esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data
###### Abstract
To better understand the internal CVIs for categorical data, we investigate five well-known internal CVIs for categorical data clustering validation evaluation, i.e., the information entropy function (\(E\)) [30], the \(k\)-modes objective function (\(F\)) [31], the category utility function (\(CU\)) [32], the objective function of clustering with slope (\(Clope_{r}\)) [33], and the objective function of categorical data clustering with subjective factors (\(R\)) [34]. We attempt to reveal the nature of the five internal CVIs by investigating the compactness measures and the separation measures, and discuss whether assumptions of separation exist and can be substituted for the separation measures. Meanwhile, we theoretically analyze whether the compactness measures for categorical data show monotonicity in certain circumstances, and the role of separation measures (assumptions) in neutralizing the monotonicity. Then, to enhance the internal measurement, we propose a new internal CVI that has improved performance, namely, the clustering utility based on the averaged information gain of isolating each cluster (\(CUBAGE\)). \(CUBAGE\) uses the proposed averaged information gain of isolating each cluster (\(AGE\)) to measure the separation, and the reciprocal entropy of the dataset conditioned on the partition to measure the compactness. The paper is organized into six sections. Section 2 is the related work. Section 3 provides in-depth analysis and discussions of five CVIs on the first two issues. In Section 4, we present the proposed internal CVI. Section 5 presents our experimental results and detailed discussion. Finally, we state our conclusions in Section 6.
## 5 Conclusions  
The Middle Route of the South-North Water Diversion Project is the \"lifeline\" of water supply for Beijing, Tianjin, Hebei and Henan, and the safety monitoring is of great significance. In this paper, we used the Sentinel-1A image dataset from October 2016 to June 2017 to obtain the overall deformation results of the high-fill section of Wenzhuang Village in Ye County of The Middle Route of the South-North Water Diversion Project using the time-series InSAR technique and found that the slope of about 1 km long on the east bank of the canal had obvious deformation with the maximum deformation of 36 mm. More than 87% of RMSE values are less than \(\pm\)2 mm and the correlation coefficient is 0.96, which is highly consistent with the deformation trend and value, fully proving the feasibility and effectiveness of InSAR technology for the safety monitoring of the high-fill canal of the South-North Water Diversion Project. Further, using PS-InSAR technology to obtain the deformation results of the canal between October 2016 and February 2018, it was found that settlement also occurred on the west bank with time, and the deformation volume was larger on the east bank compared with the west bank. This technique can not only monitor the extent and rate of settlement in the South-North Water Diversion Canal but also accurately identify the sequence of settlement on the east and west banks, which can play an important role in monitoring the safety of large-scale linear water conservancy projects.  
[PERSON] and [PERSON] conceived and designed the study; SAR data was processed by [PERSON]; [PERSON] wrote the paper; [PERSON] supervised the work; [PERSON] and [PERSON] contributed to correct the language and layout of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No.41901411), Henan Provincial Science and Technology Research Project (No.212102310052), Training Plan for Young Backbone Teachers of Colleges and Universities in Henan Province (No.2021 GJS073), Henan Province Young Talent Support Project (No.2020 HYTP010), Open Foundation of Key Laboratory of Ecological Environment Protection of Space Information Application of Henan (22-FW-07-0107), and Joint Fund of Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains, Henan Province and Key Laboratory of Spatiotemporal Perception and Intelligent Processing, Ministry of Natural Resources (No.211103), Major Science and Technology Special Projects in Henan Province (No.201300311400).  
Not applicable.  
Data Availability Statement: The Sentinel-1 data used in this study is free and openly accessible to the public.  
The authors thank the anonymous reviews for their constructive comments and suggestions.  
The authors declare no conflict of interest.
# Optimal Control Strategy for Floating Offshore Wind Turbines Based on Grey Wolf Optimizer
## 3 The Control System  
The controller's primary goals are to maintain power production at the rated value when wind speeds exceed the rated threshold and to maximize power generation when wind speeds exceed the cut-in threshold. Figure 2 illustrates this operating approach.  
