esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Influence of Cable Length on the Overvoltage Distribution of Permanent Magnet Motor
### The Influence of Cable Length on Inter-Turn Overvoltage Distribution  
Figure 13 below shows the maximum inter-turn voltage variation in the winding with the change in cable length.  
It can be seen from Figure 13 that the inter-turn voltage of the head coil decreases with the increase in the cable length before the cable length is 30 m. In the range of 30 m to 150 m in length, the inter-turn voltage of the head coil increases with the increase in the cable length. In the length of 150 m to 500 m, the inter-turn voltage of the head coil decreases again with the increase in the cable length.  
Figure 11: The maximum ground voltage of the first-turn conductor under different cable lengths.  
Figure 12: Characteristics of the maximum voltage distribution to ground of the windings for different cable lengths.  
Figure 14 shows the inter-turn voltage distribution characteristics of adjacent conductors in the stator winding of the motor under different cable lengths.  
When the cable length is in the range of 50-100 m, the maximum inter-turn voltage reaches the maximum value, which is located at the head coil, and the amplitude is about 185 V. The inter-turn voltage of the end coil is small, and the lowest is close to 0 V.
## 1 Introduction
TLS has been used in numerous works to study landslides [42; 43; 44], cliff erosion [45; 46; 47] and rock formation discontinuities [48; 49; 50]. Despite the demonstrated capabilities of these technologies and their high-resolution imaging aptitude, there are very limited applications in highly visited areas or areas of high value that combine active tectonics and frequent instability phenomena. In addition, there are few applications that exploit outcomes of these techniques regarding slope stability to improve risk mitigation and enhance development. Given that, in many cases, decisions on the exploitation of coastal zones are made with a poor understanding of the evolutionary processes and functioning of the coastal environments, in the case of high-value and highly active areas, it is considered crucial to gain an improved understanding of dangerous instability phenomena. Any sustainable and responsible effort of development on these types of zones requires a deep understanding of the complexity of geomorphic processes and the roles of influencing and instability-triggering factors [51; 52; 53], including tectonic and earthquake dynamics, mass wasting, erosion, deposition, land-use changes, human intervention, and others. In this context, the aim of this work is to exploit innovative techniques such as UAS-aided photogrammetry and TLS to improve our understanding of the processes taking place, segments of the coastal zone of the Ionian Islands in Greece and, in particular, the island of Lefkada. The two study sites are situated in an earthquake-prone area, with frequent strong earthquakes leading to various mass-movement and slope failure phenomena, enhanced by active tectonics, but also subject to high-intensity storms (including Medicanes) that have also contributed to the high landslide risk. The present study aims to apply the two techniques at the Egremnoi and Porto Katsiki beaches, two highly visited tourist attractions both characterized by noteworthy economic and ecological value. The study aims to determine the zones of higher risk through the two applications and discuss the outcomes in relation to local development, considering that Lefkada is a typical example of a Mediterranean busy coastal zone, home to hundreds of thousands of visitors each year. The study is organized as follows. First, we present the broader area and the study site, in terms of its geodynamic characteristics, then we describe the methodology for the UAS and the TLS applications, followed by the results. Then we discuss the practical implications of the findings and describe the most important conclusions in the last section.
