbf{H}_{b}^{o}\mathbf{w}_{b}|^{2} \tag{13}\] \[\tilde{\Omega}_{b}=\frac{1+K_{b}|\mathbf{H}_{b}^{o}\mathbf{w}_{b }|^{2}}{1+K_{b}} \tag{14}\]  
The PDF of the Rician distribution involves the modified Bessel function of the first kind, which is difficult to derive. However, the Rician distribution can be closely approximated by Nakagami distribution [38], with the parameters \(m_{b}=(K_{b}+1)^{2}/(2K_{b}+1)\) and \(\omega_{b}=\tilde{\Omega}_{b}\). Hence, \(\tilde{g}_{b}|\mathbf{H}_{b}\mathbf{w}_{b}|^{2}\) can be approximated by a gamma distribution with the PDF written as  
\[p_{\tilde{g}_{b}|\mathbf{H}_{b}\mathbf{w}_{b}|^{2}}(x)=\frac{\beta_{b}^{\alpha _{b}}}{\Gamma(\alpha_{b})}x^{\alpha_{b}-1}e^{-\beta_{b}x} \tag{15}\]  
where \(\alpha_{b}=m_{b}\), \(\beta_{b}=\frac{m_{b}}{\tilde{g}_{b}\omega_{b}}\) and \(\Gamma(\cdot)\) is the gamma function.  
Furthermore, we have  
\[\mathbf{H}_{b}\mathbf{w}_{\text{AN}}=\sqrt{\frac{K_{b}}{1+K_{b}}}\mathbf{H}_{b} ^{o}\mathbf{w}_{\text{AN}}+\sqrt{\frac{1}{1+K_{b}}}\mathbf{H}_{b}^{r}\mathbf{ w}_{\text{AN}}. \tag{16}\]  
Similarly, we can obtain that \(\tilde{g}_{\text{AN}}|\mathbf{H}_{b}\mathbf{w}_{\text{AN}}|^{2}\) can also be approximated by a gamma distribution, with the parameters \(\alpha_{b,\text{AN}}=m_{b,\text{AN}}\) and \(\beta_{b,\text{AN}}=\frac{m_{b,\text{AN}}}{\tilde{g}_{\text{AN}}\omega_{b,\text {AN}}}\) where  
Therefore, the PDF of \(\mathrm{SINR}_{e}\), denoted as \(p_{\mathrm{SINR}_{e}}(\gamma)\), can be expressed by replacing \(\alpha_{b}\), \(\beta_{b}\), \(\alpha_{b,\mathrm{AN}}\), and \(\beta_{b,\mathrm{AN}}\) with \(\alpha_{e}\), \(\beta_{e}\), \(\alpha_{e,\mathrm{AN}}\), and \(\beta_{e,\mathrm{AN}}\) in (19).
## 4 The Simulation Results
accurate trajectory tracking control with finite time convergence \(t_{1}\approx 0.1\) s. In particular, the error convergence of PDFS can achieve a small stable value but approximates the mean one as greater than zero. The proposed controller with the disturbance estimator provides remarkable error convergence to zero. The RMSE calculations are processed when the system becomes stable \((t>0.2\) s) for all controllers. The RMSE of the PDFS and PDAFS controller in Tables 6 and 7 is lower than the values of the others. As can be seen, the increasing speed of joints causes the amplitude of the tracking errors to the peaks. This effort is a reason for the error increase caused by the higher maximum speed and acceleration in the parabolic trajectory compared to the helix trajectory. Furthermore, the corresponding maximum errors of the proposed controller are only \([0.0181,\ 0.02,\ 0.0162]\) and \([0.0044,\ 0.0046,\ 0.0054]\) in degrees. Indeed, the PDAFS can achieve robust motion characteristics for the Delta robot in the simulation conditions. The control signal of the controllers is presented in Figure 8. In general, the control signals provided by the five controllers have the approximated transitions, whereas the controllers based on the synergetic method demonstrated the better strength signal of the control outline. The control signals of PDSM show the drawback with the chattering noises when they transit the errors on the manifold surface. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Joint 1 (Degrees)** & **Joint 2 (Degrees)** & **Joint 3 (Degrees)** \\ \hline PD & 0.0695 & 0.0800 & 0.1104 \\ PDFF & 0.0559 & 0.0689 & 0.0985 \\ PDSM & 0.0394 & 0.0544 & 0.0841 \\ PDFS & 0.0062 & 0.0066 & 0.0078 \\ PDAFS & 0.0011 & 0.0018 & 0.0021 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RMSE Comparison among the controllers and PDAFS in the Helix simulation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Joint 1 (Degrees)** & **Joint 2 (Degrees)** & **Joint 3 (Degrees)** \\ \hline PD & 0.0675 & 0.1912 & 0.3012 \\ PDFF & 0.0686 & 0.1843 & 0.2958 \\ PDSM & 0.0426 & 0.1511 & 0.2644 \\ PDFS & 0.0157 & 0.0182 & 0.0189 \\ PDAFS & 0.0089 & 0.0093 & 0.0080 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: RMSE Comparison among the controllers and PDAFS in the Parabolic simulation. Figure 7: Tracking error comparison among the controllers and PDAFS in the helix and parabolic simulation. In Figure 9, it is noteworthy that the behavior of external torque estimation is affected by the added external disturbance torques. The effective action of RBF approximation leads the torque compensation to converge to the curves generated by the disturbance assumptions and the remaining torques of the unknown DC motor parameters. The uncertainty disturbance estimation in the control signals leads to the highest performance among the compared controllers in both study cases.