To manage the generated power, the generator torque has to be adjusted. The generator requires six control zones to achieve this, as shown in Figure 2:  
* Region 1: the generator speed is below the starting value (ugen0), the generator torque is zero, and no power is collected. Instead, the wind is utilized to speed the rotor during the start-up process.
* Region 2: the generator torque is proportional to the square of the generator speed, which is between the transitional generator speed between R12 and R2 (\(\omega\)gen1) and the transitional generator speed between R2 and R23 (\(\omega\)gen2). The control technique in this area should employ an MPPT method to optimize the produced power. The torque expression can be expressed as follows: \[T_{em}=k_{mppt}{\omega_{gen}}^{2}\] (12)
* where \(k_{mppt}\) is the MPPT gain.
* Region 12: when linear transit is provided between R1 and R12, the generator speed is linearly limited between \(\omega_{gen0}\) and \(\omega_{gen1}\). \[T_{em}=S_{1}\big{(}\omega_{gen}-\omega_{gen0}\big{)}\] (13) \[S_{1}=\frac{k_{mppt}{\omega_{gen1}}^{2}}{\omega_{gen1}-\omega_{gen0}}\] (14)
* Region 3: because the power must be kept constant at the rated generator speed (\(\omega_{genRated}\)), the torque is inversely proportional to the generator speed.  
Figure 2: Typical wind turbine power evolution versus wind speed.  
\[T_{em}=\frac{P_{rated}}{\omega_{gen}} \tag{15}\]
* where \(P_{rated}\) is the rated generator power.
* Region 23: the generator speed is linearly limited to the tip -speed of rated power between \(\omega_{gen2}\) and \(\omega_{genRated}\). \[T_{em}=S_{2}\big{(}\omega_{gen}-\omega_{gen2}\big{)}\] (16) \[S_{2}=\frac{P_{rated}/\omega_{genRated}}{\omega_{genRated}-\omega_{gen2}}\] (17)
* Region 4: if the wind speed exceeds the cut-off value (\(\omega_{\text{cut off}}\)), the wind turbine is deactivated in order to safeguard itself.  
Concentrating on Region 3, when the power coefficient (Cp) changes with the blade pitch angle (\(\beta\)), the wind turbine blades can achieve aerodynamic productivity. In actuality, regulating the blade pitch angle in Region 3 for FOWTs has two unique purposes: reducing floating platform movements and regulating power [25; 26]. Most commercial FOWTs use a typical PI controller to reduce the error between the rotor speed and the rated value [27].
# Perpendicular Electrical Conductivity in the Topside Ionosphere Derived from Swarm Measurements
## 3 Results
### Seasonal Variation of Electrical Conductivity Perpendicular to the Geomagnetic Field
Apart from the values themselves, we report the presence of seasonally varying features of \(\sigma_{P}\) and \(\sigma_{H}\). Concerning a possible connection with the semiannual anomaly, the Northern Hemisphere shows features that are similar to those highlighted at low latitudes by Figures 5 and 6, with an intensification of conductivities for the March equinox, which can be related to the larger amplitudes of the semiannual anomaly characterising this period. Unfortunately, the same cannot be said for the Southern Hemisphere, for which the intensification of conductivities is seen around the September equinox and more centred in the polar cap. This is now an open question, but we have to remark that at high latitudes, the primary source of ionisation is not the solar EUV radiation; the plasma dynamics, specifically the transport processes, at these latitudes play a key role and cannot be neglected. Additionally, it is important to point out that studies of the semiannual anomaly in the topside ionosphere using in situ satellite measurements showed that its hemispheric dependence appears contradictory [102; 103]. Figure 7: _Cont._  
Figure 8: _Cont_. Figure 7: Seasonal maps (from top to bottom: winter, summer, spring, autumn) in QD-MLT coordinates of Pedersen electrical conductivity, \(\sigma_{p}\), in polar stereographic projection representation in the Northern (left column) and Southern (right column) Hemispheres.
# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 5 Assessment of the Impact of Biofouling and Maintenance Optimization  
As mentioned previously, as time elapses, the ship's frictional resistance increases due to the accumulation of biofouling on hull and propeller surfaces. This results in an increase in the thrust that must be delivered by the propulsion system to maintain a given ship speed, which leads to increased shaft power demand. The main assumption behind this reasoning is that, apart from the fouling condition of the vessel, the other conditions affecting the power demand remain the same. The goal of the data-driven modeling approach followed in the present study was to satisfy this assumption. Thus, two methods were proposed to assess the fouling condition using Model-1 and Model-2, respectively. Moreover, the power increase indicator (PI) was used, which is defined as follows:  
\[PI=100\frac{P_{f}-P_{c}}{P_{c}} \tag{19}\]  
where \(P_{c}\) is the SHP in clean vessel condition and \(P_{f}\) is the SHP in fouled vessel condition, with both values corresponding to the same operational conditions.
# A Novel Optimization Framework to Replicate the Vibro-Acoustics Response of an Aircraft Fuselage
## 2 Problem Description  
This work can be split in three main parts.  
The first part comprises the CAD/FEM modeling of an aircraft fuselage mock-up in order to build up a simplified vibro-acoustic FEM model. Such a model was used to perform the vibro-acoustic analyses of the fuselage when loaded either with the sound pressure emitted by the engines, or with the sound pressure emulated by different sets of simple acoustic sources (monopoles).  
The second part comprises the set-up of the MDO procedure, with the aim of characterizing the previously given sets of acoustic sources in order to reproduce, in a simplified manner, the reference pressure field. Moreover, such a procedure was performed two times in order to calculate, for a given configuration of acoustic sources, the results of two different configurations of microphones (two separate sets of simulations were run).  
The last part comprises the presentation of results for the two simulated cases and their comparisons with the reference data. A discussion on such results is also provided and recommendations for further improvements are added.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 4 Delay Constrained Offloading  
The tasks of an application are represented by the directed acyclic graph \(G=(V,C)\). \(V=0,1,2,\dots,m\) is the finite node set that represents the task offloading nodes, and \(C\) is the edge set that denotes the cost of task offloading. Figure 5 shows the task execution flow for collaborative offloading decision. \(S\) is the starting point of the application and \(D\) is the task destination terminal. For simplicity, take vehicle \(m\) as the requestor of the application as an example. An application contains \(n+2\) tasks. Except for the initial task and the last task, all the platoon members and the MEC server have the opportunity to execute the other \(n\) tasks.  
Denote \(x\) and \(y\) are adjacent node numbers of an edge, \(x,\ y\in V\) and \(x\
eq y\). Each edge corresponds to a task offloading decision and the weights of the edges \(c^{k}_{x,y}\) are non-negative value, namely \(c^{k}_{x,y}\geq 0\). This weight value stands for the cost of offloading the task and the time it takes to execute the task. Specifically, if the weight value is considered to be a cost price and the task \(k\) needs to be offloaded from \(x\) to \(y\) for execution, we obtain the weight \(c^{k}_{x,y}=b^{y}_{k}\). Additionally, if the weight value is considered to be a time delay, we obtain the weight value \(c^{k}_{x,y}=t^{y}_{k}+t^{k}_{x,y}\) that is the sum of the computation time and the transmission time of the task \(k\). Therefore, two directed acyclic graphs with respect to the cost price and time delay can be obtained.  
