esa-sceva/satcom-chunk-collection huggingface dataset:
# Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing
## 3 Extension of Rank-Based Ant System
### Originality Utility Function  
With the objective of promoting exploration of the search space, this section presents a utility function that measures the originality or dissimilarity of one solution with respect to a group of solutions.  
A way of measuring the distance \(dist(s,s^{\prime})\) between two tours \(s\) and \(s^{\prime}\) is to count the number of arcs contained in one tour, but not in the other [14]. The average distance between all possible pairs of tours can be used to measure the amount of exploration of the algorithm, as a decrease in the average distance indicates that preferred paths are appearing [14]. Alternatively, this distance definition (i.e., number of non-common arcs) could be used instead for our purpose of measuring the originality of one ant tour with respect to a group of tours, by considering the average distance of all the possible pairs formed by the ant tour \(s\) and the rest of the ant tours. However, a disadvantage of this measure is that it is computationally expensive [14].  
The proposed originality function, given by Equation (9), is based on the idea that the tours of those ants that traverse arcs that have been visited by a smaller number of ants are more original. The originality function \(f_{o}\) of an ant tour \(s_{k}\) is given by the inverse of the number of ants that have traversed each arc of \(s_{k}\), that is:  
\[f_{o}(s_{k})=\frac{1}{\sum\limits_{arc(i,j)\in s_{k}}\
u_{i,j}} \tag{9}\]  
where \(\
u_{i,j}\) represents the number of ants that have traversed the edge going from node \(i\) to node \(j\).  
As an illustrative example, below are calculated the originality values given by Equation (9) for each of the following five tours of a TSP problem with \(n=10\) cities. The five closed tours are:  
\[s_{1} =\{1,2,3,4,5,6,7,8,9,10,1\}\] \[s_{2} =\{1,2,3,4,5,6,7,8,9,10,1\}\] \[s_{3} =\{6,7,8,9,10,1,2,3,5,4,6\}\] \[s_{4} =\{1,8,10,9,2,3,4,5,6,7,1\}\] \[s_{5} =\{2,9,3,6,5,8,7,10,4,1,2\}\]  
To compute the originality values for each tour, we use the matrix \(\
u\) corresponding to the five tours:  
\[\
u=\begin{bmatrix}0&4&0&0&0&0&1&0&0\\ 0&0&4&0&0&0&0&0&1&0\\ 0&0&0&3&1&1&0&0&0&0\\ 1&0&0&0&3&1&0&0&0&0\\ 0&0&0&1&0&3&0&1&0&0\\ 0&0&0&0&1&0&4&0&0&0\\ 1&0&0&0&0&0&0&3&0&1\\ 0&0&0&0&0&1&0&3&1\\ 0&1&1&0&0&0&0&0&3\\ 3&0&0&1&0&0&0&0&1&0\end{bmatrix}\]where each element \(\
u_{i,j}\) indicates the number of ants that have traversed \(arc(i,j)\). For instance, \(\
u_{1,2}=4\) indicates that four ants (in this example, those associated with the tours \(s_{1},s_{2},s_{3}\) and \(s_{5}\)) have gone from node 1 to node 2.  
According to the originality function given by Equation (9), the following values are obtained: \(f_{o}(s_{5})=0.076>f_{o}(s_{4})=0.045>f_{o}(s_{3})=0.037>f_{o}(s_{1})=f_{o}(s_{2 })=0.030\). These results are in accordance with what could be expected, since \(s_{1}\) and \(s_{2}\) are identical, \(s_{3}\) and \(s_{4}\) have several arcs in common with \(s_{1}\) and \(s_{2}\), while tour \(s_{5}\) is the most different (original) tour from the rest.
## 4 Dataset Construction
### Data Preprocessing  
The type of annotation is primarily determined by the task attributes. The objective of this study is to segment the country road scene in order to obtain accurate segmentation information. Consequently, this paper focuses on pixel-level annotations of common objects on country roads. The RRD contains a substantial number of polygon annotations. The annotation cost is proportional to its complexity, which necessitates a significant amount of manual annotation work. The images are annotated using a custom-built CVAT annotation platform, with the annotations saved in JSON format. These JSON files  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Number of Semantics** \\ \hline \hline Fence & 983 \\ Barrier & 1202 \\ Asphalt road & 8480 \\ Non-hardened road & 4957 \\ Cement road & 1106 \\ Building & 4047 \\ Person & 327 \\ Sky & 2830 \\ Vegetation & 6730 \\ Banner & 592 \\ Pole & 3903 \\ Traffic sign & 724 \\ Car & 1172 \\ Motorcycle & 294 \\ Truck & 516 \\ Agricultural machinery & 106 \\ Tower & 283 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of annotated semantics per category.  
are then converted into the standard Pascal VOC format using scripts. Pascal VOC is a standardized dataset used for target detection, semantic segmentation, and other related tasks. The annotation files are stored in XML format, with each XML file corresponding to an image file. The XML files contain information about all the objects in the image, including location and category information. Figure 7 illustrates a sample of the annotated image, where each annotation entity is represented by a mask.
# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
## 5 Multi-Gateway-Based Protocol
### Cluster Setup and Schedule Creation Phase  
According to LEACH [6], nodes choose themselves as CHs in different rounds with a specified probability \(P_{i}\), which determines the expected number of CHs in current round \(r\). Let \(k\) be the number of CHs in a round and \(N\) be the total number of nodes in the monitored area.  
\[\sum_{i=1}^{N}P_{i}=k \tag{4}\]  
The probability of a node \(N_{i}\) to be elected as a cluster head at round \(r\) is given by:\[P\left(N_{i}\right)=\left\{\begin{array}{c}\frac{k}{N-k\times\left(r\text{mod}N/k \right)}\text{ : 1 if }N_{i}\in S\\ 0\text{ : otherwise}\end{array}\right. \tag{5}\]  
where \(S\) is the set of non-cluster heads in recent (_r_ mod _N/k_) rounds. \(P(N_{i})\) is 1 if node \(N_{i}\) is not selected as a CH in recent rounds otherwise \(P\left(N_{i}\right)\) is zero. Value of (_r_ mod _N/k_) determines the number of nodes selected as the CHs in the latest rounds. The total number of sensors which are eligible to be a CH at current round \(r\) is given by:  
\[T\left(r\right)=N-k\times\left(r\text{ mod }N/k\right) \tag{6}\]  
This confirms that energy consumption in all sensors is nearly the same after each round. The cluster heads are chosen in every region independently. CHs broadcast an ADV (advertisement) control packet to neighboring nodes in its region. Neighboring nodes reply with ACK (acknowledgement) to the nearest CH and become members of a cluster. To avoid collision, the cluster head of this cluster sets a time division multiple access mechanism [6] among its member nodes for data transmission. Member nodes of the cluster start transmitting data to the cluster head.
## 3 Results and Analysis
### Accuracy Comparison and Analysis of Predictive Models
Since the _RPD_ value of the Si-SVR model is higher than that of the Si-PLS model and the Si-SVR model, which is 2.27, exceeding 2.0, and the prediction accuracy is high, this method is preferable when the prediction accuracy of soil available K content is met. By summarizing the evaluation parameters of the prediction accuracy of soil nutrient content (N, P, and K) estimation models established by using four regression analysis methods, it can be concluded that the SVR model and the Si-SVR model are advantageous in modeling and prediction. However, the Si-SVR model achieves a higher prediction accuracy, which makes it the preferable method for the construction of a soil nutrient prediction model.
## 4 Conclusions  
The influence of the structural parameters of the TMFBG on its transmission spectrum was studied and calculated using OptiGrating. The tilt angle, grating length, and refractive index modulation depth will exert an impact on the transmission depth of the core modes and the number of cladding modes, while the grating period can determine the central wavelength of the light modes. The refractive index sensing can be completed by tracing the locations of the cutoff wavelength. The sensitivity of the TMFBG with the cladding diameter of 20 \(\upmu\)m was calculated to be 5 nm/RIU. The uniformly etched TMFBG with the cladding diameters of 30 \(\upmu\)m and 70 \(\upmu\)m have been prepared by the capillary protecting method. The refractive index sensing performance of the prepared TMFBGs has been experimentally demonstrated. The refractive index sensitivities for the unetched TFBG and etched TMFBG with the diameter of 30 \(\upmu\)m were compared. When the diameter increases from 125 \(\upmu\)m to 30 \(\upmu\)m, the sensitivity was improved from 0.964 nm/RIU to 6.368 nm/RIU for the higher-order cladding mode and from 0.294 nm/RIU to 2.353 nm/RIU for the lower-order cladding mode.  
Figure 9: Refractive index-sensing characteristic curve of uniformly etched TMFBGs with different diameters, showing (**a**) high-order cladding mode and (**b**) low-order cladding mode.  
