esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
###### Abstract
Additionally, several new-generation radar altimeters were launched in recent decades. For example, CryoSat-2 was launched in 2010 [7] and the two satellites of the Sentinel-3A/B (S3A/B) constellation were launched in 2016 and 2018, respectively. This new altimetry fleet provides much higher spatial resolution and lower noise, aiming at reducing land contamination in coastal radar echoes. CryoSat-2 and Sentinel-3 operating in SAR mode have a waveform footprint of 5 km\({}^{2}\), much smaller than that covered by pulse-limited altimeters in 20-30 km\({}^{2}\), such as SARAL/AltiKa and the Jason series. The along-track resolution of SAR altimeters has been increased to approximately 300 m along the flight path [8]. However, the accuracy of coastal altimetry is still affected by coexistence of the targeted water and the neighbouring terrain within a footprint [9]. Using SAR altimeters, SSH can be estimated to decimetre-level accuracy in the Baltic Sea [10] and to a couple of decimetres for small lakes with diameter less than 10 km [11]. Various validation methods are able to determine the accuracy of altimetric heights. For instance, [PERSON] et al. [12] used 478 global tide gauges to verify gridded altimetry sea-level anomaly (SLA) [12], while [PERSON] et al. [13] deployed Global Positioning System (GPS) buoys to evaluate SARAL/AltiKa products. Moreover, [PERSON] et al. [10] used the precise national geoid model to examine Sentinel-3 coastal product with a SAMOSA 2 retracker, and [PERSON] et al. [14] used a GPS-catamaran for validation. All these methods can be cross-validated as well. Sentinel-3 products are categorized in three levels. Level-0 (raw) product is an internal product and is not available to users. Level-1 (SR_1_SRA) is the Level-0 product corrected for instrumental effects. Level-2 (water SR_2_WAT and land SR_2_LN) is the Level-1 product corrected for geophysical effects. Level-3 and Level-4 products are validated and are the most popular products for oceanographers. Level-3 and Level-4 include along-track and gridded data, respectively. The main objective of this study was to validate Sentinel-3A and Sentinel-3B Level-2 altimetry products along the Estonian coast of the Baltic Sea and in two small lakes: Vortsjarv and Lake Peipus. The second aim was to evaluate the accuracy of Level-3 altimetry products as well as the Nucleus for European Modelling of the Ocean (NEMO) reanalysis model results, which are available through the Copernicus Marine Environment Monitoring Service (CMEMS) service.
## 4 Discussion
In particular, when the ratio of SNMe/NMe is decreased, there is a minor risk of finding a thin gingival biotype either in the upper or lower anterior regions. This is the first study that has evaluated gingival thickness through the periodontal probe's translucency (thin and adequate biotype) with different vertical facial heights, and these findings support the hypothesis that there is no correlation between facial morphology and gingival thickness on lateral cephalograms [24]. Gingival thickness can be assessed by transgingival probing [38], ultrasonic measurement [39], or through the visibility of the probe [40; 41]. Transgingival probing was not used because of the need for local anesthesia, which could induce a local volume increase and discomfort for patients [38]. Additionally, ultrasonic measurement was not preferred because of its repeatability with a coefficient of 1.20 mm [42]. Instead, the transparency of the probe through the gingival margin has been found to have a high reproducibility by [PERSON] et al., showing 85% inter-examiner repeatability (k value = 0.7, \(p\) value = 0.002) [40]. Thus, this study used the periodontal probe visible through the gingiva after its placement in the facial sulcus of the anterior teeth [40]. The prevalence of malocclusion can vary between children and adolescents. However, the demand for orthodontic treatment is increasing [43; 44; 45], and greater attention has to be paid to periodontal aspects; indeed, orthodontic treatment can play an important role in periodontal changes [46]. The thickness of the gingiva is supposed to represent an indicator for reducing the risk of bone loss and gingival recession [47]. In fact, there are two studies which have indicated a statistically significant relationship between facial biotype and alveolar height and thickness with a greater risk of moving incisors beyond the anatomic limits of the alveolar bone by application of uncontrolled forces [6; 48]. This uncontrolled movement can bring to: alveolar bone fenestrations, increasing susceptibility to gingival recession [49; 50] and recession in the case of less than 2 mm of gingival thickness [7]. The current study presents several strengths. First, the periodontal assessments were performed in all of patients at the beginning of orthodontic treatment. This allows for an accurate diagnosis and treatment planning. In order to avoid bias due to differences in operator performance, only two trained clinicians performed the periodontal evaluations. Moreover, a new method to evaluate craniofacial morphology was introduced without exposure to the patients of further radiation. The study also has some limitations. Firstly, the sample size was relatively small to achieve a more reliable result. Secondly, only a few patients presented a thin UGB; however, this was in accordance with the normal prevalence of this biotype [26]. Further longitudinal studies are necessary to monitor the long-term effects of orthodontic treatment on gingival biotype in different facial typologies.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 6 Theoretical Performance Bounds with Simple Models  
This section aims to characterize some theoretical performance bounds of the joint content recommendation and delivery system. For tractability, we assume a simple scenario with simplified models. More specifically, it is assumed that the user interests in different contents follow a uniform distribution in \([a,a+h]\) (e.g., rating scores uniformly distributed from 1-10), and all users' interest profiles are independent. In addition, it is assumed that content file sizes also follow a uniform distribution in \([b,b+g]\).
# SVPWM Method for Multilevel Indirect Matrix Converter with Eliminate Common Mode Voltage
## 2 Multilevel Indirect Matrix Converter and the Proposed SVPWM Method  
### The Multilevel IMC Topology  
As shown in Figure 3, the multilevel IMC comprises of two stages: The rectifier stage and the three-level T-Type inverter stage. The function of the rectifier stage is to generate sinusoidal input currents as well as to maintain the maximum DC voltage at the DC-link. The expected output voltages with variable frequency are obtained by control at the inverter stage. The cascaded rectifier is built by two identical three-phase bidirectional front-end current source rectifier (CSR) modules connected in series [12]. Each CSR includes six bidirectional as shown in Figure 3 (\(S_{ap1}\), \(S_{an1}\), \(S_{bp1}\), \(S_{bn1}\), \(S_{cp1}\), \(S_{cn1}\) for upper rectifier; and \(S_{ap2}\), \(S_{an2}\), \(S_{bp2}\), \(S_{bn2}\), \(S_{cp2}\), \(S_{cn2}\) for lower rectifier), and they are connected at neutral-point \"\(O\)\". The upper and lower rectifier stages generated the DC-link voltage: \(V_{PO}\) and \(V_{ON}\), respectively. The switching states at the rectifier stage and the DC-link voltage are shown in Table 1, where \(v_{ab}\), \(v_{ac}\), \(v_{bc}\), \(v_{ba}\), \(v_{ca}\), \(v_{cb}\) are the line-to-line input voltage.  
Figure 3: The Three-level T-Type Indirect Matrix Converter Topology.  
The three-level T-Type NPC inverter is the extension configuration of a traditional two-level three-phase inverter, by adding three bidirectional power switches that connect three-load output to the neutral point. Comparison with the traditional three-level NPC, the three-level T-Type NPC inverter have many advantages, e.g., less losses, less semiconductor components due to the absence of clamping diode [22; 23]. From Figure 3, the switching state and load voltage of inverter stage are given in Table 2.
## Appendix A Calculation of Daily Returns and Semivariance
### Semivariance Computation  
Semivariance is a measure of the downside risk, focusing only on the deviations of returns that fall below a specified benchmark or target. It quantifies the volatility associated with negative returns, providing a more focused perspective on potential losses compared to the total variance. The semivariance for a portfolio (or individual asset) is calculated based on historical daily returns that fall below a predefined threshold, typically, the mean return or a target return. In this study, semivariance was computed relative to the mean daily return of the asset/portfolio.  
The semivariance of asset \(i\), denoted as \(\text{SV}_{i}\), is computed over \(T\) daily observations, as follows:  
\[\text{SV}_{i}=\frac{1}{T}\sum_{t=1}^{T}(\min(0,\Re_{i,t}-\bar{R}_{i}))^{2},\]  
where  
* \(T\) is the total number of daily observations.
* \(\Re_{i,t}\) is the daily return of asset \(i\) on day \(t\).
* \(\bar{R}_{i}\) is the mean daily return of asset \(i\) over the period \(T\), calculated as \(\bar{R}_{i}=\frac{1}{T}\sum_{t=1}^{T}\Re_{i,t}\).
* The \(\min(0,\cdot)\) function ensures that only negative deviations from the mean are considered. Deviations above the mean are treated as zero.  
For a portfolio \(P\) with \(N\) assets and weights \(\varepsilon_{j}\), the portfolio daily return \(R_{P,t}=\sum_{j=1}^{N}\varepsilon_{j}\Re_{j,t}\). The semivariance for the portfolio, \(\text{SV}_{P}\), is then calculated similarly:  
\[\text{SV}_{P}=\frac{1}{T}\sum_{t=1}^{T}(\min(0,R_{P,t}-\bar{R}_{P}))^{2},\]  
where \(\bar{R}_{P}\) is the mean daily return of the portfolio over the period \(T\).
# Design of a Technique for Accelerating the WSN Convergence Process
## 6 Evaluation
### Scenario 2--Complex Outage  
After desimulating the mechanism on a simple simulation, more complex simulations with more nodes continued. First, simulations were performed without node mobility with a guest node failure [16]. After the outage, the simulation continued in the same way as before. And thus the shortest path was found initially through the host node 16. When the host node 0 detected an outage, it subsequently found the shortest path through the host node 19 and the host node 7, after which ping responses were also returned (Figure 18).  
Simulation result without BFD mechanism:  
------------  
sent: 177 received: 176 loss rate (\(\%\)): 0.564972 round-trip min/avg/max (ms): 1.90723/55.2861/3811 stddev (ms): 384.227 variance: 0.147631 ------------  
The result of the simulation with the BFD mechanism on host (0) and host (16) nodes:  
------------  
sent: 177 received: 177 loss rate (\(\%\)): 0 round-trip min/avg/max (ms): 1.90723/7.37104/813.213  
Figure 17: Simulation with a simple AODV life cycle.  
It is clear from the result that the applied BFD mechanism worked similarly to the simulation with a simple life cycle of the AODV routing protocol. It was possible to save the previously \"lost\" packet and significantly reduce the maximum packet route time. Further simulations took place on the same scenario but with the BFD mechanism turned on at all network nodes. In this case, however, the backup path was created through node host (10) and node host (4) (Figure 19).  
Result with the BFD mechanism enabled on all nodes:  
------  
sent: 177 received: 169 loss rate (\(\backslash\)\(\backslash\)\(\rangle\): 4.51977 round-trip min/avg/max (ms): 1.90723/31.5021/2462.21 stddev (ms): 219.911 variance: 0.048361 ------  
From the analysis of the event dump, it was found that the sessions after the outage occurred not only on the path host [0] <-> host [10] <-> host [4] <-> host [1] but also between the node host (1) and nodes host (6) and host (7) and between node host (10) and node host (6) (Figure 20).  
As seen in Figure 20, sessions were established on two possible routes. After detailed analysis, it was found that the first route created was host (0) <-> host (10) <-> host (6) <-> host (1). However, on the route between nodes host (10) and host (6), there was a loss of three consecutive BFD control packets, so the BFD mechanism detected the outage, thus  
Figure 18: Simulation with BFD enabled on nodes host [0] and host.  
initiating the finding of a new path. It was found just through the second shown route through the nodes host (0) \(<\)\(>\) host (10) \(<\)\(>\) host (4) \(<\)\(>\) host (1).  
Figure 19: Simulation with BFD enabled on all nodes.  
Figure 20: Created BFD sessions.
## 3 Results
### Partial Least Squares--Path Modelling
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{2}{c}{**Construct Level**} & \multicolumn{2}{c}{**Measured Variables Level**} & \multicolumn{2}{c}{**Construct Level**} & \multicolumn{2}{c}{**Measured Variables Level**} \\ \hline
**Construct** & **Statistics** & **Items or Parcels** & **Factor Loadings \({}^{\textbf{a}}\)** & **Construct** & **Statistics** & **Items or Parcels** & **Factor Loadings \({}^{\textbf{a}}\)** \\ \hline Availability of & \(\xi_{1}=4.45\), \(\xi_{2}=0.62\) & 1 & 0.88 & BFNS for & \(\xi_{1}=1.76\), \(\xi_{2}=0.81\) & 1 & 0.78 \\ social support & \(\rho=0.95\); AVE = 0.74 & 2 & 0.87 & Competence & \(\rho=0.81\); AVE = 0.58 & 2 & 0.89 \\  & & 3 & 0.74 & & 3 & 0.56 \\  & & 4 & 0.86 & BFNS for & \(\xi_{1}=3.22\); \(\xi_{2}=0.80\) & 1 & 0.68 \\  & & 5 & 0.88 & Relatedness & \(\rho=0.87\); AVE = 0.52 & 2 & 0.73 \\  & & 6 & 0.90 & & 3 & 0.77 \\ Satisfaction of & \(\xi_{1}=4.31\); \(\xi_{2}=0.61\) & 1 & 0.81 & & 4 & 0.63 \\ social support & \(\rho=0.94\); AVE = 0.72 & 2 & 0.86 & & 5 & 0.75 \\  & & 3 & 0.82 & & 6 & 0.73 \\  & & 4 & 0.85 & BFNF for & \(\xi_{1}=1.98\); \(\xi_{2}=0.78\) & 1 & 0.74 \\  & & 5 & 0.86 & Autonomy & \(\rho=0.80\); AVE = 0.48 & 2 & 0.65 \\  & & 6 & 0.86 & & 3 & 0.75 \\ Family support & \(\xi_{1}=2.72\); \(\xi_{2}=0.79\) & 1 & 0.78 & & 4 & 0.58 \\ for autonomy & \(\rho=0.86\); AVE = 0.54 & 2 & 0.71 & BFNF for & \(\xi_{1}=2.17\); \(\xi_{2}=0.82\) & 1 & 0.59 \\  & & 3 & 0.63 & Competence & \(\rho=0.83\); AVE = 0.54 & 2 & 0.73 \\  & & 4 & 0.81 & & 3 & 0.71 \\  & & 5 & 0.67 & & 4 & 0.85 \\ Family support & \(\xi_{1}=2.08\), \(\xi_{2}=0.78\) & 1 & 0.70 & BFNF for & \(\xi_{1}=1.95\); \(\xi_{2}=0.62\) & 1 & 0.77 \\ for competence & \(\rho=0.81\); AVE = 0.52 & 2 & 0.81 & Relatedness & \(\rho=0.85\); AVE = 0.65 & 2 & 0.80 \\  & & 3 & 0.63 & & 3 & 0.83 \\  & & 4 & 0.72 & Depressive & \(\xi_{1}=2.11\); \(\xi_{2}=0.47\) & 1 & 0.81 \\ Family support & \(\xi_{1}=1.84\), \(\xi_{2}=0.69\) & 1 & 0.62 & Feelings & \(\rho=0.88\); AVE = 0.70 & 2 & 0.87 \\ for relatedness & \(\rho=0.83\); AVE = 0.60 & 2 & 0.81 & & 3 & 0.82 \\  & & 3 & 0.87 & Purpose in life & \(\xi_{1}=2.23\); \(\xi_{2}=0.43\) & 1 & 0.81 \\ BFNS for & \(\xi_{1}=2.71\); \(\xi_{2}=0.89\) & 1 & 0.73 & & \(\rho=0.90\); AVE = 0.74 & 2 & 0.89 \\ autonomy & \(\rho=0.85\); AVE = 0.53 & 2 & 0.71 & & & 3 & 0.88 \\  & & 3 & 0.76 & & & & \\  & & 4 & 0.80 & & & \\  & & 5 & 0.65 & & & & \\ \hline \end{tabular} Note: BFNS = basic psychological need satisfaction, BFNS = basic psychological need frustration, \(\xi_{k}=\) its signvalue of the item correlation matrix; \(\rho=\) composite reliability; AVE = average variance extracted;\({}^{\textbf{a}}\) All Factor loadings were significant at \(p<0.001\).  
\end{table} 
Table 2: Psychometric properties of the outer model of the partial least square path modelling.
## 4 Fault Identification Method Based on IPSO-WNN
### Fault Identification Process  
Through the analysis of the expected output, fault data and the fault features of each fault type are input into the diagnosis model for training. The model training input values are shown in Table 1.  
In Table 1, \(U_{0}\) represents the zero-sequence voltage and \(U_{n}\) represents phase voltage of the line. When a single-phase grounding fault occurs on the line, it conforms to \(U_{0}>0.15U_{n}\), which can be used as the basis for determining the grounding fault.  
The fault identification based on IPSO-WNN is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c} \hline
**Input Fault Type** & **Input Fault Characteristics** & **Desired Output** \\ \hline Three phase short circuit fault & \(\Delta I>100\) and \(\Delta I_{i}\)(i = A, B, C) \(>100\)A is \(l\), & \\  & \(l=2\) & 1 \\ High resistance ground fault & \(\Delta I>100\) and \(l=3\) & 2 \\ Arc earthed failure & \(U_{0}>0.15U_{n}\) and \(B_{cd}\) & 3 \\ Three phase short circuit fault & \(U_{0}>0.15U_{n}\) and \(B_{cd}\) & 4 \\ \hline \end{tabular}
\end{table}
Table 1: Model training input values.
# Polarization Radiometric Calibration in Laboratory for a Channeled Spectropolarimeter
## 5 Conclusions  
The difficulty of the calibration for the fieldable CDISP lies in the decoupling of radiometric calibration coefficient and diattenuation of fore-optics. In this paper, we propose a polarization radiometric calibration model to decouple them. We first theoretically derive the calibration and compensation model considering the alignment errors of PSIM, the variation of retardations, and the polarization properties at different field of view of optical system. Before decoupling the radiometric calibration coefficient and the polarization properties, we perform classical radiometric calibration and self-correction calibration for the system. Finally, we use 0\({}^{\circ}\) and 45\({}^{\circ}\) linearly polarized beams to calibrate other parameters. By using the decoupled radiometric calibration coefficient and above-mentioned calibration results, we can accurately obtain the spectral radiance and reconstruct the polarization information of detected object. The validity and feasibility of the presented method have been confirmed by simulation experiments, and the reconstruction accuracy of the polarization information is improved compared with the traditional calibration method. The presented calibration method is significant for the quantitative application of the CDISP.  
For the integral calibration process for the practical application of CDISP, the spectral calibration is not discussed in this paper. This is because the spectral calibration of CDISP is consistent with the  
Figure 10: Measurement accuracy of normalized Stokes vectors and DOP, (**a**) \(S_{1}\)/\(S_{0}\); (**b**) \(S_{2}\)/\(S_{0}\); (**c**) \(S_{3}\)/\(S_{0}\); and (**d**) DOP. Wherein the blue and red lines respectively represent the presented calibration method and the traditional calibration method.  
calibration method of traditional dispersive imaging spectrometer, which can be completed in front of the polarization radiometric calibration.  
Conceptualization, W.X.; methodology, W.X.; software, W.X.; validation, W.X., X.J., and C.Y.; formal analysis, W.X. and B.Y.; investigation, W.X.; resources, W.X. and J.B.; data curation, W.X.; writing--original draft preparation, W.X.; writing--review and editing, X.J., J.B., C.Y., S.X., B.Y., and J.Z.; visualization, W.X. and J.B.; supervision, C.Y. and S.X.; project administration, C.Y.; funding acquisition, C.Y. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Technology Development Program of Jilin Province of China, grant number 20180201012 GX; National Key Research and Development Program of China, grant number 2016 YFF0103603 and National Natural Science Foundation of China (NSFC), grant number 61727818, 61805235 and 61875192.  
The authors declare no conflict of interest.
## 1 Introduction
### An Overview of Related Reviews  
Several articles published in scientific journals [33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65] reported different types of reviews, including systematic literature reviews, related to various aspects of smart cities' implementation, including data analytics [33; 34; 35; 36; 37], systems architectures [38], data security [39; 40; 41], data security and Internet of Things (IoT) [42; 43; 44], IoT [45], ontologies [46], healthcare [47; 48; 49], energy efficiency [50], citizenship [51], smart city indicators [52], mobility [53; 54; 55; 56; 57; 58] and age-friendly initiatives [59; 60; 61; 62; 63; 64; 65].  
Looking specifically for reviews related to mobility or age-friendly initiatives (Table 1), is possible to conclude that the state-of-art studies did not aim to analyze relevant IT-based applications requiring smart cities' infrastructures to facilitate the mobility of older adults.  
In turn, concerning age-friendly initiatives, the state-of-the-art reviews aimed to study aspects as the impact of smart cities in different countries [60; 61; 62], barriers to the implementation of age-friendly concepts in smart cities [59], the role of IT and the barriers and stressors for age and disability-friendly communities [65], and the impact of IT on the quality of life of older adults [63]. Finally, one article [64] is a narrative review on the role of mobility digital ecosystems for age-friendly urban public transport. However, this narrative review (not a systematic review), instead of performing a quantitative evidence-based analysis of empirical studies, aimed to present a discussion on how transport technology and transport mobility practices might contribute to the promotion of mobility rights of older adults and age-friendly mobility [64].  
The article is outlined as follows. In Section 2, the proposed research questions and methods are detailed. The results are presented in Section 3. Finally, the discussion and answers to the research questions are presented in Section 4, and general conclusions are presented in Section 5.