Under this framework, we then transform the optimal task offloading decision into a shortest path problem to find the path with minimum cost between nodes \(S\) and \(D\). It is constrained by the deadline time (\(T_{d}\)) of the tasks, and the time delay of the path should be less than or equal to \(T_{d}\). If the time delay of a path \(p\) satisfies the constrained conditions, \(p\) is an appropriate path. A path \(p^{*}\) is an optimal path with the minimum cost among all appropriate paths. We mathematically formulate the problem as a constrained shortest path problem by  
\[\begin{split}\min_{p\in P}& b(p)=\sum\limits_{k=1}^{n}b_{k}^{y}\\ \text{s.t.}&(\text{C1}):d(p)=\sum\limits_{k=1}^{n+ 1}\left(t_{x,y}^{k}+t_{k}^{y}\right)\leq T_{d}\\ &(\text{C2}):f_{\
u_{1}}<f_{0},\ f_{\
u_{2}}<f_{0}\end{split} \tag{15}\]  
where \(p\) is the path from node \(S\) to node \(D\), and \(P\) is the set of all possible paths, \(p\in P\). (C1) is to ensure that the time consumed in executing the tasks is within the deadline. (C2) indicates that any platoon member provides less computational resources than the MEC server. Since each task has \(m+1\) offloading decision options, there are \((m+1)^{n}\) possible options for the offloading strategy.
## 3 Results
### Nuclear Sequences  
The nuclear ITS1, 5.8S, and ITS2 (650 bp) showed a lower genotypic variability, as compared to the cpDNA haplotype variation, and no length mutations (indels) were detected (Supplementary File S2). Six genotypes were detected (codes N1-N6; Figure 2a), one of which (N2) was widely distributed across all populations, with an overall frequency of 75% (from approx. 35% to 100% according to population). All mutations occur in the ITS2 region, either as individual mutations, as in the cases of genotypes N2 and N5, or as paralogues, in genotypes N1, N3, N4, and N6 (Figure 2b). Sardinia has the largest genotypic diversity, with four genotypes (N3, N4, N5, and N6), followed by Corsica, with three, represented also in Sardinia (N2, N4, and N6) and the Vesuvius area (N1, N2, and N3). In Sicily only two genotypes occur (N2 and N4). Private genotypes occur in Sardinia, in the Mount Limbara population (N5) and in the Vesuvius population (N1 and N3). The four detected paralogous genotypes may derive from crosses between unambiguous genotypes (e.g., see Figure 2c) and were not subjected to genealogical analysis.  
Figure 2: (**a**) Distribution map of the populations of _Genista ettenesis_ and relative frequency of the ITS genotypes (nrDNA) (populations labels: B = Mount Baraca, F = Fornazzo, G = Genna Silana, L = Mount Limbara, N = Villagrande Strisaili, Pf = Serra Fiumorbu, S = Solenzara, Ss = road between Sari and Solenzara, T = Solaro, V = Mount Vesuvius); (**b**) genotype and relative SNPs (see Supplemental File S2 for the alignment, GenBank accessions N1–N6: OP794491–OP794496); (**c**) possible crosses between genotypes; (**d**) reconstructed genealogy of unambiguous genotypes.  
In the outgroup, two genotypes were observed and correlated with the same specimens for the same haplotypes (GF_NA in three and GF_NB in two samples). The MP genealogy reconstruction for the two unambiguous genotypes (Figure 2d), both with and without the outgroup, indicates that the ancestral genotype is the widespread N2. As the investigated nuclear sequences included comparatively little information when compared to the cpDNA markers and given the paralogues did add a further measure of uncertainty, they were not further employed, and the following investigations were carried out exclusively on cpDNA sequences.