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Author Contributions: Formal analysis, funding acquisition, methodology, investigation, and writing--original draft preparation, Y.Q.; conceptualization, writing--review and editing, supervision, and project administration, J.L. All authors have read and agreed to the published version of the manuscript.  
++  
* _[PERSON] et al. (2016)_ [PERSON]; [PERSON]; [PERSON] [PERSON] Influence of the cladding diameter of optical fiber on the simultaneous measurement of the refractive index and temperature of liquids using tilted fiber Bragg grating. _Jpn. J. Appl. Phys._**2016**, _55_, 068003. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON] Correlation between optical fibre diameter and characteristics of tilted fibre Bragg grating-assisted sensors. _Quantum Electron._**2021**, _51_, 1113-1117. [CrossRef]
* _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] TFBG Side Modes and Fresnel Reflection-Based Sensing System for Solution Concentration Measurement. _IEEE Sens. J._**2023**, _23_, 6901-6909. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Optimization of Cladding Diameter for Refractive Index Sensing in Tilted Fiber Bragg Gratings. _Sensors_**2022**, _22_, 2259. [CrossRef]  
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## 2 Modal LQR with Optimized Weighting Matrices
### Modal LQR
The state feedback gain matrix \(\mathbf{K_{m}}\) can be obtained by solving the Ricatti equation:  
\[\mathbf{K_{m}}=R^{-1}\mathbf{B_{m}}^{T}\mathbf{P} \tag{7}\]  
where \(\mathbf{P}\) is the solution of the Ricatti equation:  
\[\mathbf{A_{m}}^{T}\mathbf{P}+\mathbf{P}\mathbf{A_{m}}-\mathbf{P}\mathbf{B_{m }}R^{-1}\mathbf{B_{m}}^{T}\mathbf{P}+\mathbf{C_{m}}^{T}\mathbf{Q}\mathbf{C_{m} }=0 \tag{8}\]  
The control force for modal LQR control becomes \(u(t)=-\mathbf{K_{m}}\mathbf{z_{m}}(t)\). In order to convert the control input from the modal space to the configuration space, the gain matrix \(\mathbf{K_{m}}\) needs to be converted into \(\mathbf{K_{g}}\) through:  
\[\mathbf{K_{g}}=\mathbf{K_{m}}\left[ \begin{array}{cc}\mathbf{\Phi}^{-1}&0\\ 0&\mathbf{\Phi}^{-1}\end{array} \right] \tag{9}\]  
Accordingly, the state feedback gain matrix \(\mathbf{K_{g}}\) can be used to calculate the control force for the structure by \(u(t)=-\mathbf{K_{g}}\mathbf{z}(t)\) where \(\mathbf{z}\) is the states in the configuration space which contains the displacement and velocity vectors, i.e., \(\dot{\mathbf{z}}(t)=\left[ \begin{array}{cc}\mathbf{x}(t)&\dot{\mathbf{x}}(t )\end{array} \right]^{T}\).
## 3 Results and Discussion  
Experimental models provide insights by simulating diverse conditions; however, the real-time monitoring of internal dam conditions is challenging, as it is dependent on measurements. Although real-time measurements can reveal physical changes in seepage and deformation, reliability may be compromised if seepage or leakage occurs at  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \(\mathbf{\theta_{s}}\) & \(\mathbf{\theta_{r}}\) & \(\mathbf{\alpha}\) & \(\mathbf{n}\) & \(\mathbf{m(1-1/n)}\) \\ \hline Fill zone & 0.46 & 0.034 & 1.60 & 1.37 & 0.270 \\ Core & 0.38 & 0.007 & 0.80 & 1.09 & 0.083 \\ Filter & 0.43 & 0.045 & 14.50 & 2.68 & 0.627 \\ \hline \hline \end{tabular} \(\theta_{s}\) = residual water content; \(\theta_{r}\) = saturated water content; \(\alpha,n,m\) = curve fitting parameters.  
\end{table}
Table 2: Parameters of the soil–water characteristic curve used in the analysis.  
Figure 6: Boundary conditions of the analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Water Level** & **Prototype Water Level (m)** & **Model Water Level (cm)** \\ \hline Flood water level (S1: FWL) & 14.6 & 44.0 \\ Dam crest water level (S2: DWL) & 15.2 & 51.0 \\ Overtopping water level (S3: OWL) & 16.2 & 54.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Water level conditions.  
points in the dam that are not being measured. Therefore, real-time measurement analysis should be combined with model observational results for improving reliability. Numerical analysis offers visual information on structural changes to aid the comprehension of the phenomenon. However, this approach is limited because it cannot reflect dynamic environmental changes. Therefore, to leverage the strengths of each method and to enhance reliability, the review of the infiltration and displacement behavior during water level changes was conducted through numerical analysis, model observation, and real-time measurements in parallel.
## 2 Modeling of Dynamic Rotational Error for Spindle-Rolling Bearing System
### Nonlinear Bearing Model  
The dynamic supporting stiffness and time-varying bearing force are important parameters in modeling the nonlinear behaviors of rolling bearings. During operation, the dynamic supporting stiffness of the SRB system is nonlinear according to the Hertz contact theory (the Hertz contact theory illustrates the distribution rule of local stress and strain generated by the contact of two objects under compression), while the bearing force is time-variable due to the machining error of the bearing rings and rolling elements.
## 2 Materials and Methods
### Method
#### 2.3.3 Trend Analysis of NDVI  
NDVI is suitable for characterizing the growth and spatial distribution of vegetation within our study area [82; 83]. We extracted the annual average NDVI value for each pixel within the study areas through the GEE platform [84; 85]. However, when conducting  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  
a univariate analysis of NDVI, there are several considerations that must be taken into account. NDVI may exhibit anomalous values in non-vegetated regions. Furthermore, water bodies result in negative NDVI values. Therefore, when performing an isolated NDVI analysis, croplands, water bodies, barren areas, and impervious areas were excluded from the study areas. This was done to avoid potentially skewed results arising from these land cover types which do not accurately represent vegetation dynamics. The time series analysis of NDVI was conducted pixel by pixel using the Theil-Sen median trend analysis [86; 87] and Mann-Kendall trend test [88; 89]. The Theil-Sen Median method is a robust non-parametric statistical trend calculation method. This method boasts high computational efficiency, is insensitive to measurement errors and clustered data, and is apt for the trend analysis of long-term series data. The Mann-Kendall test is a non-parametric time series trend test method, which does not require measurement values to conform to a normal distribution and is unaffected by missing values and outliers. It is suitable for the trend significance test of long-time series data. Both methods are vital for determining the trend of long-time series data and can be effectively combined. This combination has gradually been used to analyze and reflect the trend change of each pixel in the long-time series [84; 90; 91; 92; 93].  
The calculation formula of NDVI is:  
\[NDVI=\frac{NIR-R}{NIR+R}\]  
where NIR refers to near-infrared band value. R represents red band value.  
The calculation formula of the Theil-Sen median method is:  
\[\beta=Median\left(\frac{NDVI_{j}-NDVI_{i}}{j-i}\right),\forall j>i\]  
where _Median_() represents the median value, and _NDVI_ and _NDVI_ represent the _NDVI_ values in years \(i\) and \(j\). If \(\beta>0\), the _NDVI_ is on an upward trend; otherwise, it is a downward trend.  
The calculation formula of the Mann-Kendall test is as follows. The calculation formula of test statistic \(S\) is:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sgn(NDVI_{j}-NDVI_{i})\]  
where _sgn_() is a symbolic function, and the calculation formula is:  
\[sgn\left(NDVI_{j}-NDVI_{i}\right)=\begin{cases}+1\;NDVI_{j}-NDVI_{i}>0\\ 0\;NDVI_{j}-NDVI_{i}=0\\ -1\;NDVI_{j}-NDVI_{i}<0\end{cases}\]  
The calculation formula of test statistic \(Z\) is:  
\[Z=\begin{cases}\frac{S}{\sqrt{Var(S)}}&(S>0)\\ 0&(S=0)\\ \frac{S+1}{\sqrt{Var(S)}}&(S<0)\end{cases}\]  
where the formula for calculating _Var_ is:  
\[Var(S)=\frac{n(n-1)(2n+15)}{18}\]  
where \(n\) is the length of data in the sequence.
## 3 Parametric Analysis
Figure 3 depicts \(\varepsilon_{eff}\), \(\varepsilon_{r,\alpha}\) and \(E_{rel}\) as a function of \(r\) by considering the dielectric constant of the substrate, \(\varepsilon_{r}\), as the parameter. It can be appreciated that \(E_{rel}\) grows with \(r\), which is as expected on account of the increasing magnitude of the electric field in the substrate/air interface as \(r\) increases. Moreover, the relative error increases with the dielectric constant of the substrate. That is, a higher dielectric constant contrast generates stronger refraction of the electric field lines in the substrate/air interface, thereby magnifying the asymmetry between the electric field lines at both sides of the metal layer. Note that in the limit when \(\varepsilon_{r}\to 1\), there is perfect symmetry with regard to the metal plane and such plane is a perfect magnetic wall. Nevertheless, according to Figure 3, the relative error in the worst considered case, \(r=20\) and \(\varepsilon_{r}=10.2\), is as small as \(E_{rel}=0.43\%\). Moreover, for reasonable  
Figure 2: Cross sectional view of the slot structure (**a**), equivalent coplanar waveguide (CPW) structure (**b**) and symmetric CPW structure, with the metal layer equally covered at both sides (**c**). The vertical symmetry planes in the CPWs of (**b**,**c**) are perfect magnetic walls for the fundamental (even) CPW mode. The horizontal symmetry plane in (**c**) is a perfect magnetic wall, whereas the horizontal plane in (**a**,**b**) are quasi-magnetic walls, provided \(E_{rel}\) is small. DB-DGS resonators and substrates, the ratio between the slot width and the substrate height is expected to be smaller than one (\(r<1\)), which is a range where the relative error is found to be negligible according to Figure 3c. Consequently, it can be concluded that the horizontal plane of the CPW metal can be considered to be a magnetic wall to a very good approximation and this extends to the slot structure of Figure 2a as far as the behavior of both structures is very similar. Figure 3: \(Cont.\)  
It is interesting to mention that, according to Figure 3b, as \(r\) decreases, the equivalent dielectric constant, \(\varepsilon_{r,eq}\), approaches that of the substrate, \(\varepsilon_{r}\). The reason is that in the limit when \(r\to 0\), the substrate satisfies the semi-infinite approximation. Moreover, for a given value of \(r\), the lower the value of \(\varepsilon_{r}\), the closer the value of \(\varepsilon_{r,eq}\) to \(\varepsilon_{r}\). Note that in the limit when \(\varepsilon_{r}\to 1\) (air), the equivalent dielectric constant must satisfy \(\varepsilon_{r,eq}\to\varepsilon_{r}=1\), regardless of the value of \(r\). In the next section, which is devoted to validation by means of electromagnetic simulation of various DB-DCS-loaded microstrip line based sensors and the analysis of the previous section, it will be demonstrated that the agreement between the equivalent dielectric constant of the considered substrates and the results given in Figure 3b is good.
## 1 Introduction
### Contributions and Structure of the Paper  
This paper proposes a new eigenvalue-based centralized cooperative spectrum sensing approach for OFDMA and other wideband signals. In this approach, the energy detection in [9, 10] is replaced by a technique based on the eigenvalues of the received signal covariance matrix. For general wideband signals (This eigenvalue fusion scheme was briefly discussed in a shorter paper, in Portuguese, which was presented in the 30 th Brazilian Telecommunications Symposium (SBrT'12), under the title \"Sensoriamento Espectral Cooperativo Baseado em Autovalores para Sinais de Banda Larga\" (Eigenvalue-Based Cooperative Spectrum Sensing for Wideband Signals) [20]), the technique aims at locating spectral holes to the level of sub-bands. An adapted version of the technique that addresses the problem of sensing OFDMA signals to the level of subchannels is also proposed. In this case, the interest is to jointly sense all carriers of a subchannel, allowing for the opportunist occupancy of a vacant subchannel by one CR or allowing for the division of a vacant subchannel to multiple CRs.  
Three forms of data fusion were addressed: (i) the fusion of samples from each CR, but in the frequency domain; (ii) the fusion of binary decisions made by all cooperating CRs and (iii) the new fusion approach in which the eigenvalues estimated by each CR are combined at the FC.  
Simulation results show that the new eigenvalue fusion scheme can overcome the performance of the sample and decision fusion for detection of unoccupied sub-bands in a general wideband signal and for detection of unoccupied subchannels in an OFDMA signal.  
The eigenvalue fusion scheme demands less transmitted data to the FC when compared with sample fusion schemes with similar performances. However, the amount of data transmitted to the FC in eigenvalue fusion and sample fusion can be, by far, greater than those in decision fusion schemes with similar system parameters.  
The remaining of the paper is organized as follows. Section 2 briefly reviews the cooperative centralized eigenvalue-based spectrum sensing technique. In Section 3, the proposed eigenvalue fusion and other fusion schemes are considered for the detection of general wideband signals. In Section 4, the eigenvalue fusion and other fusion schemes are considered for the detection of OFDMA signals. Numerical results and their interpretations are presented in Section 5. Section 6 deals with the tradeoff between the complexity of the radios and the volume of data sent to the fusion center. Conclusions and suggestions for new research are in Section 7.