esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The present study shows excellent absolute agreement in terms of the ICC. A decrease in the ICC can be observed considering a single rater's agreement rather than the observers average due to the greater precision required by the analysis conditions. Nevertheless, it has to be noticed that the value remains high (>0.7 meaning \"good\" and being very close to the threshold used for \"excellent\", which starts from 0.75) [26] (Tables 2 and 4). Thus, the evaluation by several raters remains the best solution, but the use of a single rater does not represent a drastic decrease in measurement accuracy. The average CDI values are very close to the decision threshold for ATA transposition surgery (1.2), so small variations can lead to a change in the surgical decision. Nonetheless, the decision agreement corresponds to a \"substantial\" level, as noted by [PERSON]'s kappa [16] (Table 3). This finding is also confirmed considering senior or junior raters separately; though, juniors show a slightly greater inter-observer agreement than seniors. Hence, our results show that the CDI is an accurate and reproducible measurement method, with a low inter-observer variability. Furthermore, this is the only study correlating the CDI measurements of six observers to the authors' knowledge, representing stronger evidence of reliability and reproducibility. Thus, we believe that the CDI may be employed to perform a quantitative evaluation in preoperative planning for ATA transposition surgery using a calibration marker, which allows for a more precise calculation of patellar lowering to achieve a normal CDI.
## 4 Discussion
In the study by [PERSON] et al. [26], the mean CAL gain was 3.2 \(\pm\) 0.3 mm in aCPRT group (periosteal pedicle and autogenous bone chips), 3.2 \(\pm\) 0.3 mm in GTR group and 1.6 \(\pm\) 0.1 mm in OFD group. These values were lesser compared to the CAL gain in the current clinical trial, which may be due to the longer follow and baseline recession. In a preliminary case series by [PERSON] et al. [6], eggshell-derived cement was used for treatment of intrabony defects, and here the CAL gain was 4.2 \(\pm\) 0.3 mm at 6 months. This CAL gain was comparable to the present study though the values were marginally greater in our trial. The plaque and gingival index scores showed a moderate reduction at 6 months. Both the control and test groups did not show much difference. (Refer Table 1) This denotes that the treatment outcomes were not affected by confounding factors such as oral hygiene. At the end of 6 months of study, all the groups showed a positive outcome. None of the cases reported any adverse events. Upon observation, the periosteal pedicle group (group A) showed a mean bone density of 1144.29 \(\pm\) 82.64 HU and EnHA (group B) showed 1065.57 \(\pm\) 60.48 HU. Thus, the regeneration of bone radiographically could be called a success. Though all the groups showed some increase in bone fill, group A and group B showed a better result (group A--2.06 \(\pm\) 0.74 mm and group B--1.77 \(\pm\) 1.25 mm mean bone defect fill at 6 months), which was statistically significant. The limitations of this study were the inability to use sophisticated equipment for graft particle size standardization and a smaller sample size due to the prevailing COVID situation. Within the limitations of the present study, periosteal pedicle grafting is an ingenious technique and the eggshell-derived nano hydroxyapatite uses an indigenous extraction technology and when combined they seem to give a supra-additive effect for periodontal regeneration.
# Research on Amphibious Multi-Rotor UAV Out-of-Water Control Based on ADRC
## 2 Dynamic and Kinematic Modeling
In the motion coordinate system, since the center of gravity of the UAV coincides with the origin of the motion coordinate system,\(x_{G}=0\), \(y_{G}=0\), and \(z_{G}=0\). The general equations of motion of the UAV with six degrees of freedom in space in the moving coordinate system are obtained by calculating and simplifying according to Equations (1)-(4) and the momentum and momentum moment derivation theorems [34]. \[\left\{ \begin{array}{l}m(\dot{u}-vr+wq)=X\\ m(\dot{v}-wp+ur)=Y\\ m(\ddot{w}-uq+\rho p)=Z\\ I_{x}\dot{p}+(I_{2}-I_{y})qr=K\\ I_{y}\dot{q}+(I_{x}-I_{z})rp=M\\ I_{z}\dot{r}+(I_{y}-I_{x})pq=N\end{array} \right. \tag{5}\]  
where \(I_{x}\), \(I_{y}\), and \(I_{z}\) are the moments of inertia of the UAV in the three axes of the motion coordinate system. **3. Motion Force Analysis**  
The description of the relevant physical quantities in this chapter is shown in Table 2.  
