esa-sceva/satcom-chunk-collection huggingface dataset:
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 2 Locata Core Technologies
### LocataLite  
A LocataLite is a transceiver device that is deployed in a Locata network that replicates a GNSS constellation's functions--but locally and on the ground. It currently transmits Code Division Multiple Access (CDMA) signals on two frequencies within the 2.4 GHz Industrial Scientific Medical (ISM) band (approximately 2.41428 GHz and 2.46543 GHz, referred to as S1 and S6 respectively), and from two spatially-separated antennas, which produce four ranging signals. The use of signals in the 2.4 GHz ISM band allows for (nearly) worldwide deployment without the need for RF signal transmission licences, and it does not impinge on the GNSS L-band signals [26]. For centimetre-level accuracy positioning, these signals must exceed the Locata Signal Strength (LSS) threshold of 5 [16; 27]. To address the multipath signal fading effects, the Locata hardware designs employ three types of signal diversity implementation: spatial, polarisation and frequency diversity [28]. Figure 2 shows a typical antenna configuration for a LocataLite, with one receiving (middle) and two transmitter antennas, which are separated vertically. The transmitter antennas are angled to point into the coverage area, and the receive antenna pointing at the master LocataLite to ensure time synchronisation [20].  
The Locata signals are closely related to those of the Global Positioning System (GPS), making integration with a GPS receiver easier, at least in theory. The receiver and the transmitter chipset share the same clock, which is a cheap temperature-compensated crystal oscillator (TCXO), by which the Locata signals are digitally generated and can be operated in a Pulse Amplitude Modulation (PAM) with different duty cycles, power output, and Pseudo-Random Noise (PRN) type codes. PAM is commonly used with pseudolite signals (instead of a continuous transmission, as for GNSS) to reduce interference and increase the working range (to overcome the \"near-far\" problem). \"Duty cycle\" refers to the percentage of time spent transmitting a signal when in PAM. In principle, LocataLites can be designed to broadcast at a range of microwave frequencies, modulated with digital codes of any design, at any power deemed to be appropriate for a specific application.
# Spectrum and Energy Efficiency of Uplink Massive MIMO System with D2D Underlay
## 3 Spectrum Efficiency Analysis
### Power Management for CUEs  
With the increase of \(P_{\mathrm{c}}\), the SE of the CN can be improved, while the SE of the D2D link is degraded, and vice visa. So, it is interesting to strike a balance between the CN and the D2D link through the power management for the CUEs. The spectrum performance of the D2D link can be improved effectively by decreasing \(P_{\mathrm{c}}\) especially in the high signal-to-noise ratio (SNR) regime. However, the power management of \(P_{\mathrm{c}}\) has limited influence on the CN.  
**Proposition 3**.: _When \(P_{\mathrm{c}}\rightarrow\infty\), \(\mathbb{P}_{ck}^{\mathrm{out}}\) approaches the lower bound \(\hat{\mathbb{P}}_{ck}^{\mathrm{out}}=\Gamma\left(\bar{a}_{ck},\frac{N\beta_{ck }}{\gamma_{ck}\bar{b}_{ck}}\right)/\Gamma(a_{ck})\), while \(\mathcal{R}_{ck}\) approaches the upper bound \(\hat{\mathcal{R}}_{ck}\) given as_  
\[\hat{\mathcal{R}}_{ck}=\frac{\mathcal{J}\left(\hat{a}_{ck},\frac{\hat{b}_{ck }}{\bar{b}_{ck}N}\right)}{\Gamma(\bar{a}_{ck})\ln(2)}-\frac{\psi\left(\hat{a}_ {ck}\right)}{\ln(2)}+\log_{2}\left(\frac{\beta_{ck}N}{\hat{b}_{ck}}\right), \tag{15}\]  
_where_  
\[\hat{a}_{ck}=\frac{\left(\sum\limits_{j\
eq k}^{K}\beta_{cj}\right)^{2}}{\sum \limits_{j\
eq k}^{K}\beta_{cj}^{2}},\ \hat{b}_{ck}=\frac{\sum\
olimits_{j\
eq k}^{K}\beta_{cj}^{2}}{\sum \
olimits_{j\
eq k}^{K}\beta_{cj}^{j}},\]  
_and \(\psi(x)=\frac{\mathrm{d}}{\mathrm{d}x}\ln\Gamma(x)\)._  
Proof.: Let \(\rho_{ck}=\sum\limits_{j\
eq k}^{K}\beta_{cj}\left|\frac{\mathbf{h}_{ck}^{H}}{ \left|\mathbf{h}_{ck}\right|}\mathbf{h}_{cj}\right|^{2}\). Similarly to \(l_{ck}\), \(\rho_{ck}\sim\mathrm{Gamma}(\hat{a}_{ck},\hat{b}_{ck})\). With the increase of \(P_{\mathrm{c}}\), \(\mathcal{R}_{ck}\) approaches the following upper bound  