## 3 Techno-Economic Analysis at 2060
### Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c}  & **Baseline 2030** & **CC 2060** & **CC + TEC(I) 2060** & **CC + TCC(I) 2060** & **CC + TCC(I) 2060** & **TEC(I) 2060** & **CC + TCC(I) 2060** \\ \hline Water depth (m) & \(-\)463 & \(-\)463 & \(-\)463 & \(-\)463 & \(-\)463 & \(-\)463 \\ Distance from shore (km) & 40 & 40 & 40 & 40 & 40 & 40 \\ Distance from grid (km) & 44.9 & 44.9 & 44.9 & 44.9 & 44.9 & 44.9 \\
**Mean wind speed at hub height (m)** & **8.05** & **7.80** & **7.80** & **8.10** & **8.36** & **8.10** \\ Wind farm capacity (MW) & 1000 & 1000 & 1000 & 2000 & 2000 & 2000 \\ Number of turbines & 100 & 100 & 100 & 100 & 100 & 100 \\ Turbine rate (MW) & 10 & 10 & 10 & 20 & 20 & 20 \\ Hub height (m) & 119 & 119 & 119 & 168 & 168 & 168 \\ Terrestrial cable cost (M/Km) & 0.25 & 0.25 & 0.25 & 0.25 & 0.25 & 0.25 \\ Offshore cable cost (M/M/W) & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Total grid-connection costs (M) & 41.23 & 41.23 & 41.23 & 41.23 & 41.23 & 41.23 \\ CAPEX (excluding cable costs) (Mc/MW) & 2.30 & 2.30 & 2.07 & 2.07 & 2.07 & 1.50 \\
**Total CAPEX (MM/MW)** & **2.34** & **2.34** & **2.11** & **2.09** & **2.09** & **1.52** \\
**OPEX (MM/MW)** & **0.125** & **0.125** & **0.119** & **0.119** & **0.119** & **0.063** \\
**WACC (x)** & **7.00** & **7.00** & **5.00** & **3.75** & **3.75** & **2.50** \\ Farm lifecycle (year) & 30 & 30 & 30 & 30 & 30 & 30 \\ Total production efficiency (\%) & 83.3 & 83.3 & 83.3 & 83.3 & 83.3 & 83.3 \\ Gross annual production (M/W) & 3,6/4/2000 & 3,4/5/4000 & 3,4/5/4,000 & 7,26/2000 & 7,5/000,000 & 7,262,000 \\ Net annual production (MW) & 2,99/4/23 & 2,8/7/162 & 2,8/7/162 & 6,0/4/143 & 6,295,291 & 6,0/4/143 \\ Specific productivity (M/MW) & 2999 & 2876 & 2876 & 3024 & 3148 & 3024 \\ Capacity factor (\%) & 34.2 & 32.8 & 32.8 & 34.5 & 35.9 & 34.5 \\
**LC6 (\%/MW)** & **104.6** & **109.1** & **89.0** & **78.1** & **75.0** & **44.6** \\
**LC6E variation referring to baseline (\%)** & / & **+4.3** & **-14.9\%** & **-25.4\%** & **-28.3\%** & **-57.3\%** \\ \end{tabular}
\end{table} 
Table 6: Case study 2 (Sicily); net wind speed variation: \(-\)0.25 m/s. (Variable parameters in bold).
## 2 System Model  
The ANTs are deployed at the ceiling with same height, high vertical dilution of precision makes the system cannot provide reliable vertical positioning results [29], which is usually obtained by other sensors [30].  
Therefore, for the system model, we assumed that ANTs are deployed at the ceiling and the height of UE is already known by other sensors, and only two-dimensional horizontal positioning is considered. The system model of the indoor distribution system based UE positioning is shown in Figure 1. We consider a UE position estimation problem using an indoor distribution system containing \(M\) ANTs. For the system shown in Figure 1, \(M\) is equal to 4. For each positioning process, in order to simplify mathematical representations, ANTs are sorted by the distance to the UE. The nearest ANT is numbered 1, and the farthest ANT is numbered \(M\). UE could obtain \(M\,-\,1\) TDOA measurements and one TOA measurement from the nearest ANT. In Figure 1, the nearest ANT to UE is the ANT 1 and the farthest is the ANT 4.  
Figure 1: The system model of user equipment (UE) indoor positioning based on the indoor distribution system.  
In the following description, the position of UE is denoted by \(u=\left(x,y\right)^{T}\). The system contains \(M\) ANTs locating at \(a_{i}=\left(x_{i},y_{i}\right)^{T}\), \(i=1,\ 2,\ 3,\ldots,\ M\).  
