esa-sceva/satcom-chunk-collection huggingface dataset:
# Chaos and Stability in a New Iterative Family for Solving Nonlinear Equations
## 3 Complex Dynamics Behavior
### Rational Operator  
The rational operator can be built on any nonlinear function; however, we construct this operator on quadratic polynomials, as the criterion of stability or instability of a method applied to these polynomials can be generalized for other nonlinear functions.  
**Proposition 1**.: _Let \(p(x)=(x-a)(x-b)\) be a generic quadratic polynomial with roots \(a,b\in\mathbb{R}\). Therefore, the rational operator \(R_{\alpha}(x)\) associated with CMT(\(\alpha\)) family given in (29) and applied on \(p(x)\), is_  
\[R_{\alpha}(x)=\frac{x^{6}\big{(}x^{6}+5x^{5}+12x^{4}+19x^{3}+21x^{2}+14x+ \alpha+5\big{)}}{(\alpha+5)x^{6}+14x^{5}+21x^{4}+19x^{3}+12x^{2}+5x+1}, \tag{30}\]  
_with \(\alpha\in\mathbb{C}\) an arbitrary parameter. Furthermore, if \(\alpha\in\{-77,-1,1,5\}\), \(R_{\alpha}(x)\) is simplified as shown_  
\[R_{-77}(x) =-\frac{x^{6}\big{(}x^{5}+6x^{4}+18x^{3}+37x^{2}+58x+72\big{)}}{72 x^{5}+58x^{4}+37x^{3}+18x^{2}+6x+1}, \tag{31}\] \[R_{-1}(x) =\frac{x^{6}\big{(}x^{4}+3x^{3}+5x^{2}+6x+4\big{)}}{4x^{4}+6x^{3} +5x^{2}+3x+1},\] (32) \[R_{1}(x) =\frac{x^{6}\big{(}x^{4}+4x^{3}+7x^{2}+8x+6\big{)}}{6x^{4}+8x^{3} +7x^{2}+4x+1},\] (33) \[R_{5}(x) =\frac{x^{6}\big{(}x^{4}+5x^{3}+11x^{2}+14x+10\big{)}}{10x^{4}+14x ^{3}+11x^{2}+5x+1}. \tag{34}\]  
Proof.: Let \(p(x)=(x-a)(x-b)\) be a generic quadratic polynomial with roots \(a,b\in\mathbb{R}\). We apply the iterative scheme given in (29) on \(p(x)\) and obtain a rational function \(A_{p,\alpha}(x)\) which depends on the roots \(a,b\in\mathbb{R}\) and a parameter \(\alpha\in\mathbb{C}\). Then, if we use Mobius transformation (see in [7; 8; 9]) in \(A_{p,\alpha}(x)\) with  
\[h(w)=\frac{w-a}{w-b},\]  
that satisfies \(h(\infty)=1,h(a)=0\) and \(h(b)=\infty\), we get  
\[R_{\alpha}(x)=\Big{(}h\circ A_{p,\alpha}\circ h^{-1}\Big{)}(x)=\frac{x^{6} \big{(}x^{6}+5x^{5}+12x^{4}+19x^{3}+21x^{2}+14x+\alpha+5\big{)}}{(\alpha+5)x^{ 6}+14x^{5}+21x^{4}+19x^{3}+12x^{2}+5x+1}, \tag{35}\]  
which only depends on an arbitrary parameter \(\alpha\in\mathbb{C}\). Furthermore, if we factor numerator and denominator of (35), it is easy to show that for \(\alpha\in\{-77,-1,1,5\}\) some roots coincide and simplify \(R_{\alpha}(x)\), as it is observed in Equations (31)-(34), and the proof is finished.  
From Proposition 1, for four values of \(\alpha\) the rational operator \(R_{\alpha}(x)\) is simpler, so there will be fewer fixed and critical points that can improve the stability of the associated methods. This will be seen in Sections 3.2 and 3.3.
# Polynomial Time Algorithm for Shortest Paths in Interval Temporal Graphs +
## 3 Shortest Paths
### Overview  
Let \(P(v,k)\) be the list of pairwise non-dominant \(r\)-hop paths \((t,l)\) from \(s\) to \(v\) that arrive at \(v\) at time \(t\) and have length \(l\), \(r\leq k\); the paths are in increasing order of \(t\) (equivalently, decreasing order of \(l\)). Let \(P_{new}(v,k)\) be the list of non-dominated \(k\)-hop paths from \(s\) to \(v\). A high-level description of our shortest paths algorithm can be found in Algorithm 1.  
The lists \(P(v,0)\) and \(P_{new}(v,0)\) are initialized in lines 1 and 2 to be lists of 0-hop paths from \(s\) to \(v\). The \((k-1)\)-hop paths in \(P_{new}(u,k-1)\) are extended to \(k\)-hop paths by adding an outgoing edge \((u,v)\) in lines 8 and 10. Line 8 considers one edge extensions that use the interval \((i.st,i.c)\) for the case \(t<i.st\). Of the possible extensions using this interval, the one that departs \(u\) at time \(i.st\) dominates the others and so only this one is added to \(P_{new}(v,k)\). Line 10 considers the remaining case for a legitimate extension, \(i.st\leq t\leq i.c\). Again, only the single non-dominated extension is added to \(P_{new}(v,k)\). Line 13 removes dominated paths from \(P_{new}(v,k)\). In Line 14, the paths in \(P(v,k-1)\) and \(P_{new}(v,k)\) are merged to obtain \(P(v,k)\) (recall that \(P(v,k)\) is to contain only non-dominated paths). Additionally, paths in \(P_{new}(v,k)\) that are dominated by a path in \(P(v,k-1)\) are eliminated from \(P_{new}(v,k)\).  
Figure 5: \(p1\) dominates \(p2\).  
```
1:\(P(s,0)\leftarrow\{(0,0)\}\), \(shtrstP(s)=(0,0)\) ;\(\forall v\
eq s,P(v,0)\leftarrow\varnothing,shtrstP(v)=\varnothing\)
2:\(P_{new}(s,0)\leftarrow\{(0,0)\}\) ;\(\forall v\
eq s,P_{new}(v,0)\leftarrow\varnothing\)
3:for\(k=1,\ldots,n-1\)do
4:\(\forall v,P_{new}(v,k)=\varnothing\)
5:for every edge \((u,v)|P_{new}(u,k-1)\
eq\varnothing\)do
6:for all\((t,l)\in P_{new}(u,k-1)\)do
7:\(\forall i\in(u,v)|t<ist\) add \((t^{\prime},l^{\prime})=\)
8:\((i.st+i.\lambda,l+i.\lambda)\) to \(P_{new}(v,k)\)
9: If there is an \(i\), such that \(i.st\leq t\leq i.c\), add
10:\((t^{\prime},l^{\prime})=(t+i.\lambda,l+i.\lambda)\) to \(P_{new}(v,k)\)
11:endfor
12:endfor
13:\(\forall v\), eliminate dominated paths from \(P_{new}(v,k)\)
14:\(\forall v\), merge \(P_{new}(v,k)\) and \(P(v,k-1)\) to get \(P(v,k)\) eliminating dominated paths from \(P(v,k-1)\) as well as paths in \(P_{new}(v,k)\) that are dominated by paths in \(P(v,k-1)\)
15:\(\forall v|P_{new}(v,k)\
eq\varnothing\), \(shtrstP(v)=P(v,k).shtrst()\)
16: If \(\forall v,P_{new}(v,k)=\varnothing\) terminate!
17:endfor
18:return shtrstP[]
```  
**Algorithm 1** Shortest Paths Pseudocode
## 2 Experimental Validation
### Comparison of Residual Cross-Section  
In the following step, models M2, M3, and M4 were also analyzed according to the evolution of the char layer over time for the instants of 90, 100, 110, and 120 min. The wood profile used to analyze the char layer was in a cross-section of the central wooden beam, to obtain results for the most critical region. The limit temperature considered for the formation of the char layer, imposed by the authors [7], was 288 \({}^{\circ}\)C.  
To evaluate the char layer and compare their proximity with the results obtained experimentally, the percentage of the residual wood cross-sections was determined, as shown in Table 2. Simultaneously, the relative error was calculated, regarding Test 4, and presented in Table 2.  
Based on the representation of the char-layer obtained by the experimental tests from the authors [7], the Image\({}^{\circledR}\) 1.52a software was used to determine the residual wood cross-section. This software relates the pixels of a given image to the dimensions of the element and delimits sections by color, thus, allowing the charred area to be obtained. With  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Controlled Points**} \\ \cline{2-5}  & & **A** & **E** & **F** \\ \hline \multirow{4}{*}{M3} & n & 152 & 129 & 141 \\  & M & 284.61 & 121.89 & 190.57 \\  & SD & 222.87 & 89.51 & 140.91 \\  & SEM & 18.08 & 7.88 & 11.87 \\ \hline \multirow{4}{*}{M4} & n & 138 & 127 & 142 \\  & M & 246.32 & 111.86 & 164.93 \\ \cline{1-1}  & SD & 198.90 & 81.28 & 126.90 \\ \cline{1-1}  & SEM & 16.93 & 7.21 & 10.65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative observations for models M3 and M4: n, M, SD, and SEM.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Area, mm\({}^{2}\)** & **90 min** & **100 min** & **110 min** & **120 min** \\ \hline Test 4 [7] & 3382 & 81.8 & 65.7 & 51.7 & 25.6 \\ \hline \multirow{2}{*}{M2} & R & 100.0 & 96.6 & 92.1 & 88.2 \\  & e & 22.25 & 47.03 & 78.14 & 244.53 \\ \hline \multirow{2}{*}{M3} & R & 100.0 & 92.6 & 72.6 & 36.4 \\  & e & 22.25 & 40.94 & 40.43 & 42.19 \\ \hline \multirow{2}{*}{M4} & R & 96.8 & 88.3 & 51.3 & 13.3 \\  & e & 18.34 & 34.40 & 0.77 & 48.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Residual wood cross-section (R) and relative error (e), %.  
this, it was possible, considering the total area of the amount of wood and the charred area, to calculate the residual wood cross-section, \(R\), by applying Equation (1).  
\[R=\frac{Area-A_{charred}}{Area}\times 100 \tag{1}\]  
Considering the results obtained in Table 2, it is concluded that the model developed with the effect of radiation and convection in the cavities (M4) presents the closest and most favorable results to Test 4. For the instants 90, 100, and 110 min, in M4, the relative error was smaller, and the values were closer to Test 4 when compared to the other models, except for 120 min.
## 2 The Normal-Mode Helical Antenna Design Considerations
### Performance Improvement of the Active Helical Antenna over the Half-Wave Dipole  
In this section, the performance of the active helical antenna in comparison to a passive half-wave horizontally polarized dipole is presented. The tests were conducted under real conditions in a non-reflective environment. The impedance of the monitoring reference horizontal half-wave dipole was measured at each test frequency (within 20-30 MHz) using a portable vector analyzer (NanoVNA V2). This ensured that all coupling losses of the reference dipole were accounted for in the final spectrum analyzer signal strength measurements, as depicted in Figure 10.  
The active helical antenna was tested both vertically and horizontally to determine its axial ratio, a vital array design parameter. The distance between the transmitting and receiving antennas was set at 15 \(\lambda\) to ensure proper far-field measurements. The transmitting antenna used for the test was a half-wave horizontally polarized dipole. The signal strength values of the helical antenna under test are illustrated in Figure 10 as well. The blue curve represents the response of the reference half-wave horizontally polarized dipole. The brown and grey curves represent the respective vertical and horizontal responses of the active NMHA.  
Based on Figure 10, the helical antenna exhibits an average axial ratio of 2 dB. Considering the compact size of the antenna, which is 6 cm in height operating at the 12 m band, the results are very satisfactory.  
Figure 9: The spectrum analyzer results of the helical antenna under real conditions.
## Security Analysis in Spatial and Frequency Domains
### Discrete Entropy  
Discrete Entropy (or Shannon Entropy) is a statistical measure of randomness so as to characterize the digital image. It is formulated as a sum of products of the probability of outcome times the logarithm of the inverse of the probability (7), over all potential outcomes in the event \(\{x_{1},x_{2},\ldots,x_{k}\}\), where \(p(i)\) is the probability distribution and \(k\) is the count of intensity levels. It can simply describe the uncertainty of an arbitrary set of discrete events. Intuitively it indicates the average amount of information conveyed from the digital image information source being selected.  
\[H(x)=\sum_{i=1}^{k}{p(i)\log_{2}{\frac{1}{p(i)}}}=-\sum_{i=1}^{k}{p(i)\log_{2} {p(i)}} \tag{7}\]  
Regardless of the host, chaotic watermarked and dual chaotic watermarked images, the greater the discrete entropy, the more the information content, and, in turn, the more the security. All three cases of digital image chaotic watermarking and dual chaotic watermarking were taken into account with all discrete entropy data listed in Table 3. The discrete entropies of three chaotic watermarked images are slightly bigger than those of three source images. It shows that the former was a little bit more complex than the latter with respect to information content. On the other hand, the discrete entropies of three dual chaotic watermarked images were much higher than those of three source images and three single chaotic watermarked images. It highlighted that a remarkable extra amount of information was covered using dual chaotic watermarking, making it much more secure and robust against attacks (e.g., Entropy Attack).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mu\)**Outcomes** & \(\mu\) = **3.6** & \(\mu\) = **3.7** & \(\mu\) = **3.8** & \(\mu\) = **3.9** & \(\mu\) = **4.0** \\ \hline Chaotic Watermarking & 0.1200 & 0.1201 & 0.1201 & 0.1199 & 0.1199 \\ Dual Chaotic Watermarking & 0.1426 & 0.1425 & 0.1425 & 0.1424 & 0.1421 \\ Mismatch Chaotic Shuffling & 0.6600 & 0.6600 & 0.6600 & 0.6600 & 0.6600 \\ Mismatch Dual Watermark & 0.6601 & 0.6601 & 0.6601 & 0.6601 & 0.6601 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative Metric of Unified Average Changing Intensity (UACI).
## 2 Methodology
### Proposed Controller Design
#### 2.4.2 Stability Analysis
So, by using (41) in conjunction with (40) allows us to get an upper-bound of \(\dot{V}(\mathbf{e},\mathbf{\tilde{\theta}})\) (see Equation (34)) given by  
\[\dot{V}(\mathbf{e},\mathbf{\tilde{\theta}})\leq-\lambda_{m}\{\mathbf{Q}\}\|\mathbf{e}\|^{2}+ \mathbf{e}^{T}\mathbf{Pbw} \tag{42}\]  
The expression given in (42) can be rearranged as follows:  
\[\dot{V}(\mathbf{e},\mathbf{\tilde{\theta}})\leq -\left[\lambda_{m}\{\mathbf{Q}\}-\frac{1}{2}\right]\|\mathbf{e}\|^{2}+ \frac{1}{2}\|\mathbf{Pbw}\|^{2}\] \[\Delta_{1}=\det[b_{11}]=-Lk_{d_{1}}p_{12},\]0  
which can be simplified as  
\[\Delta_{1}=\det[b_{11}]=-Lk_{d_{1}}p_{12},\]1  
By recalling the outstanding result from (2) (Theorem 9.1, p. 124) we can tune the fuzzy systems to make the minimum approximation error \(w\) small enough such that \(\|\mathbf{Pbw}\|<\delta\) with \(\delta>0\) as an arbitrarily small constant. Hence, by stability theory of perturbed systems (see, e.g., (26) Lemma 9.3, pp. 348-349) and selecting \(\lambda_{m}\{\mathbf{Q}\}>\frac{1}{2}\) is possible to conclude that \(\mathbf{e}\) and \(\mathbf{\tilde{\theta}}\) are uniformly bounded, and \(\mathbf{e}\) is uniform ultimately bounded. Then, we have that the states of the closed-loop system, that is to say, the generalized error and the fuzzy parameters estimation error, are bounded (27) in the sense that \(\sup_{t\geq 0}\|\mathbf{e}\|<\infty\) and \(\sup_{t\geq 0}\|\mathbf{\tilde{\theta}}\|<\infty\) i.e, \(\mathbf{e}\in\mathcal{L}_{\infty}^{2}\) and \(\mathbf{\tilde{\theta}}\in\mathcal{L}_{\infty}^{2(\sum_{t=1}^{2}\prod_{j=1}^{2}p_ {j})}\). Furthermore by integrating both sides of (43) and selecting \(\lambda_{m}\{\mathbf{Q}\}>\frac{1}{2}\) we have  
\[\Delta_{1}=\det[b_{11}]=-Lk_{d_{1}}p_{12},\]2 \[\Delta_{1}=\det[b_{11}]=-Lk_{d_{1}}p_{12},\]3  
where \(V_{0}:=V(\mathbf{e}(0),\mathbf{\tilde{\theta}}(0))\). If \(w\) is square integrable, then \(\mathbf{e}\) is also square integrable, and from (32) follows that \(\mathbf{\dot{e}}\) is also bounded; then from Barbalat Lemma we can conclude that \(\lim_{t\to\infty}\mathbf{e}=\mathbf{0}\). In order to assure \(\hat{G}\) in (22) is different from zero, we require that \(\|\mathbf{\theta_{g}}\|:=\|\mathbf{\theta_{g_{1}}}^{T}\), \(\mathbf{\theta_{g_{2}}}^{T}\|\) be bounded from below by a positive constant \(\epsilon>0\). Towards this end we have used a projection modification for (25) and (26) given by (2):  
Whenever an element of \(\mathbf{\theta_{g_{1}}}\) or \(\mathbf{\theta_{g_{2}}}\) equals \(\epsilon\), use  
\[\dot{\mathbf{\theta}}_{g_{1}}=\left\{ \begin{array}{ccc}-\gamma_{3} Le^{T}\mathbf{Pb\eta_{1}u}&\text{if}&\mathbf{e}^{T}\mathbf{Pb\eta_{1}u}<0\\ \\ 0&\text{if}&\mathbf{e}^{T}\mathbf{Pb\eta_{1}u}\geq 0\end{array} \right.\]or  
\[\dot{\theta}_{g_{2}}=\left\{ \begin{array}{ccc}-\gamma_{4}\mathbf{e}^{T}\mathbf{P}\mathbf{b} \eta_{2}u&\text{if}&\mathbf{e}^{T}\mathbf{P}\mathbf{b}\eta_{2}u<0\\ \\ 0&\text{if}&\mathbf{e}^{T}\mathbf{P}\mathbf{b}\eta_{2}u\geq 0\end{array} \right.\]  
according to each case.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### Drones' Connectivity  
Ensuring smooth, reliable, and continuous connectivity for drones is one of the major challenges faced in the implementation of drones over mobile wireless networks. Drones flying in multiple levels of airspace require seamless connectivity, which is required for network planning and implementation. An extensive network coverage ensures seamless connectivity while the drones are flying autonomously. However, covering a wide range of altitudes and maintaining reliable communication is a significant challenge for 4G/5G cellular networks. This is due to different factors, such as the fast movements of drones, the different trajectories, high levels of interference due to the LoS connections, and movement in 3D. Moreover, drones move in the sky faster than UEs, resulting in a large variance in the RSRP. This will lead the connected drones to continue switching the connection link from one cell to another much more than in the terrestrial UEs. Moreover, this large variance in the RSRP may lead quickly to connection loss between drones and the serving network. Moreover, the fast growth in the use of drones will require high-capacity backhaul connectivity to ensure their reliable and smooth connectivity. Furthermore, the Multi-Level Networks (3D) composed of ultra-dense heterogeneous networks in 6G can boost the number of connected drones in high-density ecosystems by roughly 10 times compared to the connection density in 5G. This also negatively impacts the drone's connectivity. Furthermore, aircraft registration, identification, tracking, regulation, and control all necessitate reliable cellular network connectivity. Drone traffic conditions can be detected and measured by actively monitoring drones' locations and route details, and early recognition of geofencing and potential attacks can be identified accordingly. In addition, the massive growth in the use of drones, IoT applications, U2U, V2V, V2X, M2M, D2D, AR, and all the other connected devices will negatively impact connectivity. Thus, the future mobile networks will provide high-quality and reliable cellular connection with extensive coverage, and provide robust, cost-effective, and seamless connectivity beyond visible LoS.