\[\hat{\mathcal{R}}_{ck} =\mathbb{E}\left[\log_{2}\left(1+\frac{\beta_{ck}N}{\rho_{ck}} \right)\right]\] \[=\int_{0}^{\infty}\left[\log_{2}\left(\beta_{ck}N+\rho_{ck} \right)-\log_{2}\left(\rho_{ck}\right)\right]f(\rho_{ck})\,\mathrm{d}\rho_{ck}\] \[= \frac{l(\hat{a}_{ck},\hat{b}_{ck},\hat{\rho}_{ck}N)}{(\hat{b}_{ck} )^{a_{ck}}\Gamma(\hat{a}_{ck})\ln(2)}-\frac{\psi\left(\hat{a}_{ck}\right)}{ \ln(2)}-\log_{2}\left(\hat{b}_{ck}\right). \tag{16}\]  
where the last step is obtained using ([18] Equation (4.352.1)). After calculating \(l(\hat{a}_{ck},\hat{b}_{ck},\beta_{ck}N)\) similarly to Theorem 1, we can conclude the proof.
# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
## 3 Results
### Inversion of Winter Wheat Water Content Using Sentinel-1 Data
The final remote sensing thematic maps have a good performance on water content of wheat crop.
# A Privacy Measurement Framework for Multiple Online Social Networks against Social Identity Linkage
## 5 Experimental Evaluation
### Experiment 3  
In this section, we will use User7 as an example to show how users reasonably change profile settings to reduce privacy leakage and guard against SIL using our method.  
In Figure 8, we can see that the privacy score of each attribute significantly decreases after the settings change based on Table 8. Here, we provide an example of how to reduce privacy leakage using our method. Because people have varying requirements for privacy protection in the real world, there is no universal protection method. The fundamental purpose of the privacy score in this paper is to stimulate and cultivate the privacy awareness of users to ensure that the privacy of users is not violated from the beginning. Users can intuitively understand their privacy according to the privacy score.  
Figure 8: Privacy score after profile changes.  
Figure 7: Privacy score on fewer online social networks (OSNs).  
To demonstrate the effectiveness of our approach to prevent SIL, we chose three methods of SIL that use profile matching [16; 17; 20] (represented by method 1, method 2, and method 3). First, we formed a dataset by randomly crawling profiles on Sina Microblog; this dataset includes 2000 profiles. Then, we put the profiles of the 28 people in our study into the Sina Microblog and Kaixin.com dataset. Finally, we use their Tencent Microblog and QQ profiles as testing data to match the 2056 profiles in the dataset.  
In this experiment, we ran each method 1000 times and recorded the precision of successful matches. Moreover, the changes of the profiles in our testing data settings did not deliberately differentiate the testing data from the dataset. Therefore, some profiles can still be matched after changing the settings. In Figure 9, because most people do not deliberately set different usernames, avatars, and other highly recognizable attributes on different OSNs, the possibility of a user being successfully matched is very high. After changing the settings, this probability is greatly reduced.
# A New Internet of Things Hybrid VLC/RF System for m-Health in an Underground Mining Industry
## 6 Conclusions  
The proposed hybrid architecture for VLC and RF communications, based on IIoT, presents a promising solution to monitor the mobile health of workers in the mining industry. The use of VLC for downlink positioning and RF communication for uplink vital signs data transmission demonstrates the feasibility of this architecture. The results of the coverage and transmission analysis indicate that the proposed system effectively addresses the challenges of mobile health monitoring in mining environments. The findings support the viability of the hybrid architecture, providing valuable information for the implementation and deployment of the communication system. This research contributes to the advancement of m-Health technologies and underscores the potential of the proposed hybrid communication system to improve worker safety and health in the mining industry. Future work could focus on further optimizing the system's performance, considering factors such as environmental influences and potential integration with advanced data analytics for real-time health monitoring and predictive maintenance in mining operations.  