## 3 Model Formulation
### Declarative Model Description
**Constraints:**:
* The tasks must be performed according to the sequence specified by the \(E_{j}\), project activity network, and within the time limit specified by \(SE_{j}\) and \(HE_{j}\): \[y_{j,\alpha}=0,\text{ if }Z_{\alpha}\
otin VE_{j},\ \forall E_{j}\in E\] (1) \[y_{j,\alpha}=y_{j,\beta}+\tau_{j,\beta}(G^{n},X^{n}),\text{ if }\big{(}Z_{ \alpha},Z_{\beta}\big{)}\in AE_{j},\ \forall E_{j}\in E\] (2) \[y_{j,\alpha}\geq SE_{j},\text{ if }Z_{\alpha}\in VE_{j},\ \forall E_{j}\in E\] (3) \[y_{j,\alpha}+\tau_{j,\alpha}(G^{n},X^{n})\leq HE_{j},\text{ if }Z_{\alpha}\in VE_{j},\ \forall E_{j}\in E\] (4)
* The assignment of a worker \(P_{k}\) to a \(Z_{i}\) task determines when it starts: \[\Big{(}x^{n}_{k,i,j}=1\Big{)}\Rightarrow\big{(}y_{j,i}=n\big{)}\,,\ \forall P_{k}\in P,\ \forall Z_{i}\in Z,\ \forall E_{j}\in E,\ n=0\ldots\max_{j=1\ldots J}\big{\{} HE_{j}\big{\}}\] (5) \[\big{(}y_{j,i}=n\big{)}\Rightarrow\Big{(}\sum_{k=1}^{K}x^{n}_{k,i,j}=1 \Big{)}\,,\ \forall Z_{i}\in Z,\ \forall E_{j}\in E,\ n=0\ldots\max_{j=1\ldots J}\big{\{} HE_{j}\big{\}}\] (6)
* At a time \(n\), each employee \(P_{k}\) is assigned to only one task: \[\sum_{i=1}^{Q}\sum_{j=1}^{J}x^{n}_{k,i,j}\leq 1,\ \forall P_{k}\in P,\ n=0 \ldots\max_{j=1\ldots J}\big{\{} HE_{j}\big{\}}\] (7)
* Each task \(Z_{i}\) and the project \(E_{j}\) must be completed: \[\sum_{m=0}^{HE_{j}}\sum_{k=1}^{K}x^{n}_{k,i,j}=1,\text{ if }\forall Z_{i}\in VE_{j},\ \forall E_{j}\in E\] (8)* The structure of competences changes over time (the effect of forgetting and learning): \[g_{k,i}^{n}=g_{k,i}^{n-1}+\varphi(G^{n},X^{n}),\ \forall P_{k}\in P,\ \forall Z_{i}\in Z,\ n=1 \ldots\max_{j=1\ldots J}\bigl{\{}HE_{j}\bigr{\}}\] (9)
* The level of the competence structure after the completion of the project portfolio cannot be less than the set value \(SG^{*}\) : \[y_{j,\alpha}=0,\text{ if }Z_{\alpha}\
otin VE_{j},\ \forall E_{j}\in E\]0 (10) \[y_{j,\alpha}=0,\text{ if }Z_{\alpha}\
otin VE_{j},\ \forall E_{j}\in E\]1 (11)  
Among the introduced constraints can be distinguished restrictions (1)-(8) describing the relationship between the assignment of employees to tasks and their schedule, as well as restrictions (9)-(11) describing the impact of task assignment on the structure of competences and the impact of the competence structure on the duration of tasks.