# Asymptotic Performance Analysis of the MUSIC Algorithm for Direction-of-Arrival Estimation
## 2 Notation  
\(L\): The number of snapshots  
\(M\): The number of antennas  
\(n\): The index of incident signal (\(n=1,\ldots,d\))  
\((\cdot)^{T}\): Transpose of a matrix  
\((\cdot)^{H}\): Hermitian of a matrix  
\((\cdot)^{-1}\): Inverse matrix  
\(E\)\((\cdot)\): Statistical expectation  
\(\theta_{n}^{(0)}\): The \(n\)-th true azimuth of \(d\) incident signals (\(n=1,\ldots,d\))  
\(\hat{\theta}_{n}^{(1)}\): The \(n\)-th azimuth estimate in (47)  
\(\delta\theta_{n}^{(1)}\): The difference between \(\hat{\theta}_{n}^{(1)}\) and \(\theta_{n}^{(0)}\)  
\(\hat{\theta}_{n}^{(2)}\): The \(n\)-th azimuth estimate in (38)  
\(\delta\theta_{n}^{(2)}\): The difference between \(\hat{\theta}_{n}^{(2)}\) and \(\theta_{n}^{(0)}\)  
\(\hat{\theta}_{n}^{(3)}\): The \(n\)-th azimuth estimate in (39)  
\(\delta\theta_{n}^{(3)}\): The difference between \(\hat{\theta}_{n}^{(3)}\) and \(\theta_{n}^{(0)}\)  
\(\mathbf{n}\)\((t_{i})\): zero-mean Gaussian random vector representing the noise on the antenna  
\(\Lambda\): The wavelength of a incident signal  
\(\mathbf{k}\): The unit vector indicating the DOA of the incident signal  
\(\mathbf{z}_{m}\): The coordinate of the \(m\)-th antenna  
\(\Delta\): The distance between the adjacent antenna elements  
\(r\): Radius of antenna array  
\(\mathbf{R}\): The true covariance matrix  
\(\mathbf{\hat{R}^{\prime}}\): The sample covariance matrix  
\(\delta\mathbf{R}\): The difference between the sample covariance and the true covariance matrix  
\(\mathbf{E}_{\mathrm{S}}\): The signal eigenvector matrix of \(\mathbf{R}\) matrix whose columns are the signal eigenvectors of \(\mathbf{R}\)  
\(\mathbf{\hat{E}}_{\mathrm{S}}^{\prime}\): The signal eigenvector matrix of \(\mathbf{\hat{R}}\) matrix whose columns are the signal eigenvectors of \(\mathbf{R}\)  
\(\delta\mathbf{\hat{E}}_{\mathrm{S}}\): The difference between \(\mathbf{\hat{E}}_{\mathrm{S}}^{\prime}\) and \(\mathbf{E}_{\mathrm{S}}\)  
\(\mathbf{P}\): The projection matrix onto the column space of \(\mathbf{E}_{\mathrm{S}}\)  
\(\mathbf{\hat{P}^{\prime}}\): The projection matrix onto the column space of \(\mathbf{\hat{E}}_{\mathrm{S}}^{\prime}\)  
\(\delta\mathbf{P}\): The difference between \(\mathbf{\hat{P}^{\prime}}\) and \(\mathbf{P}\)  
\(F\)\((\theta)\): MUSIC spectrum as a function of azimuth associated with covariance matrix \(\mathbf{R}\)  
\(\hat{F}\)\((\theta)\): MUSIC spectrum as a function of azimuth associated with covariance matrix \(\mathbf{\hat{R}^{\prime}}\)  
\(F^{(g)}\)\((\theta)\): Approximate MUSIC spectrum due to the \(g\)-th order approximation of the signal eigenvectors,  
Approximation of \(F\)\((\theta)\)  
\(F^{(g,h)}\)\((\theta)\) The \(h\)-th Taylor expansion-based approximation of \(F^{(g)}\)\((\cdot)\)
## 3 Dual-Polarized 3D absorber
### Fabrication and Measurement  
A prototype of the proposed dual-polarized 3D absorber with 20\(\times\)20 unit cells was fabricated and measured using the conventional free-space measurement to verify our design, as shown in Figure 8. The measurement was carried out in an anechoic chamber. The prototype was placed 4.5m away from the two horn antennas. The measurement procedure can be referred to [19].  
The measured \(\mid S_{11}\mid\) of the proposed dual-polarized 3D absorber under the normal incidence is shown in Figure 9. The measured absorption band is from 2.74 GHz to 11.08 GHz (120.7% FBW), which agrees well with the simulated one. The small discrepancy may be due to fabrication errors.  
Figure 10 shows the measured and simulated \(\mid S_{11}\mid\) of the proposed dual-polarized absorber under different (TE and TM) polarizations and incident angles (20\({}^{\circ}\) and 40\({}^{\circ}\)).  
Figure 8: Photograph of the fabricated prototype and the measurement set-up.  
Figure 9: Simulated and measured \(\mid S_{11}\mid\) of the proposed 3D absorber under the normal incidence.  