Due to the need for a suitable and accurate force analysis of the amphibious multi-rotor UAV, and considering that the working environment of the amphibious multi-rotor UAV is variable and complex, this paper mainly considers the factors that play a major role in the system performance: static force (moment) \(\tau_{C}\), control force (moment) \(\tau_{P}\), and hydrodynamic force (moment) \(\tau^{\prime}\). Under normal conditions, the total external force (moment) on the amphibious multi-rotor UAV moving in the water is \(\tau=\tau_{C}+\tau_{P}+\tau^{\prime}\), where \(\tau_{C}\) is the static force (moment) consisting of gravity and buoyancy; \(\tau_{P}\) is the control force (moment) including rotor lift; \(\tau^{\prime}\) is the hydrodynamic force (moment) caused by the motion of the amphibious multi-rotor UAV in the water. In this paper, it is stipulated that the force model is judged according to the position of the center of mass of the aircraft. When z \(>0\), the hydrodynamic force is ignored, and the static and dynamic models are judged according to the positive and negative values of z.
## 3 Ontology Modeling and Quality Measures  
### Quality Metrics  
There are many quality requirements of ontology models that should be considered during the ontology construction process. On the other hand, from the viewpoint of practical applications, better support for quality ontology development is a key factor in the desired success of the ontology model.  
Considering the difficulties in practical ontology modeling, the following factors can be emphasized:Many developers come from the database domain, where a closed-world approach is the dominant model. In contrast, the ontology model uses an open-world approach, and explicit additional axioms must be created to provide a more suitable view.
* An ontology should cover a wide range of concepts, and global ontologies are usually constructed by many partial (domain-specific) ontology models having different granularity and functionality. Due to the large size of ontology models, an automatic integration tool that can discover hidden inconsistencies is usually required.
* Subjectivity, i.e., there are no golden rules and guidelines for ontology design, and there exist different approaches to ontology development (e.g., both inductive and deductive approaches [35], resulting in very different ontology models for the same domain).
* In many OOP models, the main relationship between classes is the specialization relationship. Child classes inherit the properties of the parent class automatically. In ontology modeling, the declaration of a domain axiom on a property does not mean an automatic inheritance; a separate subclass axiom must be added to the corresponding ontology.  
In the next sections, we provide an overview of research results related to ontology quality. A common approach is to adapt the standard software quality metrics to ontology [35]. For example, [36] proposed following quality aspects: syntactic quality, semantic quality, pragmatic quality, and social quality. Considering the different approaches in the literature, we can categorize the ontology-specific quality aspects into the following three main areas [34; 37]:  
* schema and type definitions;
* the amount and the resolution of the data;
* clarity, compatibility, and usability.  
Unfortunately, it is a difficult task to construct a common set of metrics which objectively describe all aspects of ontology quality. In the next section, we present the most commonly used examples, which generally emphasize important properties of the ontology.
## 4 Results
### WP2--Underwater Scenario  
In this phase, the underwater archaeological scenario has been analyzed. The experiment was setup by simulating a water column of 30 cm above the sand surface. In correspondence of the S1 and S2 transects, 2D GPR was performed above the water surface while ERTs were carried out above and below the water column.  
Figure 11: 3D loop–loop ERT depth-slices at 0.25 m (**a**), 0.45 m (**b**), 0.70 m (**c**), and 1.00 m (**d**) in WL2.  
Figure 10: 3D loop–loop ERT depth-slices at 0.25 m (**a**), 0.45 m (**b**), 0.70 m (**c**), and 1.00 m (**d**) at WL3.  
GPR results are shown in Figure 13. In this case, the signal attenuation is greater than the previous conditions (WP1). However, it is possible to easily detect the location and the shape of the buried archaeological structures. The em velocity was equal to 0.035 mns\({}^{-1}\) for the water layer (\(\epsilon_{r\text{-}w}\approx 81\)) and 0.065 mns\({}^{-1}\) for the water saturated sand (\(\epsilon_{r}\approx 25\)).  
Figure 12: 3D loop–loop ERT depth-slices at 0.25 m (**a**), 0.45 m (**b**), 0.70 m (**c**), and 1.00 m (**d**) in WL1.  
Figure 13: 2D radargrams obtained along the S1 (**a**) and S2 (**b**) profiles in Figure 5 with indication of the anomalies related to the archaeological structures.  
Even with the presence of water-saturated soil and the water column, the reflections imputable to the column (A), the stone wall (D), and the top of the two tombs (F and D) are clearly individuated (Figure 13a,b) while the top of the road (C) is weakly identifiable.  
Moreover, 2D ERTs results are shown in Figure 14. Electrical resistivity measurements were performed with a dipole-dipole array and an electrode spacing of about 0.20 m. Inversions with ResIPy software converged into four iterations reaching the final RMS Misfit of 1.0. The electrical resistivity values range is between 10 and 100 \(\Omega\)m. In general, the lower resistivity values (\(<\)50 \(\Omega\)m) are due to the presence of the saturated sand while the higher values (\(>\)50 \(\Omega\)m) are induced by the presence of the archaeological remains.  
The 2D ERTs were acquired with two different approaches. The electrical resistivity distribution obtained with floating electrodes was not able to detect anomalies well that were associated with the buried objects (Figure 14a,b). It is possible to identify the shallowest features (column A, the wall D) whilst, the features at greater depth, as in the case of the paved road and tombs, are not visible. On the contrary, the electrical resistivity image obtained with electrodes placed on the underwater floor provides an improvement of the information about the position of the archaeological remains (Figure 14c,d). In fact, the shallowest (column A, the wall D) and deeper (paved road and tombs) features are highlighted well.
### Ground-Penetrating Radar  
Ground-penetrating radar emits electromagnetic waves into the ground, as illustrated in Figure 1; waves are partially transmitted and reflected at boundaries with different electromagnetic properties, and reflections are captured by a receiving antenna to infer ground composition [10]. Individual GPR signals, known as A-Scans, are typically compiled into two-dimensional representations for analysis. These are designated as B-Scans (radargram, YZ-plane), C-Scans (XY-plane), or D-Scans (XZ-plane), depending on their plane orientation (see Figure 1d).  
The propagation speed of an electromagnetic wave \(v\) in a medium can be approximated from the ratio of the speed of light in vacuum (\(c_{0}\)) and the square root of the product of relative permeability (\(\mu_{r}\)) and relative permittivity (\(\varepsilon_{r}\)) ([11], p. 149):  
\[v=\frac{c_{0}}{\sqrt{\mu_{r}\varepsilon_{r}}}. \tag{1}\]  
Moving an antenna over a reflective object thus alters the signal's travel time, creating hyperbolas in B- and C-Scans due to changing distances.
## 3 System Architecture
### Synchronization Circuit  
To guarantee frequency synchronism between RRUs, a synchronization circuit is implemented on every AAU. For each RRU, the CDR module of the _Xilinx GTY_ transceiver retrieves the clock information from the DL bitstreams and generates a 30.72 MHz clock. The retrieved 30.72 MHz clock goes to the AAU. However, the jitter of this clock is high due to the mediocre performance of the on-FPGA PLLs.  
The synchronization circuit comprises two phase lock loops (PLLs) (Figure 6). The first PLL (PLL1) functions as a jitter cleaner and generates a low-jitter 92.16 MHz clock, which provides the sampling clock of analog-to-digital converters (ADCs) and the reference clock for the second PLL (PLL2). PLL2 generates the carrier clock for down-conversion. The measurement results in Section 4.2 demonstrate the RRU synchronism.
## 1 Introduction  
Wireless Sensor Network (WSN) applications such as the Internet of Things (IoT) are an integral part of the Future Internet that could be defined as a dynamic global network infrastructure with self-organization capabilities that is seamlessly integrated into the information network based on standard or interoperable communication protocols. This attracts more attention to how sensor nodes can monitor and cooperatively pass their data like temperature, sound, pressure, _etc._ more efficiently through the network under realistic physical or environmental conditions. For example: the environment could be a scale free data sampling area like groups of lakes, high mountains with original forest, or the environment is so inaccessible that humans can't get the sampling data in the short term. A survey shows that a few networks only have one existing sink node, called a tree, but other multi-sink networks exist in some specific applications, called a forest, which are more indispensable in WSNs. Figure 1 shows a scenario illustrating that common unicast routing protocols will set up separated routes from sink node #1, 2 to sensor node #1-6, denoted by the dashed lines, but it is obvious that if multicast technology could be used, it would be a better, more energy-efficient way to suppress the duplicate transmissions of same data packets and another nodes which are not included in multicast groups could be put into power saving mode, indicated by the real lines.  
Multicasting is an automatic communication technique in which data from source nodes are transmitted to a larger number of subscribed destination nodes for the purpose of networking these kinds of applications. However, maintaining a functional level of Quality-of-Service (QoS) in a multicast environment, especially in a resource-constrained wireless sensor network, can be difficult. Contemporary research indicates that this problem can be abstractly constructed and modeled through a multicast tree (forest) model with resource allocations and routing assignment problems [1,2].  
Figure 1: Scenario of unicast and multicast.  
In order to design a multicast sensor system and solve the problems mentioned above, the solution planning is concerned with the economic feasibility of establishing a network in the first stage, the QoS, and the fairness to subscribers in the second stage. This work can be constructed by a cost of a multicast sensing tree to determine the total resource utility of networks for multimedia applications with regards to delay in transmission. QoS metrics is a crucial for the measurement of latency, delay jitter, delay variance, and end-to-end delay per source-destination path. Lastly, we must keep in mind that subscribers may have different service level agreements in a real operation system. We also consider fairness, which measures whether resources are being adequately and fairly allocated to subscribers from both a user perspective and an operations perspective.
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 4 MODY Subtypes and Their Treatments
### PAX4-mody (mody 9)  
MODY 9 is caused by heterozygous mutations in the paired box 4 (_PAX4_) gene, which encodes for a transcription factor that is required for insulin-producing beta-cell generation, differentiation, development, and survival [34; 40]. During the early stages of embryonic development, _PAX4_ is expressed in endocrine promoter cells, and later in \(\beta\)-cells [83]. Ketosis-prone diabetes has been associated with mutations in _PAX4_ gene [84]. At the early stages, MODY 9 patients are treated with dietary changes or OADs [85]. However, at more advanced stages of disease, affected patients may require insulin administration [86].
## 5 Case Study (Results (Step 2 to 5 in Figure 3))
### Gap of MWL in Each Factor (Step 4 in Figure 3)
#### 5.2.2 Manning Option
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**In LoA**} & \multicolumn{2}{c}{**MWL of RO in**} & \multicolumn{2}{c}{**Comparison to MWL of RO in MO-Start Point of**} \\  & \multicolumn{2}{c}{**MO-Endpoint of Arrow**} & \multicolumn{2}{c}{**Increase or Decrease**} & \multicolumn{2}{c}{**Arrow Lines in Figure 7 (Parentheses Are Numbers in Figure 7) [Square Brackets are Difference of**} \\  & \multicolumn{2}{c}{**Line in Figure 7**} & & **MLW]** \\ \hline \multirow{6}{*}{LoA-1} & MO-6 & increase & (1) MO-1 [+39.5], (2) MO-2 [+30.94], (3) MO-5 [+18.00] \\ \cline{2-5}  & MO-5 & increase & (4) MO-1 [+21.5], (5) MO-2 [+12.94] \\ \cline{2-5}  & MO-5 & decrease & (7) MO-4 [\(-\)11.93] \\ \cline{2-5}  & MO-4 & increase & (7) MO-1 [+33.43], (8) MO-2 [+24.87] \\ \cline{2-5}  & MO-6 & increase & (9) MO-1 [+36.87], (10) MO-2 [+26.7], (11) MO-3 [+32.27], (12) MO-4 [+7.5], (13) MO-5 [+16.90] \\ \cline{2-5}  & MO-5 & increase & (14) MO-1 [+19.97], (15) MO-3 [+15.37] \\ \cline{2-5}  & MO-4 & decrease & (16) MO-4 [\(-\)9.40] \\ \cline{2-5}  & MO-4 & increase & (17) MO-1 [+29.37], (18) MO-2 [+19.27], (19) MO-3 [+24.77] \\ \cline{2-5}  & MO-2 & increase & (20) MO-1 [+10.1] \\ \hline \multirow{6}{*}{LoA-3} & MO-6 & increase & (21) MO-1 [+20.90], (22) MO-2 [+19.60], (23) MO-3 [+25.70], (24) MO-5 [+11.50] \\ \cline{2-5}  & MO-5 & increase & (25) MO-3 [+14.20] \\ \cline{2-5}  & MO-4 & increase & (26) MO-3 [+22.64] \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Difference of MWL in scenarios extracted in Figure 7.  
Figure 7: Scenarios that have a significant difference between MO in the same LoA according to \(t\)-test (\(p<0.05\)) (described by arrow lines). Numbers in parentheses are \(p\)-values of the \(t\)-tests.
## 4 Feature Visualization Analysis Based on RDCA
### Convolution Filter and Extracted Features Visualization
Furthermore, the alternating amplitude of L7 indicates that this feature is closest to the characteristic of the flicker signal. Both L5 and L13 demonstrate that the harmonics have more than one line in the feature visualization. Thus, it is revealed that the RDCA can accurately determine the location of the PQDs and categories. Nevertheless, some of the features are hard to be recognized visually. For example, it is difficult to intuitively judge whether L13 contains a sag or swell signal in Figure 6. Besides, owing to the amplitude of spike being small, it is not obvious for finding the difference of L9 by our intuitive feeling. To investigate the meaning of the unintuitive features, we utilize the Deconvnet approach proposed by [PERSON] [29]. The Deconvnet gives an insight into the intermediate feature layers which maps the extracted features back into the input pixel space. The strategy of Deconvnet is comprised of deconvolution and unpooling operations. Typically, the Deconvnet can be obtained by stacking deconvolution and unpooling operations with the weight parameters that have been trained in the RDCA. Following this technology, an exemplary plot of four extracted features and the corresponding Deconvnet analysis results have been presented in Figure 7. The symbol L11-\(i\) and L11-\(d\)-\(i\) represent the feature of RDCA-3 in the \(i\)th layer of L11 and the feature of Deconvnet in \(i\)th layer of L11, respectively. Figure 5: The output of the convolution filter when L13 is the input. Figure 6: The feature visualization of the third layer in RDCA. It can be observed from Figure 7 that each feature corresponds to a part of the original PQD's input image. For example, the part of harmonic in L11-2 is arduous to identify, however, the flicker and harmonic features become clear after the Deconvnet as shown in L11-\(d\)-2. Furthermore, those visually illegible abstract features especially for the high-level features in the third layer, such as the L6-3 and the L11-3, both have a clear and explicable meaning. Even though the features are sometimes illegible, it is to be mentioned that each feature corresponds to a portion of the PQD signal. Overall, the meaning of automatically extracted features has a tremendous difference from the traditional manually selected features. The visualization results as illustrated make automatic feature extraction possible through their differences. However, it is still unknown whether these abstract features can be subdivided. Therefore, a suitable visualization analysis of feature classification is necessary.
## 2 Description of the Models
### Dynamic Model of HGRS
#### 2.2.2 System Model  
When the dynamic model for each element is established, the finite element method is employed to assemble the element models according to the system structural parameters, and the dynamic model of the HGRS can be obtained, as shown in Figure 7.  
The matrix form of motion equations of the HGRS can be expressed as  
\[M_{G}\ddot{q}_{G}(t)+C_{G}\dot{q}_{G}(t)+K_{G}(t)[q_{G}(t)-e_{G}(t)]=F_{G} \tag{11}\]  
Figure 7: Dynamic model of HGRS.  
where \(q_{G}\) is the generalized coordinates of HGRS, \(M_{G}\), \(C_{G}\), \(K_{G}\) are mass matrix, damping matrix and stiffness matrix of HGRS, respectively, \(e_{G}\) denotes the CMS vector of HGRS, \(F_{G}\) refers to load vector of HGRS.  
In the actual operating condition, the gear pair is subjected to dynamic mesh force. For a spur gear system, it is easy to observe the strong nonlinear dynamic behavior (\"jump\" phenomenon), which is caused by tooth separations under resonant condition. However, for a helical gear system, the fluctuation of dynamic mesh force is far less than that of spur gear pairs owing to the much higher contact ratio. The published experimental work shows that strong nonlinear phenomenon (\"jump\" phenomenon) caused by tooth separations usually does not occur even under resonant condition for a helical gear system [21; 30]. Moreover, it is suggested that the dynamic responses of a helical gear system, which is calculated by using quasi-static mesh excitations, is very close to the experimental results in the published works [15; 30]. The research in this paper focuses on cumulative pitch deviations, which have a long period, and the established dynamic model of HGRS has large DOF. Considering the huge computational effort of the three-dimensional dynamic contact model and the conclusions in the published works [15; 21; 30], the quasi-static mesh excitations obtained from the enhanced LTCA model are used to predict the vibration responses of the HGRS.