esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background--The aim was to determine the influence of sonic activation in the tubular dentine penetration of biocearamic sealers. Methods--Forty mesibucocal curved root canals of mandibular molars with an apical diameter smaller than #30 were prepared, divided into two groups, and filled with EndoSequence BC sealer, with or without sonic activation during its placement. Roots were sectioned at 3 mm, 6 mm, and 9 mm from the apex, producing a sample size of 120. The samples were evaluated using a confocal laser scanning microscope and comparing these images to the images obtained from an operator microscope. The percentage of sealer penetration and maximum sealer penetration were evaluated. Statistical analysis was performed using the two-tailed Mann-Whitney \(U\) test, where statistical significance was set to \(p\) < 0.05. Results--Sonic activation showed higher values for the percentage of sealer penetration when compared at the 9 mm level (_p_ = 0.03). A higher value of maximum sealer penetration was observed at all levels when the sealer was activated. Conclusions--The sonic activation of biocearamic cement resulted in higher sealer penetration into dentinal tubules.  
CLSM; Endoactivator; EndoSequence BC sealer; sonic activation; tubular penetration +
Footnote †: journal: Applied Sci.  
entomb any residual bacteria [8]. The dentist's knowledge of the sealer's behavior improves the clinical outcome [9]. A sealer's antibacterial activity is proportional to its tubular penetration [10]. In fact, BC Sealer eliminates bacteria beyond the tubular penetration of the sealer [11]. This could be carried out through ultrasonic or sonic activation of endodontic sealers, which have been proposed to improve the quality of root filling [11; 12]. Sonic activation uses low-frequency vibration (1-6 kHz) through flexible tips, which when combined with short pecking movements inside the root canal, act in a synergistic way by creating a hydrodynamic phenomenon that is responsible for the increased penetration of the sealer into lateral canals [12]. The aim of the present study was to determine whether sonic activation increased the dentinal tubule penetration of EndoSequence BC sealer in curved root canals.
## 3 Artificial Intelligence as Enabler for Adoption of Nuclear Maritime Ships  
Advances in computing and artificial intelligence emerged during a period when nuclear-powered maritime vessels, aside from Arctic icebreakers, were virtually nonexistent, as stated earlier. Consequently, the application of AI in nuclear maritime ships remains largely unexplored. However, this limited precedent also presents an opportunity to integrate AI into the field. The success of AI in related domains [38], such as civilian nuclear power [39], highlights its potential to enable the adoption of nuclear-powered ships by enhancing their operational efficiency and diminishing cost.  
This section identifies the areas in maritime vessels where AI can be utilized while exploring future opportunities for AI in each of the areas. Given the scarcity of current solutions, the focus shifts toward future possibilities driven by advancements in related fields. Notably, nuclear power and AI are distinct domains, but their integration within the maritime industry could significantly contribute to the broader adoption of nuclear-powered ships.  
AI has the potential to offer a wide range of solutions for nuclear-powered maritime vessels within the context of the aforementioned unique challenges, significantly impacting environmental sustainability. Specifically, it can help reduce operational costs and minimize the environmental footprint by optimizing the cost-efficient use of various onboard processes. In practice, AI and nuclear power have yet to coexist on ships, resulting in limited practical experience and a highly premature stage of research. Therefore, this paper explores solutions informed by experience from other domains and how they are envisioned to optimize nuclear power utilization. Table 1 provides a summary of the areas where AI may provide significant solutions.  
It should be noted that implicitly, the use of AI in the areas provided in Table 1 is related either directly or indirectly to the overcoming of economic, regulatory, and public acceptance barriers. For instance, the monitoring of reactor systems and the transmission of data via satellites to regulatory agencies satisfy regulatory requirements. Another example is the use of AI in radiation protection which enhances the public acceptance of the peaceful use of nuclear power in maritime vessels.