Conceptualization, D.I. and P.P.J.; formal analysis, A.D.F., D.I. and I.S.; funding acquisition, J.G.-M.; experiments, D.I. and A.D.F.; project administration, P.P.J. and J.G.-M. and I.S.; writing--original draft preparation, A.D.F. and P.P.J.; writing--review and editing, A.D.F., M.I., I.S., J.G.-M. and D.Z.-B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ANID FONDECYT Regular (1211132), ANID PFCHA/Beca de Doctorado Nacional/2019 (21190489), SENESCYT \"Convocatoria abierta 2014-primera fase, (Acta CIBAE-023-2014)\", Escuela de Informatica y Telecomunicaciones, Universidad Diego Portales, Facultad de Ingenieria, Pontificia Universidad Catolica del Ecuador, Telecommunications Engineering Degree, FICA, Universidad de Las Americas, FONDECYT iniciacion No. 11230129, the Competition for Research Regular Projects, year 2021, code LPR21-02, Universidad Tecnologica Metropolitana, FONDECYT No. 3190147, FONDEF IT17M10012, and FONDECYT No. 11160517.  
Not applicable.  
**Informed Consent Statement:** Not applicable  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
## 3 Results and Discussion
### C-glycosflavone Derivatives
#### 3.1.4 Hydroxylasmonic Acid Derivatives  
Hydroxylasmonic acid derivatives were also detected in _C. vespertilionis_ extract based on Clusters G (positive ion mode) and R (negative ion mode) of the generated MN (Figure 7). Spectral library matching via the GNPS platform allowed the annotation of a node in Cluster G, having precursor ion [M + NH\({}_{4}\)]\({}^{+}\) at \(m/z\) 406.207, as 3-oxo-2-[5-(hexopyranosyloxy)-pent-2-enyl]-cyclopentaneacetic acid (commonly known as tuberonic acid hexoside) (**24**). The putative identity of **24** was also supported by the negative ion MS/MS data, as represented by the node with precursor ion [M - H]\({}^{-}\) at \(m/z\) 387.166, which is congruent with previously reported data [31]. The neighboring node, with a precursor ion at \(m/z\) 408.223 [M + NH\({}_{4}\)]\({}^{+}\), a mass difference of 2 Da from that of **24**, was proposed as its hydrogenated derivative, **26**, further supported by characteristic fragment ions at \(m/z\) 229 for [(M+NH\({}_{4}\))-NH\({}_{3}\)-162]\({}^{+}\), 211 for [(M+NH\({}_{4}\))-NH\({}_{3}\)-162-H\({}_{2}\)O]\({}^{+}\) (base peak), and 165 for [(M+NH\({}_{4}\))-NH\({}_{3}\)-162-H\({}_{2}\)O-(CO+H\({}_{2}\)O)]\({}^{+}\). The base peak ion provided key evidence for the main backbone chain of this compound (Figure S14). The putative identity of compound **26** was further supported by the negative ion MS/MS data, which gave a deprotonated molecular ion at \(m/z\) 389.182 [M - H]\({}^{-}\) and fragment ions at \(m/z\) 209 for [(M\(-\)H)-162-H\({}_{2}\)O]\({}^{-}\) and 59 for carboxylate ion, CO\({}_{2}\)CH\({}_{3}\). The node in Cluster G, with a precursor ion at \(m/z\) 406.244 [M + NH\({}_{4}\)]\({}^{+}\), was deduced to be a structural isomer of **24**, possibly differingin the location of the double bond on the backbone chain. This compound (**33**) showed the same fragment ions as **24**, i.e., at \(m/z\) 209 and 163. Another node in Cluster G, having precursor ion [M + NH4]\({}^{+}\) at \(m/z\) 492.244 (**42**), a mass difference of 86 Da from that of **33**, was proposed to be a malonylated derivative of **33**. The mass spectral data suggested that the malonyl moiety (C3H2O3) was attached to the hydroxyl group of the hexoside unit based on the fragment ions formed at \(m/z\) 227 for the simultaneous losses of ammonia and malonylhexose, 209 for [(M+NH4)-NH3-162-86-H2O]\({}^{+}\), and 163 for [(M+NH4)-NH3-162-86-H2O-(CO+H2O)]\({}^{+}\). Meanwhile, a node with a precursor ion at \(m/z\) 475.217 was subsequently assigned as the protonated molecular ion [M + H]\({}^{+}\) of **42** on the basis of a similar fragmentation pattern at \(m/z\) 227, 209, and 163.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 4 Suitability of Legacy Techniques and Standards for 5G and Upcoming Networks
### Proxy Mobile IPv6 (PMIPV6)  
The structure of proxy mobile IPV6 is such that it does not need any involvement from a mobile node in the handover process [30; 34; 35]. In light of the discussion in Section 3, we identify the advantages and drawbacks of PMIPV6 techniques concerning their usage in 5G and future networks. These are as follows:  
**Advantages**  
* The decentralization can be achieved by supporting the DMM approach of PMIPV6 [35].