The distance from ANT \(i\) to UE is expressed as  
\[r_{i}=\parallel a_{i}-u\parallel=\sqrt{\left(x_{i}-x\right)^{2}+\left(y_{i}- y\right)^{2}},\ \ i=1,2,3,\ldots,M. \tag{1}\]  
The distance difference from ANT 1 to UE and ANT \(i\) to UE is expressed as:  
\[r_{i,1}=\left[\parallel a_{1}-u\parallel-\parallel a_{i}-u\parallel\right] =r_{1}-r_{i}. \tag{2}\]  
Considering the TDOA ranging error \(e_{i,1}\), TDOA measurement from ANT 1 and ANT \(i\) is represented as:  
\[ct_{i,1}=d_{i,1}=r_{i,1}+e_{i,1},\ \ i=2,3,4,\ldots,M \tag{3}\]  
where \(t_{i,1}\) is TDOA measurement and \(c\) is the speed of light, \(d_{i,1}\) is the distance difference calculated by TDOA measurement, \(r_{i,1}\) is the real distance difference and \(e_{i,1}\) is the ranging error caused by TDOA measurement error.  
TOA measurement of ANT 1 to UE is expressed as:  
\[ct_{1}=d_{1}=r_{1}+e_{1} \tag{4}\]  
where \(t_{1}\) is TOA measurement, \(d_{1}\) is the distance calculated by TOA measurement, \(r_{1}\) is the real distance from ANT 1 to UE and \(e_{1}\) is the ranging error caused by TOA measurement error.  
For brevity, we denote \(e_{1}\) as the TDOA ranging error and \(e_{i}\) for \(i=2,3,\ldots,M\) as TDOA ranging errors between the signal from ANT \(i\) and signal from ANT 1, which is denoted as \(e_{i,1}\) in the above formula.  
In a system with \(M\) ANTs, an equation system containing TDOA and TOA measurements is obtained as:  
\[\left\{\begin{array}{c}d_{1}\ =r_{1}\ \ \ +e_{1}\\ d_{2,1}=r_{1}-r_{2}+e_{2}\\ d_{3,1}=r_{1}-r_{3}+e_{3}\\ \vdots\\ d_{M,1}=r_{1}-r_{M}+e_{M}\end{array}\right.. \tag{5}\]
## 2 Theoretical Research on Indirect Measurement Method Based on Vehicle-Bridge Coupling
### Wavelet Analysis  
At the initial stage of structural damage or when the damage is relatively small, the changes of structural response caused by the damage are also very small. In order to highlight the damage information in vehicle response, wavelet analysis is applied in dynamic response signal analysis. As a new method in the field of signal processing, wavelet analysis can effectively identify the subtle differences in signals. It carries out multi-scale analysis on the signal in time domain and frequency domain, so that the signal can be time-refined at high frequency and frequency-refined at low frequency. Therefore, it is widely used in the field of signal detection.  
Wavelet transforms are divided into continuous wavelet transforms and discrete wavelet transforms, continuous wavelet transform (CWT) can decompose the original signal into a two-dimensional signal; by adjusting the scale factor \(m\) and translation factor \(n\), the original signal \(f(s)\) is analyzed to obtain the signal local detection information. The original signal \(f(s)\) is continuously transformed at the scale factor m and the translation factor n, because the adjustment factor has continuity, so the wavelet transformation coefficient will generate a large amount of redundant data, which is not conducive to signal processing, so a discrete wavelet transform (DWT) is introduced. The scale factor m and translation factor n are generally calculated as follows:  
\[m=m_{0}^{j} \tag{21}\]  
\[n=km_{0}^{j}n_{0} \tag{22}\]  
where \(k\) and \(j\) are integers and \(m_{0}\
eq 1\).  
The discrete wavelet transform function is:  
\[WT_{f}(m,n)=|m_{0}|^{-\frac{j}{2}}\int_{-\infty}^{+\infty}f(s)\phi^{*}{}_{m,n} \Big{(}m_{0}^{-j}s-kn_{0}\Big{)}ds \tag{23}\]  
Discrete wavelet transform effectively reduces the redundant data generated by continuous wavelet transform and improves the calculation efficiency and accuracy. In this paper, discrete wavelet transform is used to analyze the vehicle dynamic response data and accurately find the subtle changes in the data, which is conducive to bridge damage identification.