## 2 Theory and Methods
\begin{split}\mathbf{A}_{i}=\big{[}&\mathbf{I}_{i}^{1} \quad\mathbf{I}_{i}^{2}\quad\cdots\quad\mathbf{I}_{i}^{N}\big{]}\\ =&\big{[}\triangle I_{i}^{1}(1,1)\quad\cdots\quad \triangle I_{i}^{1}(N_{x},N_{y})\quad\triangle I_{i}^{2}(1,1)\quad\cdots\\ &\triangle I_{i}^{2}(N_{x},N_{y})\quad\cdots\quad\triangle I_{i}^ {N}(1,1)\quad\cdots\triangle I_{i}^{N}(N_{x},N_{y})\big{]}.\end{split}  \tag{5}\]  
\(M\) groups' differential speckle patterns \(\{\triangle I_{i}^{k}(x,y)\}_{i=1}^{M}(k=1,2,\cdots,N)\) can construct an \(M\times P\) matrix \(\mathbf{R}\) as:  
\[ \begin{split}\mathbf{R}=\begin{bmatrix}\mathbf{A}_{1}\\ \mathbf{A}_{2}\\ \vdots\\ \mathbf{A}_{M}\end{bmatrix}\\ =\begin{bmatrix}\triangle I_{1}^{1}(1,1)&\triangle I_{1}^{1}(1,2) \quad\cdots\quad\triangle I_{N}^{N}(N_{x},N_{y})\\ \triangle I_{2}^{1}(1,1)&\triangle I_{2}^{1}(1,2)\quad\cdots\quad\triangle I_{2 }^{N}(N_{x},N_{y})\\ \vdots&\vdots&\ddots&\vdots\\ \triangle I_{M}^{1}(1,1)&\triangle I_{M}^{1}(1,2)\quad\cdots\quad\triangle I_{M} ^{N}(N_{x},N_{y})\end{bmatrix}.\end{split}  \tag{6}\]  
Equation (3) can be expressed in a matrix form:  
\[ \begin{bmatrix}\triangle B_{1}\\ \triangle B_{2}\\ \vdots\\ \triangle B_{M}\end{bmatrix} =\eta \begin{bmatrix}\triangle I_{1}^{1}(1,1)& \triangle I_{1}^{1}(1,2)&\cdots\quad\triangle I_{N}^{N}(N_{x},N_{y})\\ \triangle I_{2}^{1}(1,1)&\triangle I_{2}^{1}(1,2)\quad\cdots\quad\triangle I_{2 }^{N}(N_{x},N_{y})\\ \vdots&\vdots&\ddots&\vdots\\ \triangle I_{M}^{1}(1,1)&\triangle I_{M}^{1}(1,2)\quad\cdots\quad\triangle I_{M} ^{N}(N_{x},N_{y})\end{bmatrix}\begin{bmatrix}T(1,1,1)\\ T(1,2,1)\\ \vdots\\ T(N_{x},N_{y},N)\end{bmatrix} , \tag{7}\]  
where \(\mathbf{T}_{N}=\big{[}T(1,1,1)\quad T(1,2,1)\quad\cdots\quad T(N_{x},N_{y},N) \big{]}^{T}\) is a \(P\times 1\) column vector representing the object's transmission coefficient \(\{T(x,y,k)\}_{k=1}^{N}\). In reconstructing the images part, to reduce the number of measurements, the CS algorithm is used to reconstruct \(\{T(x,y,k)\}_{k=1}^{N}\). Here, we use the TVAL3 algorithm [32], which has the advantage of reconstructing high-quality images with fewer measurements. Therefore, the object's \(N\) images \(\{\widehat{T}(x,y,k)\}_{k=1}^{N}\) can be reconstructed by:  
\[\min\sum_{j}\|D_{j}\mathbf{T}_{N}\|_{1}+\frac{\mu}{2}\|\mathbf{B}-\mathbf{T} \mathbf{T}_{N}\|_{2}^{2}, \tag{8}\]  
where \(\mathbf{B}\) is a \(M\times 1\) column vector consisting of the \(M\) differential bucket detector signals \(\{\triangle B_{i}\}_{i=1}^{M}\). \(D_{j}\mathbf{T}_{N}\) denotes the discrete gradient of \(\mathbf{T}_{N}\) at element \(j(j=1,2,\cdots,P)\), \(\sum_{j}\|D_{j}\mathbf{T}_{N}\|\) is the discrete total variation of \(\mathbf{T}_{N}\), \(\|\cdot\|_{1}\) and \(\|\cdot\|_{2}\) stand for the \(l_{1}\) norm and \(l_{2}\) norm, respectively, \(\mu\) is a coefficient used to balance data fidelity
###### Abstract  
Effective and timely collision avoidance decision support is essential for super-large vessels navigating in port waters. To guarantee the navigational safety of super-large vessels, this work proposes a collision avoidance decision support method based on the curve increment strategy with adaptive particle swarm optimization (CIPSO). Firstly, the objective function is constructed based on the multi-objective optimization method. Here, a fuzzy comprehensive evaluation (FCE)-based vessel collision hazard model and vessel speed-varying energy-loss model integrating the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) are involved. Furthermore, in response to the limitations of the PSO algorithm, which is prone to falling into local optima in the later stages of iteration, a curve increment strategy is incorporated. To improve the performance of the global optimization, it is optimized using a local followed by global search method. The iterative evolution of CIPSO is used to obtain the optimal decision value in the set domain of feasible solutions. Finally, the effectiveness and feasibility of the proposed method are verified by the numerical simulation and large vessel maneuvering simulator, which can provide collision avoidance decision support for ship pilots.  
super-large vessel; speed-varying collision avoidance; collision avoidance decision support; CIPSO
# Risk Assessment and Source Apportionment of Heavy Metals in Soils from Handan City
## 2 Materials and Methods
### Evaluation Approach of Heavy Metals
#### 2.3.3 Health Risk Evaluation Approach  
The caluclation of the health risk is as follows:  
\[ADD_{ing}=C\times\frac{IngR\times EF\times ED}{EBW\times AT}\times 10^{-6} \tag{7}\]  
\[ADD_{\text{dermal}}=C\times\frac{SA\times AF\times ABS\times EF\times ED}{ EBW\times AT}\times 10^{-6} \tag{8}\]  
\[ADD_{inh}=C\times\frac{InhtR\times EF\times ED}{PEF\times EBW\times AT} \tag{9}\]  
\[HI=\sum HQ_{i}=\sum\frac{ADD_{i}}{RFD_{i}} \tag{10}\]  
\[CR=\sum ADD_{i}\times SF_{i} \tag{11}\]  
where \(ADD_{ing}\), \(ADD_{dermal}\), and \(ADD_{inh}\) are the mean daily exposure dose to ingestion, dermal and inhalation absorption from soil heavy metals (mg/kg/d) [8]. \(C\) stands for the content of metal in soil (mg/kg). A detailed description of the other parameters can be found in Tables S2 and S3. \(HQ_{i}\) represents the non-carcinogenic risk of \(i\)th heavy metal, while \(HI\) stands for the total non-carcinogenic risk [39]. When \(HI\) or \(HQ<1\), the risk is small or negligible. When \(HI\) or \(HQ>1\), the metal may have a non-carcinogenic risk. \(CR\) represents carcinogenic risk. Humans suffer unacceptable carcinogenic risk if the risk exceeds 1 \(\times\) 10\({}^{-4}\)[34]. However, there will be no significant health impact if the risk is lower than 1 \(\times\) 10\({}^{-6}\). It is considered that there is an acceptable carcinogenic risk if the risk is between 1 \(\times\) 10\({}^{-6}\) and 1 \(\times\) 10\({}^{-4}\).
## 3 Results
### Material Characterization
#### 3.1.3 Surface Functional Groups  
The qualitative composition of surface functional groups was not significantly altered by the KOH activation. The raw and activated hydrochar samples showed similar peaks in the FTIR-PAS analyses (Figure 2). The spectra in the region between 1700 and 1300 cm\({}^{-1}\) and the bands at 3800 cm\({}^{-1}\) were assigned to the residual water content in the hydrochar samples. The bands in the range of 2930-2850 cm\({}^{-1}\) corresponded to aliphatic C-H stretching vibrations [24; 25; 26]. The bands at 1600 and 1446 cm\({}^{-1}\) referred to aromatic C=C stretching vibrations [26; 27] and aliphatic CH\({}_{2}\) scissoring vibrations [28; 29], respectively. A modest vibration shift was observed from the bands in the range of 1446-1400 cm\({}^{-1}\) of the activated hydrochar samples. This referred to the deprotonation of the OH at the surface of hydrochar. Te bands in the 1110-1010 cm\({}^{-1}\) region were derived from O stretching vibrations [25; 27; 28]. The bands at 780 cm\({}^{-1}\) was derived from out-of-plane bending vibrations of aromatic C-H bonds [24; 26].  
Figure 1: Zeta potential of the hydrochar and _Escherichia coli_ strain over a function of pH. The lines represent the mean zeta potential value and error bars indicate the standard deviation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **C** & **H** & **N** & **O** & **Ca** & **Mg** & **Al** & **K** & **Fe** & **P** \\ \hline Raw & 28.6 & 3.6 & 2.0 & 22.3 & 5.1 & 0.9 & 2.6 & 0.5 & 5.0 & 4.5 \\ Activated & 29.4 & 3.7 & 1.9 & 21.1 & 5.8 & 1.0 & 2.0 & 0.9 & 5.5 & 2.6 \\ \hline \end{tabular}
\end{table}
Table 1: Elemental composition (%) of hydrochar variants used in this study.
## 5 Discussion  
The current systematic literature review has highlighted the information security behavior, its factors, and its theoretical implications towards information security policy compliance. The review focused on determining the behavior transformation from non-compliance behavior to compliance behavior presented in Figure 5. The transformation process has never been highlighted in the ISPC literature. For this purpose, the researchers reviewed the literature from 2010 to 2020 in two dimensions: (1) studies measuring compliance behavior; (2) studies measuring noncompliance behavior. The literature was reviewed and categorized according to the dimension of the studies. Seven categories were identified as factors influencing compliance behaviors. Three categories were highlighted for the factors influencing noncompliance given in Appendix A Tables A1, A2 and A5. Figure 5 demonstrates the activities and events extracted from a detailed literature review of ISB with ISC. The process model has two lanes: (1) employees' noncompliance and (2) employees' noncompliance to compliance. The combination and transformation of activities have been discussed in detail in the following paragraphs. **RQ1**: What are the behavioral factors concluded in studies as a significant determinant of information security policy compliance? As seen in Section 4.1 of the Results section, there are seven major categories of significant behavioral factors of compliance behavior that have been classified from the last decade of the literature. Appendix A Table A5 lists all of the significant determinants of compliance behavior. The literature review results indicated that national culture has a variety of effects on employee enforcement behavior. Multiple studies have shown that national culture can affect ISC in organizations [43, 99]. Due to the studies' related concepts, we only included one study [44] on national culture, which significantly demonstrated that national culture is a powerful determinant of compliance behavior. Intrinsic/extrinsic motivations were established as the second influencing factor in the enforcement behavior in the literature. Motivation is an essential element in human behavior. The ISC literature discusses a variety of motivations as influential factors in improving compliance behavior. In total, 18 studies (i.e., motivation) were taken from the previous
## 2 Communication Network Infrastructure
### Lagrange Subnet Routers (LSR)
#### 2.2.2 Auto-Localization  
Please note that, in addition to the LSR design, the rest of the swarm involves continuous orbit control and correction. For this purpose, the methodology of navigability through the analysis of local gravity maps, as proposed in [4], is suggested.  
This methodology is based on the selection and analysis of gravity maps to support autonomous navigation. By converting these maps into grayscale images and performing statistical analyses based on frequency distribution (e.g., histograms, gradient values), characteristics related to gravitational anomalies in a specific area can be obtained. The authors in [4] note that larger gravity anomalies or differences within a zone enhance navigability. Consequently, this inertial navigation method is ideal for LSR satellites, given the significant difference in gravity between a Lagrange point and its surroundings. Thus, the navigation system can process the optimal route and adjust the course of the nanosatellites to maintain their positions at the Lagrange points [2].  
Concerning the navigation payload, the LSR is equipped with sensors like those used in submarines [5]. These sensors collect data about gravity anomalies in their surroundings and correct their inertial navigation system (INS) without depending on external feedback.