* Similarly, by supporting the cluster-based technique in PMIPV6, the dependability, energy-saving, and extensibility can be enhanced [25].
* Likewise, seamless mobility is also possible with the support of [25; 29].
* Furthermore, the stated approach (PMIPV6) is also adopted by 3 GPP for LTE; hence, the pertinent implementation capability will increase the ease of usage for the adaptation of the future network.  
**Drawbacks**  
* An apparent behavior of PMIPv6 concerning the parameters for adaptability and battery-saving criteria is omitted [29; 35].
* PMIPv6 also lacks the capabilities of adaptability and dependability in its original specification due to the creation of single point of failure (SPOF) molded by the LMA in its design [25].  
In light of the stated advantages and drawbacks of PMIPv6, it is inferred that the seamless handover parameter DL4 (Table 2) in dependability criteria is fulfilled by the original implementation and deployment of PMIPV6. Similarly, with the help of distributed and cluster-based approaches in PMIPV6, the parameters EL2 (Table 2) and DL5 (Table 2) of extensibility and dependability are fulfilled, respectively. Finally, as we also know that PMIPV6 has been applied and explored in the LTE networks, it satisfies the parameter EL4 (Table 2) of extensibility that is based on ease of implementation.
## 4 Contribution of Thermodynamic Calculations
### Calculations in the Cu-Fe-O System  
In the present work, no experimental measurements were carried out in the Cu-Fe-O system. However, the existence of a full thermodynamic model [52] makes it possible to evaluate the potentialities of such mixed oxides for the purpose of TCES applications.  
As mentioned in Section 3, one of the main constraints regarding Cu-containing redox systems is to avoid the formation of a liquid phase. As shown by the phase diagram plotted in Figure 11a, some liquid phase forms at 1048 \({}^{\circ}\)C for 0 \(<\) x(Fe) \(<\) 0.50, due to the presence of an eutectic at x(Fe) = 0.15. Meanwhile, the minimal temperature to reach in order to reduce Cu\({}^{\rm II}\) into Cu\({}^{\rm I}\) is 1028 \({}^{\circ}\)C. Thus, in the composition range 0 \(<\) x(Fe) \(<\) 0.50, the addition of iron to copper oxide does not solve the problem of a very narrow window of operating temperature.  
Figure 11b presents the mass variation (i.e., the OSC) in the whole Cu-Fe composition range, for various maximal cycle temperatures. In agreement with the two compositions studied by [PERSON] et al. (2016) [41], the calculations show that the addition of iron to copper oxide decreases the OSC if the maximal temperature is fixed at 1040 \({}^{\circ}\)C. For high iron content (x(Fe) \(>\) 0.66), the mass variation is very low. Indeed, in this case the TCES properties rely on the Fe\({}^{\rm III}\)/Fe\({}^{\rm II}\) redox reaction, which requires much higher temperatures (above 1200 \({}^{\circ}\)C). Figure 11b also clearly illustrates that a maximal temperature lower than 1040\({}^{\circ}\)C implies a low OSC for most of the Cu-Fe compositions.  
Notably, the calculations evidence that the case of pure delafossite compound CuFeO\({}_{2}\) (x(Fe) = 0.5) may constitute the best compromise in the Cu-Fe-O systems. This stoichiometric compound allows for a higher maximal temperature of operation before decomposition (1075 \({}^{\circ}\)C instead of 1048 \({}^{\circ}\)C), with an OSC value of 4.9% as already confirmed by experimental studies devoted to delafossite properties [62], and a calculated transition enthalpy of 206 kJ/kg. This observation calls for a deeper investigation of the potentialities of CuFeO\({}_{2}\) for TCES applications.  