The measured results are in good agreement with the simulated ones, indicating a stable absorption performance up to a 40\({}^{\circ}\) incident angle.  
Finally, a comparison of the proposed 3D absorber with some recently reported absorbers is shown in Table 1. It can be seen that the proposed 3D absorber features the least usage of lumped components in a unit cell. It is also worth mentioning that the proposed 3D absorber can be free of a reflecting plate (see Section 2.3), which is distinct from other absorbers. The reason for this is the utilization of 3D notch-slot resonators, which operate differently from conventional absorbers.
_Applied_  
_sciences_  
_Article_  
Variation in the Polarization Loss Factor in an Unmanned Aerial Vehicle Jamming Link Due to the Attitude Change  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Variation in the Polarization Loss Factor in an Unmanned Aerial Vehicle Jamming Link Due to the Attitude Change. _Appl. Sci._**2021**, _11_, 10725. [[https://doi.org/10.3390/appl12120725](https://doi.org/10.3390/appl12120725)]([https://doi.org/10.3390/appl12120725](https://doi.org/10.3390/appl12120725))  
Academic Editor: [PERSON]  
Received: 6 October 2021  
Accepted: 10 November 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Coyright:** 2021 by the authors. License MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
**Abstract: A method to analyze the variation of the jamming power received by an unmanned aerial vehicle (UAV) receiver according to the change in the attitude of the small UAV is presented. The main interest of the study is in the variation of the polarization loss factor (PLF) in the UAV jamming link due to attitude change, which may be caused by the wind or intended movement. The attitude change is modeled by a combination of three successive rotations using roll, pitch, and yaw, which are defined as UAV rotations. The proposed method is applied to a jamming link, where a small UAV with an omnidirectional antenna is jammed by a jammer antenna on the ground. The method is applied to a plane ground and over a simulated terrain. The variation of the PLF according to the change of UAV attitude may be higher than the generally expected PLF at locations where the height difference between the UAV and the jammer is large, and near the locations where the jammer is located close to the vertical plane containing the rotation axis when the attitude change is represented by one rotation.  
**Keywords:** polarization loss factor; jamming; UAV; attitude change
# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
## 2 Materials and Methods
### Application of the Methodology
#### 2.4.3 Data Preprocessing  
Data preprocessing is a critical step in building accurate machine-learning models. It is important to note that preprocessing is a dynamic process that may require repetition as new data become available or the problem's requirements change.  
Data cleaning was first performed in the first research ([3]), where incorrect, corrupted, and missing values were removed, resulting in a dataset with 4700 class 0 and 209 class 1 observations. The non-numeric variables, such as gender, type of work, and smoking status, were converted to numbers by label coding. Additionally, feature scaling was applied to standardize features and feature selection to focus on the most critical variables. These steps improved the quality and significance of the data, as well as the performance of the models.  
In the second research ([4]), data preprocessing includes data transformation and structuring, handling missing values by mean imputation, MICE, and imputation of BMI for age groups. Data imbalance is corrected with SMOTE to generate synthetic examples of the minority class [20]. Outliers are removed using a stable scalar, and categorical variables are coded into binary values by one-time coding.  
In the third research ([5]), four different data preprocessing methods are proposed. The first method involves handling all features without imputation of missing values. The second method applies the mean value to fill in missing data and uses an MI estimation method to select features. The third method also uses the mean value for imputation but applies to the PC method for feature selection. The fourth method, again using mean imputation, applies the FI method to feature selection.
## 4 Conclusions  
In this study, to improve the microhardness and wear resistance of 316L stainless steel, fine TiC and NbC ceramic particles were introduced into 316L powders to fabricate the composite coating by powder-fed laser cladding. The phase composition, microstructure, microhardness, and wear properties of the 316L coating and the composite coating are compared and discussed in detail. The main conclusions are as follows:  
1. The TiC and NbC ceramic particles with low thermal conductivity were mainly located at the grain boundary, effectively inhibiting grain growth, increasing nucleation sites, and refining grains. The introduction of ceramic particles decreased the temperature gradient in the melt pool and promoted the transformation of coarse columnar crystals to fine equiaxed crystals, improving the metallurgical bonding properties of the composite coating to the substrate.
2. The microhardness of the composite coating was 232.12 \(\pm\) 2.91 hV, which was 1.28 times that of the 316L coating. The increase in microhardness was a combination of fine grain strengthening and solution strengthening.
3. The friction coefficient and wear cross-sectional area of the composite coating were 0.381 and 8164.732 \(\upmu\)m\({}^{2}\), which were 0.846 and 0.603 times that of the 316L coating, respectively. The wear mechanism changed from severe adhesive wear and plastic deformation to slight adhesive wear and abrasive wear due to the addition of TiC and NbC ceramic particles.
4. During the wear process, the removal rate of the composite coating and the grinding balls decreased due to the lubrication of the fine abrasive chips.  
Overall, this work presents a method for improving the wear resistance of 316L stainless steel by laser cladding. However, it was also found that the microhardness distribution of the fabricated coatings is not uniform. Therefore, we plan to design an ultrasound-assisted laser cladding system to improve the mechanical properties of the coating. In addition, the mechanical machinability of the composite coatings is also the focus of future work.  
J.Y.: conceptualization, methodology, testing, analysis, data curation, writing--original draft, writing--review, and editing. H.H.: supervision, funding acquisition, analysis of the results, and project administration. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Optical and 3D morphology of the GCr15 ball after wear test: (**a1–a3**) for 316L coating and (**b1–b3**) for composite coating.  
**Funding:** This work was supported by the National Natural Science Foundation of China [No. 51650110502].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets supporting the conclusion of this article are included within the article.  
**Conflicts of Interest:** The authors declare no conflict of interests.
# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 5 Conclusions and Future Work  
Relocation models were presented to solve problems with one-way carsharing services. A discrete event simulation model was first developed to evaluate alternative relocation models on relocation cost, acceptance ratio, and utilization ratio based on real customer traveling patterns. The three months of carsharing trip data in Korea was collected and converted into artificial reservation data according to its distribution. Simulations were used to analyze the performance of alternative relocation methods, and impacts for every combination of the number of cars and reservations in a week. The total number of reservations and cars affect the relocation cost, acceptance ratio, and utilization ratio of the relocation models. Forecasting relocation outperformed rebalancing or no relocation in terms of the utilization ratio and acceptance ratio. However, forecasting relocation generates a high relocation cost compared to the other relocation schemes, since more cars end up in unpopular departure stations. Thus, if an operator implements a forecasting model, they relocate a high number of cars from low to high demand stations, with consequential higher relocation costs. Forecasting relocation will help the operator predict car demand for each station based on the reservation history, increasing the chance of customers using the car, i.e., high acceptance and utilization ratios. The operator can consider different policies to implement either rebalancing relocation, reducing relocation cost (reducing the number of staff for relocation), but also reducing utilization and customer satisfaction; or forecasting relocation, increasing relocation costs, but also increasing utilization and customer satisfaction.  
One-way operators must also decide the maximum allowable booking time to make the reservations. A long maximum booking time will reduce profit, particularly when the operator has a high number of reservations and a low number of operational cars.  
There are a number of limitations to the current study. There were no relocation or station constraints, i.e., the number of relocation staff and station size were effectively infinite. Different types of carsharing datasets should be considered for comparison to the current simulations. Comparison with other relocation models should be considered.  
This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry, and Fisheries (IPET) through the Agriculture, Food, and Rural Affairs Research Center Support Program, funded by the Ministry of Agriculture, Food, and Rural Affairs (MAFRA) (Grant number: 710003).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# A Placement Method of the 5G Edge Nodes Based on the Hotspot Distribution of Mobile Users
## 4 Our Experiments
### The Feature Extraction Experiment of Clustering Results  
Our experiment environment is a Personal Computer, Windows 10, Intel (R) Core (TM) i7-6700 [EMAIL_ADDRESS] GHz processor, 16 GB RAM, and Python programming language.  
1. Test the runtime of the clustering algorithm based on DBSCAN When \(Eps=0.002\), 0.004 and _MinPts_ = 800, we separately tested the clustering time of DBSCAN, The test results are shown in Figure 4. In this test, we select the data of 20 users from 1 to 9 days in the GeoLife dataset, the number of locations ranging from 13,060, 37,517, 86,130, 126,173, 149,941, 175,440, 202,693, 228,854, 259,522 to 316,436.  
We can observe from Figure 4 that the clustering time increases exponentially when \(Eps=0.004\). Therefore, we only selected \(Eps=0.002\) and extracted about 200,000 locations to test our methods in the following experiments.  
1. The influence of Pt on the hotspot extraction When \(Eps=0.002\) and \(MinPts=400\), we cluster 202,693 locations using the DBSCAN algorithm. The clustering result includes 31 cluster areas and 1 discrete area.  
We test the number of hotspots extracted at different \(P_{t}\) with \(T_{d}=0.5\) h, 1 h, and 2 h, respectively. The test results are shown in Figure 5 when \(S_{t}=2\), and Figure 5 when \(S_{t}=3\).  
In Figure 5, we can observe that: only 1 hotspot area is extracted from the 31 clustering areas when \(S_{t}=2\) and \(P_{t}\geq 8\); 8-10 hotspots are extracted from the 31 clusters when \(S_{t}=2\) and \(P_{t}=2\); only 1 hotspot area is extracted from the 31 clustering areas when \(S_{t}=3\) and \(P_{t}\geq 6\); 5-8 hotspots are extracted from the 31 clusters when \(S_{t}=3\) and \(P_{t}=2\).  
Figure 4: The runtime of the clustering algorithm is based on DBSCAN.  
It is obvious that the bigger the \(S_{t}\) is, the less the hotspots that can be extracted, the bigger the \(P_{t}\) is, the smaller the hotspots that can be extracted. So, we can control the number of hotspots by settings \(S_{t}\) and \(P_{t}\).
3. The influence of Td on the hotspot extraction We cluster our experimental dataset with \(Eps\) = 0.002 and \(MinPts\) = 400 and test the number of hotspots at different \(T_{d}\). Figures 5(a) and 5(b) show the experimental results when \(S_{t}\) = 2 and \(S_{t}\) = 3, respectively. From this experiment, we can determine that \(T_{d}\) is also a key factor in extracting the hotspots.  
When \(P_{t}\) = 2 and \(P_{t}\) = 3, the maximum number of hotspots are extracted at \(T_{d}\) = 6 in Figure 5(a) and \(T_{d}\) = 2 in Figure 5(b) because the duration time of hotspots can be computed using \(S_{t}\)*\(T_{d}\). This experiment preliminarily verified the rule that hotspots should continue 6-12 h.
## 2 Broadband System
### Mobile Network Evolution
In this context, some extensive researches are ongoing regarding 6G [218,221]. Moreover, 6G is envisioned to be based on major innovative technologies such as super Internet-of-Things (IoT), mobile ultra-broadband, and AI. Besides, it is envisaged that terahertz (THz) communications should be a viable solution for supporting mobile ultra-broadband. Furthermore, super IoT can be achieved with symbiotic radio and satellite-assisted communications. Besides, machine learning methods are expected to be promising solutions for AI networks [218,221,222]. As summarized in Table 7, 6G is envisaged to offer a considerable improvement on the 5G by employing AI to automate and optimize its operation. In addition, concerning the data rate enhancement and bandwidth improvement, the THz band can ideally deliver three orders of magnitude greater than the realizable ones by mm-wave band [221]. At large, one of the main goals of the 6G networks is to achieve ubiquitous connectivity. In this context, satellite and underwater communications networks will be integrated to offer global coverage. Moreover, innovative service classes/use cases such as ubiquitous mobile ultra-broadband (uMUB), ultrahigh-speed-with-low-latency communications (uHSLLC), and ultra-high data density (uHDD) will be defined for the 6G networks [219]. Based on this, a study group has been dedicated in the IEEE 802.15 for THz spectral allocations and standardizations. Likewise, some companies like Huawei and Intel have been carrying out different experiments in these bands [218,221]. Consequently, to give an overview of the main broadband technologies and help in their selection, we consider different salient factors such as coverage, capacity, benefits, and limitations in Table 8.