## 3 Materials and Methods
### Architecture
#### 3.2.4 Enhanced Positional Encoding  
In time series prediction tasks, the sequential positional relationships of sample points are crucial. However, the attention mechanism in Transformers is permutation invariant, discarding the positional information of tokens in the input sequence. The model's ability to capture temporal relationships relies heavily on the positional encoding (PE) of input tokens. Without positional information, the model cannot perceive temporal variations, hindering its ability to compute temporal dependencies. To address this limitation, positional encoding--such as sinusoidal PE and learnable PE--has been introduced in related Transformer work to mitigate the issues arising from permutation invariance.  
While positional encoding helps retain some ordering information, the inherent nature of permutation-invariant attention mechanisms can lead to the loss of temporal information as network depth increases. To overcome this, positional encoding in standard time series prediction models is typically embedded only at the input layer, resulting in a gradual reduction of positional information with increasing network depth. We propose incorporating PE at the input of each attention layer to reinforce positional information, termed enhanced PE. Enhanced PE incorporates positional encoding information during the generation of Q and K using sinusoidal PE to construct PE for each layer, thereby addressing the issue of reduced positional information as network depth grows, as shown in Figure 3.  
Figure 3: Enhance positional encoding.
## 1 Introduction
### Discovery  
Fluoropolymers have existed for nearly a century and are popular in various commercial, industrial, and military products because of their ability to create water resistant, low friction coatings, and flame-retardant products [1]. Polymers that comprise fluorine atoms on the carbon backbone have unique physical properties when compared to a hydrocarbon polymer. The replacement of hydrogen atoms with fluorine contributes to the overall stability of the fluoropolymers due in part to the significant difference in the average bond energy between the C-F bond (565 kJ/mol) and the C-H bond (414 kJ/mol), due to the high electronegativity of fluorine [24; 25]. The overall stability of the fluoropolymers arises from the strength of the C-F bond, which is one of the strongest single bonds [26]. The added bond strength and stability of the adjacent C-F and C-C bonds arises from the multiple C-F bonds on the same geminal carbons on the skeletal backbone of the polymer which induces a strong positive partial charge on the carbon atoms [25]. The intramolecular dipole moments within the structure of fluorocarbons, specifically within the individual C-F bonds, cancel each other resulting in non-polar dispersion intermolecular forces across an overall large molecular cross-sectional area that results in hydrophobicity of the fluorinated moiety [27]. Additional key properties of fluoropolymers are their tendency to possess high chemical and thermal stability and low coefficient of friction which are critical properties in the use and application of such products as polytetrafluoroethylene (PTFE). The history of the fluoropolymer industry and its widespread use began with the discovery of (PTFE) by [PERSON] at Dupont in 1938 [28], to advancements in characterization of physical and chemical properties of other fluorinated polymers over recent decades, and now to the modern-day uses, novel-synthesis methods, industrial viability, and general environmental concerns of the fluoropolymer industry in the 21 st century [7; 20; 29].
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 3 Results