Figure 11: (**a**) Calculated phase diagram (model from [52]) of the Cu-Fe-O system in air, with highlight on the presence of the liquid phase (**b**) Evolution of mass change with composition for cycles between T\({}_{\rm min}\) = 800 \({}^{\circ}\)C and T\({}_{\rm max}\) = 1010, 1025 and 1040 \({}^{\circ}\)C.
# Fault Diagnosis via Neural Ordinary Differential Equations
## 4 Fault Diagnosis via Neural ODE Applied to Nonlinear Benchmark System
### Actuator Faults
#### 4.3.2 Results  
In order to evaluate the results, we first show in Figure 7 the generated dynamics using the neural network for 2000 points with initial conditions \(x=[0,0,0,0]\), that is compared in the same plot with the real system responses, this demonstrates that the network is representing essential information from the model.  
For the diagnosis, a total of 20,000 samples from the data-set are used, results are shown in Figure 8, where a comparative of the real faults and detected faults is presented, the network reached 98.73% of accuracy for this test. In the total data-set with 1,000,000 samples, the accuracy was 98.5%.  
Figure 6: Neural network model for fault diagnosis training.  
Figure 7: First stage. Continuous line: true dynamics, dotted line: learned dynamics.
## 4 Behavioral and Quantitative Analysis of Reconfigurable S\({}^{3}\)PR Net
### Reversibility  
Reversibility means that a system can always return to its initial marking. A controlled reconfigurable S\({}^{3}\)PR Petri net model (\(N_{RC}\), \(M_{RCo}\)) is reversible if for each marking \(M\in R(N_{RC}\), \(M_{RCo})\), initial marking \(M_{RCo}\) is reachable from \(M\).  
**Theorem 9**: _Let a reconfigurable S\({}^{3}\)PR net (\(N_{RC}\), \(M_{RCo}\)) with \(N_{RC}=(P_{RC}\), \(T_{RC}\), \(F_{RC}\), \(W_{RC}\), \(M_{RCo}\), \(K_{RC}\)) be a live and controlled net. \(N_{RC}\) is reversible if for each marking \(M\in R(N_{RC}\), \(M_{RCo})\), initial marking \(M_{RCo}\) is reachable from \(M\), \(M\) and \(M_{RCo}\) satisfying all place invariants and \(M\) marks each trap of \(N_{RC}\)._  
Suppose that \(M\) is reachable. Then there exists a finite transition sequence \(\delta=t_{1}\ t_{2}\ t_{3}\ \dots\ t_{n}\) that can be fired, and markings \(M_{1}\), \(M_{2}\), \(M_{3}\), \(\dots\), _and_\(M_{n-1}\) are such that \(M_{RCo}[t_{1})M_{1}[t_{2})M_{2}[t_{3})M_{3}\dots\)\(M_{n-1}[t_{n})M\), expressed as \(M_{RCo}[\delta)M\), agrees with the state equation \(M=M_{RCo}+[N_{RC}]\stackrel{{\rightarrow}}{{\delta}}\). In addition, \(M\) and \(M_{RCo}\) satisfy all place invariants, \(I^{T}.M=I^{T}.M_{RCo}\). Therefore, we can say that \(M_{RCo}\) is the home marking of the net (\(N_{RC}\), \(M_{RCo}\)), \(M\) is reachable from \(M_{RCo}\), and we get \(M_{RCo}\stackrel{{\rightarrow}}{{\delta}}M\). Thus, the reconfigurable S\({}^{3}\)PR net (\(N_{RC}\), \(M_{RCo}\)) is reversible.  