# The Impact of Space-Based AIS Antenna Orientation on In-Orbit AIS Detection Performance
## Materials and Methods
### Analysis Method
#### 2.3.1 Data Preparation
This statement is based on the consideration that the observation is intended to be performed on the entire possible coverage area of LAPAN-A2 ranging from about \(-20^{\circ}\) to \(20^{\circ}\) of latitude. Since LAPAN-A2 is an equatorial orbiting satellite, the ground area covered by this satellite in each pass is almost similar. However, because this satellite is orbiting at 6\({}^{\circ}\) inclination, it takes several passes to cover the entire possible coverage area of this satellite. Based on our simulation, as provided in Figure 7 above, the 11-h duration, approximately 7-8 satellite passes, is enough to cover all the possible areas. Therefore, besides the power limitation issue, the 11-h maneuvering duration was also chosen because this duration gives enough time for the AIS receiver to receive all the vessel messages transmitted from the possible coverage area. In other words, in case the two sets of the observation cannot be performed, one set of observations can be used to produce valid analysis. In our case, the second observation is used in order to provide more complete datasets for analysis purposes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Task Name**} & \multirow{2}{*}{**Angle**} & \multicolumn{2}{c}{**Before**} & \multicolumn{2}{c}{**Maneuver**} & \multicolumn{2}{c}{**After**} \\ \cline{3-8}  & & **Start** & **Stop** & **Start** & **Stop** & **Start** & **Stop** \\ \hline Run-1 & 90 & 20/3/2019 & 20/3/2019 & 20/3/2019 & 21/3/2019 1:00 & 21/3/2019 & 21/3/2019 \\  & & 3:00 & 14:00 & 14:00 & 1:00 & 1:00 & 12:00 \\ Run-2 & 90 & 22/3/  
Once the necessary datasets are available, an in-depth analysis involving the comparison of the number of received messages and unique MMSIs stored on \"before\", \"maneuver\", and \"after\" datasets has been performed. The comparison was also performed based on certain classes of AIS messages, which have been categorized based on Table 2. Therefore, our concern is not only to assess the overall message reception performance but also to observe the sensitivity of the AIS receiver to various AIS classes under different antenna orientations.
# Noise Attenuation Based on Wave Vector Characteristics
###### Abstract  
Polarization filtering has been widely used to enhance signal-to-noise ratios for multicomponent seismic data. Polarization filters routinely depend on the ellipticity and directionality of spatial particle motions. However, factors such as noise and formation heterogeneity often make the polarization characteristics of body waves hard to distinguish. Here, we introduce a technique in the time domain for the separation of valid body waves and noise based on wave vector characteristics. First, we characterise the ground-roll polarization by the median wave vectors derived in large-scale moving time windows. For the suppression of ground roll, we fit the particle trajectory of ground roll by the least square method using all components simultaneously. Second, we apply three-stage smoothing to the ground-roll-removed multicomponent records. In each stage, we use mean or median vectors derived in small-scale moving-time or moving-trace windows to attenuate random noise and other non-ground-roll related coherent noise. The filter in the proposed method is not devised according to ellipticity and directionality. Instead, we use the wave vector decomposition to distinguish between noise and valid signals. Synthetic data and field data examples confirm that the proposed method can effectively suppress noise without damaging the high and low frequencies of a valid signal.  
seismic wave; wave separation; polarization filtering; three-component; noise attenuation; vector +
Footnote †: journal: Applied Sciences  
Article
## 2 Framework and Methodologies  
### Systems Framework  
This article introduces an improved Hybrid Grey Wolf Particle Swarm Optimization Multi-Output Support Vector Regression (IPSOGWO-MSVR) indoor localization algorithm, as Figure 1 illustrates. Firstly, we utilize the Intel 5300 network interface card to obtain CSI information from Wi-Fi signals. To achieve this goal, we require the support of the Ubuntu 14.04 LTS system and CSI-tools [38] driver program. By enabling the debugging mode on the Intel 5300 network card through CSI-tools, we allow the NIC to report CSI to the main storage. Secondly, we preprocess the collected CSI binary files, including data parsing, denoising, and feature extraction, to mitigate the impact of environmental noise and redundant information on localization results. Subsequently, we construct a CSI location fingerprint database and input it into the PSOGWO-MSVR model for training. The globally optimal parameters obtained from the training are then passed to the online phase, ultimately outputting the optimized model's localization results.
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
## 1 Introduction
Withstanding this, one of the main shortcomings in the use of self-assembly as a valuable cost-effective nanofabrication tool for lab-on-fiber platforms relies on limited versatility in terms of possible attainable geometries and from the natural occurrence of unwanted defects breaking the ordered pattern. In this review, we focus on the importance of the \"order\" in patterns for photonic applications and specifically for lab-on-fiber platforms. To this aim, in Section 2, we highlight the basic phenomenological aspects associated with periodic and aperiodic ordered systems in photonics. The attempts made so far to decorate the OF with highly ordered nanostructures by bottom-up methods are briefly summarized in Section 3, with emphasis on very recent papers. Special attention is devoted to reporting on techniques enabling the enlargement of the set of attainable geometries and the removal of unwanted defects. The review ends with a brief summary of available tools to quantify the degree of order.