### On the Remarkably Large and Stable Characteristics of the Copolar Correlation Coefficient  
The value range for \(\rho^{HV}\) is between 0 (no correlation between the two polarizations) and 1 (perfect correlation). On the one hand, if the targets within the radar sampling volume were identical, then the time series of the signals at the horizontal and vertical polarizations would be perfectly correlated.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\pm\sigma\{\textbf{10}\cdot\textit{Log}(\textit{ZH}/\textit{ZV})\}\) & \(\pm\sigma\{\textbf{10}\cdot\textit{Log}(\textit{ZH})\}\) & \(\pm\sigma\{\textbf{10}\cdot\textit{Log}(\textit{ZV})\}\) \\ \hline
5 January 2015 & \(\pm\)0.7 dB & \(\pm\)1.0 dB & \(\pm\)1.5 dB \\
9 February 2015 & \(\pm\)0.7 dB & \(\pm\)0.6 dB & \(\pm\)0.8 dB \\
10 February 2015 & \(\pm\)0.8 dB & \(\pm\)0.7 dB & \(\pm\)1.0 dB \\
13 October 2016 & \(\pm\)0.9 dB & \(\pm\)0.9 dB & \(\pm\)1.2 dB \\
18 January 2017 & \(\pm\)1.0 dB & \(\pm\)0.8 dB & \(\pm\)1.1 dB \\ \hline \hline \end{tabular}
\end{table}
Table 6: Standard deviation of the differential reflectivity, the horizontal reflectivity and the vertical reflectivity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\pm\left(\textbf{Z}_{84}^{dr}-\textbf{Z}_{16}^{dr}\right)/\textbf{2}\) & \(\overline{Z^{dr}}\) & \(\textbf{Z}_{50}^{dr}\) \\ \hline
5 January 2015 & \(\pm\)0.7 dB & 1.8 dB & 1.7 dB \\
9 February 2015 & \(\pm\)0.7 dB & 0.8 dB & 0.7 dB \\
10 February 2015 & \(\pm\)0.9 dB & 1.1 dB & 1.1 dB \\
13 October 2016 & \(\pm\)1.0 dB & 1.0 dB & 1.0 dB \\
18 January 2017 & \(\pm\)1.1 dB & 1.1 dB & 1.1 dB \\ \hline \hline \end{tabular}
\end{table}
Table 5: Spread, average and median values derived using 5-min differential reflectivity samples.  
On the other hand, the greater the variability in the shapes of the targets, the less likely it is that the two signals will be correlated. Hence, the copolar correlation coefficient is a good measurement of shape diversity: the light rain and drizzle and echoes (homogeneous liquid precipitation) are associated with very large values of \(\rho^{HV}\), most of which are larger than 0.995; the \(\rho^{HV}\) values in melting snow are lower (generally between 0.8 and 0.9) and make the melting layer easily distinguishable. If the sampling volume contains a significant number of large non-meteorological targets, such as biological targets (birds, insects etc.) or ground clutter, the \(\rho^{HV}\) values will decrease considerably. The range of most ground clutter or anomalously propagated echoes is between 0.65 and 0.95.
## 3 Results  
Table 1 presents the descriptive data of the mechanical parameters of the Hzt FV profile and COD performance tests expressed as mean \(\pm\) SD. Using the Kruskal-Wallis test, significant differences were found between the study groups for all variables tested [F\({}_{0}\) (X\({}^{2}\) = 14.94, \(p\) = 0.002); P\({}_{\text{max}}\) (X\({}^{2}\) = 10.83, \(p\) = 0.013); T10m (X\({}^{2}\) = 11,36 \(p\) = 0.010); 505 test (X\({}^{2}\) = 32.85, \(p\) = 0.000); M505 test (X\({}^{2}\) = 17.53, \(p\) = 0.001); 505_CODD (X\({}^{2}\) = 18.73, \(p\) = 0.000); M505_CODD (X\({}^{2}\) = 8.82, \(p\) = 0.032)] except for the variable V0 (X\({}^{2}\) = 0.86, \(p\) = 0.835). In addition, using the U Mann-Whitney test, significant differences were found in the following group comparisons: F\({}_{0}\) [U18 LN (\(n\) = 24)--Professional (\(n\) = 20), U = 25.4, \(p\) = 0.001]; P\({}_{\text{max}}\) [U18 LN (\(n\) = 24)--Professional (\(n\) = 20), U = 21.9, \(p\) = 0.007]; T10m [U18 LN (\(n\) = 24)--Professional (\(n\) = 24)--Professional (\(n\) = 20), U = \(-\)22.4, \(p\) = 0.006]; 505 test [U18 LN (\(n\) = 24)--Professional (\(n\) = 20), U = \(-\)37.7, \(p\) = 0.000], [U18 LN (\(n\) = 24)--Amaeturs (\(n\) = 12), U = 25.75, \(p\) = 0.007], [U18 LN (\(n\) = 24)--U18 DH (\(n\) = 21), U = \(-\)22.714, \(p\) = 0.004]; M505 test [U18 LN (\(n\) = 24)--Professional (\(n\) = 20), U = \(-\)26.9, \(p\) = 0.000], [U18 LN (\(n\) = 24)--Amaeturs (\(n\) = 12), U = \(-\)21.25, \(p\) = 0.043]; and 505_CODD [U18 LN (\(n\) = 24)--Professional (\(n\) = 20), U = \(-\)6.06, \(p\) = 0.000], [U18 LN (\(n\) = 24)--Amaeturs (\(n\) = 12), U = \(-\)21.43, \(p\) = 0.040]. Figure 1 shows the correlations between the mechanical variables of the Hzt FV profile and both tests (505 test and M505 test) separated by groups. Significant and high correlation between V\({}_{0}\)--505 test [Professional (r\({}_{\rm s}=-0.682\), \(p<0.01\)); Amateurs (r\({}_{\rm s}=-0.721\), \(p<0.01\)); U18 DH (r\({}_{\rm s}=-0.736\), \(p<0.01\)); U18 LN (r\({}_{\rm s}=-0.569\), \(p<0.01\))] and between F\({}_{0}\)--M505 test [Professional (r\({}_{\rm s}=-0.468\), \(p<0.05\)); Amateurs (r\({}_{\rm s}=-0.690\), \(p<0.01\)); U18 DH (r\({}_{\rm s}=-0.642\), \(p<0.01\)); U18 LN (r\({}_{\rm s}=-0.658\), \(p<0.01\))] could be highlighted. Figure 2 displays the correlations between CODD and the mechanical variables of the Hzt FV profile separated by groups. Significant and high correlations between V\({}_{0}\)--505_CODD in the Professional group (r\({}_{\rm s}=-0.511\), \(p<0.05\)) and between F\({}_{0}\)--505_CODD [Amateurs (r\({}_{\rm s}=0.622\), \(p<0.05\)); U18 DH (r\({}_{\rm s}=0.626\), \(p<0.01\)); U18 LN (r\({}_{\rm s}=0.510\), \(p<0.01\))] could be highlighted.
# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 4 Proposed rStaple Tracking Method  
In this section, the detailed procedure of the proposed real-time object tracking algorithm termed the robust sum of template and pixel-wise learners (rStaple) is presented. It is based on the Staple [4], an effective tracker which has been widely used in visual tracking.  
To initialize the tracker in the first frame of video sequence, the proposed method primarily extracts fHOG [6] and CN [7; 8] from the searching area around the given target position. The combination of these two handcrafted features is used to train a translation filter \(F_{T}\). Secondly, the histogram models of the foreground and background are established by current target information. Finally, our tracker trains a 1-D scale filter with fHOG extracted from the initial searching area.  
In subsequent frames, firstly, the combination of fHOG and CN of the searching area are extracted to compute a translation filter response map via the translation filter \(F_{T}\) learned in previous frames. Then, we compute the color histogram pixel score map by the histogram model of previous frames, and apply a significant enhancement to the original pixel score map. The final translation score map is a linear combination of the above translation filter response map and the enhanced color histogram score map. Thus, the target position in this frame can be obtained via the final translation score map. Meanwhile, we use fHOG extracted in the current frame and the scale filter learned in the previous frames for scale estimation. Note that the scale estimation can be obtained directly based on the estimated translation result, which greatly reduces the computational cost. Finally, quantitative indicators average peak-to correlation energy (_APCE_) [44] and \(F_{\text{max}}\) are introduced to detect the state of tracking target for the adaptive model update. If the indicators computed from the translation filter response map satisfy the predetermined condition, the translation filter and scale filter are updated in the current frame. Figure 1 illustrates the overall procedure of the proposed rStaple. The area in yellow represents the estimation part in frame \(t\) and the green area is the training part in frame \(t\). In particular, the training part is performed after estimation.  
Figure 1: Overview of the proposed robust sum of template and pixel-wise learners (rStaple) algorithm.
## 4 Discussion  
### Evaluation of the Methodology  
Our sea-bottom screening method has several advantages: The total cost for equipment was, 666 EUR, relatively low. With 0.3 to 10 min (median around 1 min), the time required to cover one square meter of harbor bottom is relatively short. The system is simple, easy to learn and easy to apply. It is mobile and portable, and allows relatively fast comparative studies in very different environmental and regional settings. Several minor technical improvements could further improve the method, such as a stronger underwater light, an increased stick length, or an automatic distance measurement between camera and bottom.  
However, the method also has several weaknesses: Objects are often overgrown or covered with organic material. Therefore, a manual analysis of the photos with bare eyes is recommendable. Going into detail, this is time-consuming and the identification of objects is biased because it depends on the size, color, state and material of the object. Consequently, not all objects have the same likelihood of being found, and larger, colorful and freshly deposited items are favored. In addition, the method and the quality of the resulting data are highly dependent on the water transparency [32]. It is more suitable for clear-water seas, such as the Mediterranean Sea, and is problematic and time-consuming in turbid systems, such as coastal lagoons. How well and how long an object can be identified after deposition, depends on how fast it is overgrown or buried by sediment. This depends on environmental conditions, such as light conditions, nutrient availability and productivity, as well as sediment reallocation. These factors vary greatly from harbor to harbor. Therefore, for how long an identified object was located on sediment can hardly be determined. The method, and how we applied it, does allow the calculation of deposition rates, e.g., the number of pieces per m\({}^{2}\) and month. Despite a considerable time and effort, the number of particles found in several harbors was too low to allow a detailed item analysis.  
The method is fast because it is carried out from solid harbor walls, wooden bridges or fixed floating pontoons. The resulting weakness is that only the nearby bottom pollution is assessed which can hardly be considered spatially representative. We have tried to compensate for this effect by using a large number of sampling locations within each harbor. However, in most cases less than 1% of the total harbor area was sampled.  
For a spatially representative sampling, a boat or a float could be used, but this would increase the time required. Further, it would increase the difficulty of obtaining permission for field studies in harbors and marinas. Permissions were a major challenge in all African harbors and restricted the choice of harbors and the extent of our field studies. This was a major reason, too, for not applying underwater drones for our study. We tested the use of different underwater drones in German harbors but faced additional problems that made the approach ineffective [32].
# Passively Q-Switched Operation of a Tm,Ho:LuVO\({}_{4}\) Laser with a Graphene Saturable Absorber
###### Abstract  
A passively Q-switched (PQS) operation of Tm,Ho:LuVO\({}_{4}\) laser is experimentally demonstrated with a graphene saturable absorber (SA) mirror. An average output power of 1034 mW at 54.5 kHz is acquired with an 8% optical-optical conversion efficiency. The energy per pulse of 40.4 \(\upmu\)J and a peak power of 56.07 W are achieved; the narrowest pulse width of 300 ns is acquired, and the output wavelengths of Tm,Ho:LuVO\({}_{4}\) are 2075.02 nm in a continuous wave (CW) regime and 2057.03 nm in a PQS regime.  
Keywords:Tm,Ho:LuVO\({}_{4}\) laser; PQS; graphene saturable absorber +
Footnote †: journal: Applied Physics Letters
## 2 Problem Formulation
### Nonlinear Attitude Model of HSV with Unexpected Centroid Shift
#### 2.1.2 Attitude Angular Rates Affine Nonlinear Equations of HSV with Centroid Shift  
In standard aircraft modeling, the mass distribution of an aircraft is assumed to be symmetric with respect to the x-z plane, such that the cross products of the inertial forces \(J_{xy}\) and \(J_{yz}\) are zero [26]. However, with the unexpected centroid shift (\(O_{b}\) moving to the original location \(O_{b}^{\prime}\), as shown in Figure 1), \(J_{xy}\) and \(J_{yz}\) become nonzero leading to the following moment equations for HSV [7; 9; 25]:  
\[J_{xx}\dot{p} -J_{xy}\dot{q}-J_{xz}\dot{r}+J_{xy}pr-J_{xz}pq+(J_{zz}-J_{yy})qr+( r^{2}-q^{2})J_{yz}+m((pV_{A}-qU_{A}+\dot{W}_{A})\Delta y\] \[+(pW_{A}-rU_{A}-\dot{V}_{A})\Delta z)=L+mg\cos\theta\cos\phi\Delta y -mg\cos\theta\sin\phi\Delta z\] \[-J_{xy}\dot{p} +J_{yy}\dot{q}-J_{yz}\dot{r}+J_{yz}pq-J_{xy}qr+(J_{xx}-J_{zz})pr+( p^{2}-r^{2})J_{xz}+m((qU_{A}-pV_{A}-\dot{W}_{A})\Delta x\] \[+(qW_{A}-rV_{A}+\dot{U}_{A})\Delta z)=M-mg\cos\theta\cos\phi \Delta x-mg\sin\theta\Delta z\] \[-J_{xz}\dot{p} -J_{yz}\dot{q}+J_{zz}\dot{r}+J_{xz}qr-J_{yz}pr+(J_{yy}-J_{xx})pq+ (q^{2}-p^{2})J_{xy}+m((rU_{A}-pW_{A}+\dot{V}_{A})\Delta x\] \[+(rV_{A}-qW_{A}-\dot{U}_{A})\Delta y)=N+mg\cos\theta\sin\phi \Delta x+mg\sin\theta\Delta y \tag{6}\]  
where \(J_{xx},J_{yy}\) and \(J_{zz}\) represent the moments of inertia. \(J_{xy},J_{xz}\) and \(J_{yz}\) are the products of inertia of HSV. In addition, \([U_{A}\ V_{A}\ W_{A}]^{T}\) and \([\dot{U}_{A}\ \dot{V}_{A}\ \dot{W}_{A}]^{T}\) are the states of HSV with respect to the aircraft body frame, as shown in (A4). For the convenience of theoretical analysis, \(v_{A}=[U_{A}\ V_{A}\ W_{A}]^{T}\) is introduced as the velocity vector of HSV.  
Then, through a close inspection of (6), the affine attitude angular rate subsystem of HSV can be reshaped as  
\[\dot{\omega} =-J^{-1}\omega\times J\omega-J^{-1}(m\bar{\rho}\times\dot{v}_{A} +m\omega\times(\bar{\rho}\times v_{A})+mv_{A}\times(\omega\times\bar{\rho})- \bar{\rho}\times W)\] \[\quad+J^{-1}(\tau+\Delta\tau)+J^{-1}d_{f}\] \[=-J^{-1}\omega\times J\omega+J^{-1}\tau-J^{-1}f(\bar{\rho})+J^{-1 }d_{f}+J^{-1}\Delta\tau \tag{7}\]  
where \(J=J^{*}+\Delta J\) represents the inertial matrix of HSV, where \(J^{*}\) stands for the inertial matrix without centroid shift and \(\Delta J\) is the uncertainties of inertial matrix due to centroid shift, and the details are explained later. \(d_{f}\) is the external disturbance. Furthermore, \(W=[-\sin\theta\ \cos\theta\sin\phi\ \cos\theta\cos\phi]^{T}\) is the gravitational force vector.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 6 Mode Selection  
The user equipment can communicate with the BS directly in the D2D cellular network. This capability is beneficial, and it significantly improves the network performance in terms of delay and network throughput. Nevertheless, D2D also creates other new challenges in terms of resource management and network overloading. Besides, the network management becomes more complex as two or more pieces of user equipment use the same mode, hybrid mode, and different mode to communicate. Table 6 shows a brief summary of mode selection issues and proposed solutions in D2D communication. Generally, there are four modes of communication for the user equipment:  
1. Pure cellular mode--used when low resources are available and there is high interference because there is no D2D communication. This does not allow D2D users to transfer their data;
2. Partial cellular mode--without sharing the co-channel spectrum, this mode allows the UEs to communicate through the BS;
3. Dedicated mode--allows the user equipment to communicate with other user equipment using dedicated spectrum resources;
4. Underlay mode--allows D2D users and cellular user equipment to share the uplink and downlink resources.