To demonstrate the reversibility of a controlled reconfigurable S\({}^{3}\)PR net, consider the model illustrated in Figure 8. Its reachability graph is illustrated in Figure 13. In the net shown in Figure 8, there are seven minimal place invariants: \(I_{1}=p_{3}+p_{5}\), \(I_{2}=p_{2}+p_{3}+p_{10}\), \(I_{3}=p_{7}+p_{9}\), \(I_{4}=p_{4}+p_{7}+p_{11}\), \(I_{5}=p_{2}+p_{3}+p_{4}+p_{7}+p_{12}\), \(I_{6}=p_{2}+p_{4}+p_{6}+p_{8}\), \(I_{7}=p_{1}+p_{2}+p_{3}+p_{4}+p_{7}+p_{8}\), since \(\forall\)i \(\in\) {1,2,3,4,5,6,7}, \(I_{i}^{T}\). \([N_{RC}]=0^{T}\). \(M_{6}\in R(N_{RC}\), \(M_{RCo})\), \(I_{1}^{T}.M_{6}\)=\(I_{1}^{T}.M_{RCo}\)= \(M_{6}(p_{3})+M_{6}(p_{5})\)= \(M_{RCo}(p_{3})+M_{RCo}(p_{5})\)=1. The net has a unique T-invariant \(J=t_{1}+t_{2}+t_{3}+t_{4}+t_{5}+t_{6}\) and the transition sequence \(\delta=t_{1}t_{2}t_{3}t_{4}t_{5}t_{6}\) is firable. As a result, \(M_{RCo}[t_{1})M_{1}[t_{2})M_{2}[t_{3})M_{3}[t_{4})M4[t_{5})M6[t_{6})M_{RCo}\). Therefore, the reconfigurable S\({}^{3}\)PR net (\(N_{RC}\), \(M_{RCo}\)) is live, bounded, and reversible.  
Figure 13: Reachable markings of a controlled reconfigurable S\({}^{3}\)PR net, as illustrated in Figure 7.
## 5 Algorithm Analysis
### Number of Trips Using the Required Number of Steps: Best Case  
The best case occurs when there is almost no overlap of paths to visit every node in the DFG. If the graph \(G_{h}\) is divided by a straight line between the base station and the farthest region \(\eta\), symmetrical sub areas are obtained. Figure 7 shows the environment divided in half. If the process continues, eventually only a straight line is obtained. Thus, it is possible to apply a divide and conquer strategy. Formally, this behavior is defined as follows. Let _f(n)_ be the problem of exploring an environment represented by graph \(G\) with \(n\) regions.  
**Definition 1**.: _Each time the DFG \(G_{h}\) is divided, f(n) is split into two equal subsets with the same cardinality. In order to combine the solution, at least \(n\) steps need to move towards the base station. Therefore, for any given environment \(f(n)\) can be expressed by:_  
\[f(n)=2f(n/2)+c \tag{7}\]  
_where \(c\) is a constant._  
We have a linear time to explore \(n\) nodes, that is: \(f(n)=\mathcal{O}(n)\) (see Appendix B). However, there is a precise way of calculating the minimum number of trips required to explore any DFG \(G_{h}\), given the required number of steps \(e_{h}\) available. There are two ways that a main line can be traversed: Either by choosing a main line and returning to the base using the same regions, or by backtracking using the next row of regions. However, the farthest region \(\eta\) of the next row will not be marked. Figure 8 shows an example of this situation. It can be seen that the minimum number of trips for any given DFG \(G_{h}\) is equal to its depth \(p_{h}\).  
**Definition 2**.: _The minimum number of trips \(T_{h}\) to explore any given DFG \(G_{h}\) is equal to its depth \(p_{h}\) (see Equation (1))._  
\[T_{h}=p_{h} \tag{8}\]  
For any DFG \(G_{h}\), in every trip, the number of nodes traversed is \(2(2h-1)\). Since there are \(T_{h}\) trips, the total number of nodes traversed required to explore the DFG \(G_{h}\) is \(2T_{h}(2h-1)\). Based on Equation (8), the previous equation is \(2(2h-1)^{2}\). Expanding the equation yields:  
Figure 7: A central line divides the environment in half. Step 1 divides the Hextille in two symmetric parts. Step 2 continues to do this until there is only one straight line at Step 3.  
\[f(n)=8h^{2}-8h+2 \tag{9}\]  
However, to obtain profitable data sources, various trips must overlap in order to discriminate valuable sources from common ones. Therefore, a general case of reconnaissance where various trips overlap must be addressed.
## 3 A Resilient Routing Protocol
### Routing Algorithm  
The routing algorithm implemented within the SDN controller integrates the clustering results, AHP-based weights, and DQN to provide reliable and efficient routing paths. The algorithm is outlined below: