esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Dynamic Journey-Planning +
## 3 The Multimodal Dynamic Timetable Model
### Improved Query Algorithm  
In order to boost the performance of the query algorithm MDTM-QH, we adapted the ALT heuristic [17]. The combined algorithm is named MDTM-QH-ALT. ALT is a goal-directed technique--that is, its main aim is that of pushing the shortest-path search faster towards the target stop \(t\). This is achieved by adding a _feasible potential_ to the priority-key of each node in the priority queue. The feasible potentials are computed as follows. Given a set of nodes \(L\subseteq V\) called _landmarks_, the feasible potential of a node \(u\in V\) towards a target \(t\) is computed as \(\pi_{t}(u)=\max_{\ell\in L}\max\{dist(u,\ell)-dist(t,\ell);dist(\ell,t)-dist( \ell,u)\}\). By the triangle inequality, it follows that \(\pi_{t}(u)\) is a lower bound to the distance \(dist(u,t)\) and this is enough to prove the correctness of the shortest path algorithm (see [17] for more details). Now, the exploration and settlement of the nodes when running [PERSON]'s algorithm to compute a shortest path from a source node \(s\) to a target node \(t\) is done using the priority \(dist(s,u)+\pi_{t}(u)\) for a node \(u\), instead of \(dist(s,u)\) which is used in the classical [PERSON]'s algorithm. It is easy to see that the tighter the lower bounds are, the more narrow the search space becomes--that is, the faster the query algorithm finds the shortest path. Therefore, choosing good landmarks that provide tight lower bounds is a fundamental part of the preprocessing phase of ALT.  
As in [10], we apply in MDTM an approach similar to that proposed in [18]. In particular, we select as landmarks the switch nodes, each of which represents the arrival node group of a station. Therefore the lower bound distance, \(dist(s_{A},s_{B})\), between two switch nodes, \(s_{A}\) and \(s_{B}\), denotes the minimum travel time among connections traveling from station \(A\) to station \(B\). These lower bound distances can be computed during a preprocessing phase by running single-source queries from each switch node. The tightest lower bounds can be obtained by storing all pair station distances \(O(|\Phi|^{2})\) and by computing all-pairs shortest paths on the condensed version of the input graph (see [18]). This makes sense, particularly when the stations are relatively few in number.  
We combined ALT along with our query algorithm in Section 3.1. This combination (MDTM-QH-ALT) considerably reduces the search space and leads to a more efficient algorithm.
## 1 Introduction
significantly increases average response time for localization. We propose a fast and energy efficient localization method by transferring the location estimation from sensors to base station. Our scheme aims to minimize the number of localization messages while decreasing the average response time and keeping the accuracy considering to the mobility feature of water currents. The rest of the paper is organized as follows: Section 2 discusses the research background. The detailed localization scheme is discussed in Section 3. Section 4 discusses the error analysis. Section 5 shows the simulation results. Advantages and disadvantages of the proposed approach are described in Section 6 and we conclude our work in Section 7.
## 2 Numerical Model
### Fictitious Domain Method  
Suppose that the particle density, volume, moment of inertia, translational velocity, and angular velocity are \(\rho_{s}\), \(V_{p}\), \(\mathbf{J}\), \(\mathbf{U}\), and \(\mathbf{\omega}_{p}\), respectively. The viscosity and density of the fluid are \(\mu\) and \(\rho_{f}\), respectively. Let \(P(t)\) represent the solid domain and \(\Omega\) the entire computational domain containing both solid and fluid regions. We introduce the following characteristic scales for the non-dimensionalization: the cylinder diameter \(D\) for length, the fluid mean velocity \(U_{m}\) for velocity, \(D/U_{m}\) for time, \(\rho_{f}U_{m}^{2}\) for pressure, and \(\rho_{f}U_{m}^{2}/D\) for the pseudo-body force. Then, the dimensionless FD formulation for the incompressible fluid can be written as  
\[\frac{\partial\mathbf{u}}{\partial t}+\mathbf{u}\cdot\
abla\mathbf{u}=\frac{\
abla^{2}\mathbf{ u}}{Re}-\
abla p+\mathbf{\lambda}\quad in\ \ \Omega, \tag{2}\]  
\[\mathbf{u}=\mathbf{U}+\mathbf{\omega}_{p}\times\mathbf{r}\ \ \ in\ \ P(t), \tag{3}\]  
\[\
abla\cdot\mathbf{u}=0\ \ \ in\ \ \Omega, \tag{4}\]  
\[(\rho_{r}-1)V_{p}^{*}(\frac{\mathrm{d}\mathbf{U}}{\mathrm{d}t}-Fr\frac{\mathbf{g}}{g} )=-\int_{p}\mathbf{\lambda}\mathrm{d}\mathbf{x}, \tag{5}\]  
\[(\rho_{r}-1)\frac{\mathrm{d}(\mathbf{J}^{*}\cdot\mathbf{\omega}_{p})}{\mathrm{d}t}=- \int_{p}\mathbf{r}\times\mathbf{\lambda}\mathrm{d}\mathbf{x}. \tag{6}\]  
where \(\mathbf{u}\), \(p\), \(\mathbf{\lambda}\), \(\mathbf{r}\) represent the fluid velocity, pressure, pseudo-body force, and position vector with respect to the mass center of the particle, respectively. \(\rho_{r}=\rho_{s}/\rho_{f}\) is the particle to fluid density ratio, \(Re\) the Reynolds number defined by \(Re=\rho_{f}U_{m}D/\mu\), \(Fr\) the Froude number defined by \(Fr=gD/U_{m}^{2}\), with \(g\) being the gravitational acceleration, \(V_{p}^{*}\) the dimensionless particle volume define by \(V_{p}^{*}=V_{p}/D^{3}\), and \(\mathbf{J}^{*}\) the dimensionless moment of inertia defined by \(\mathbf{J}^{*}=\mathbf{J}^{*}/\rho_{s}D^{5}\). A fractional-step temporal scheme is used to decouple the system (2)-(6) into the following two sub-problems:  
Fluid sub-problem for \(\mathbf{u}^{*}\) and \(p\):  
\[\frac{\mathbf{u}^{*}-\mathbf{u}^{n}}{\Delta t}-\frac{1}{2}\frac{\
abla^{2}\mathbf{u}^{*}} {Re}=-\
abla p-\frac{1}{2}[3(\mathbf{u}\cdot\
abla\mathbf{u})^{n}-(\mathbf{u}\cdot\
abla \mathbf{u})^{n+1}]+\frac{1}{2}\frac{\
abla^{2}\mathbf{u}^{n}}{Re}+\mathbf{\lambda}^{n}, \tag{7}\]  
\[\
abla\cdot\mathbf{u}^{*}=0. \tag{8}\]  
Figure 2: Flow model of a DLD cell containing a stationary cylinder and a moving particle. The periodic boundary condition is imposed in the lateral (\(y\)) direction and the streamwise (\(x\)) direction with a shifted distance. We will introduce the solution of the fluid sub-problem later.
## 3 Experimental and Results
### Physical Properties and Strength
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{4}{c}{**Unweathered Stone**} & \multicolumn{4}{c}{**Weathered Stone (Cored from the Wall)**} \\ \cline{2-10}
**Property** & \multicolumn{2}{c}{**Minimal Value**} & \multicolumn{2}{c}{**Maximal Value**} & \multicolumn{1}{c}{**Average**} & \multicolumn{1}{c}{**Standard Deviation**} & \multicolumn{1}{c}{**Minimal Value**} & \multicolumn{1}{c}{**Maximal Value**} & \multicolumn{1}{c}{**Average**} & \multicolumn{1}{c}{**Standard Deviation**} \\ \hline \multicolumn{10}{l}{Capillary absorption coefficient,} \\ g per m\({}^{2}\) per hour\({}^{\frac{1}{2}}\) & & & & & 1747 & 25,398 & 13,569 & 8924 \\ - & parallel to rift plane & 1601 & 1907 & 1777 & 158 & & & & \\ - & perpendicular to rift plane & 2073 & 3176 & 2765 & 603 & & & & \\ \hline Total water absorption, W, mass & 3.3 & 10.4 & 4.8 & n/a & 3.7 & 13.0 & 7.3 & n/a \\ \hline Saturation (Hirschwald's) coefficient [22] & & & & & 0.76 & 0.80 & 0.78 & n/a \\ - & parallel to rift plane & 0.3 & 0.5 & 0.35 & n/a & & & & \\ - & perpendicular to rift plane & 0.4 & 0.6 & 0.52 & n/a & & & & \\ \hline Water evaporation capacity, w. \%, & & & & & & & & \\ after & & & & & & & & \\ - & 24 k\({}_{\rm c}\) & & & 68\% & & & & 30\% & \\ - & 96 k\({}_{\rm c}\) & & & 90\% & & & & 83\% & \\ - & 168 h & n/a & n/a & 100\% & n/a & n/a & n/a & 91\% & n/a \\ \hline Compressive strength, MPa & & & & & & & & & \\ - & load parallel to rift plane & 6.3 & 34.4 & 14.0 & 8.8 & 6.8 & 25.6 & 11.7 & 8.6 \\ - & load perpendicular to rift plane & 3.7 & 12.5 & 6.6 & 3.0 & 2.1 & 10.1 & 5.1 & 2.2 \\ \hline Modulus of rupture, MPa & & & & & & & & \\ - & parallel to rift plane & 4.2 & 8.8 & 6.2 & 1.7 & 2.8 & 4.0 & 4.0 & 1.4 \\ - & perpendicular to rift plane & 3.3 & 6.0 & 4.9 & 1.1 & n/a & n/a & n/a & n/a \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Properties of weathered versus unweathered kurkar stones. The decreased compressive strength and modulus of rupture, i.e., a drop of 16 to 23% and 18 to 35%, respectively. The capillary absorption rise: 4.9 to 7.6 times as much as of the original unweathered stone. The total absorption has dramatically risen due to weathering, and it was 1.5 times more than in the original unweathered stone. A sharp decline of the initial evaporation capacity was observed: about twice as less as in the unweathered stone. An increase in the saturation (Hirschwald's) coefficient of weathered stones is more than twice as high as observed in the unweathered samples.
## 2 On-Site Measurements
### Measurements  
#### 2.2.1 Temperature  
To determine the seasonal temperature change of the test bridge, the main arch ribs and concrete deck slabs were measured using 24 and 6 thermocouples that had been installed on the main arch ribs and concrete deck slab, respectively (see Figure 2). Additionally, one thermocouple had been installed in the shade on the upper part of the bridge pier in order to measure the ambient temperature. The temperature measurements were carried out from 10 December 2015 to 24 October 2016 (about 10 months).
# Exploring Dentists' Preferences in Selecting Adhesive Systems: A Survey Analysis
###### Abstract
These primers prepare the dental surface by integrating the smear layer into the adhesive interface, effectively utilizing the smear layer as part of the bonding substrate. Consequently, the SE adhesion mode simultaneously demineralizes and infiltrates enamel and dentin dental tissues [7; 8]. Compatibility with various adhesive techniques, including etch-and-rinse (ER), self-etch (SE), and selective etching, is a key feature of universal adhesives (UAs). This versatility has led to their common designation as \"multi-mode\" adhesives. The pH levels of universal adhesives typically range from 1.5 to 3.2, placing most of them in the ultra-mild category (pH > 2.5), mild category (pH around 2), or intermediately strong category (pH between 1 and 2) [7]. Universal adhesives (UAs) were introduced in 2011 and quickly gained popularity among dental professionals due to their unique properties. They can bond to various dental materials, such as ceramics, composites, and metals, while also reducing the number of technical steps required [9; 10; 11]. These adhesives contain specific carboxylate and/or phosphate monomers that bond ionically to calcium from dental structures, such as hydroxyapatite (Ca\({}_{10}\)[PO\({}_{4}\)]\({}_{6}\)[OH]\({}_{2}\)). This category includes methacryloyloxydelyi dihydrogen phosphate (MDP), a functional hydrophilic monomer with mild etching properties [12; 13]. This enables the use of a universal adhesive with any etching technique. Furthermore, this monomer chemically binds to zirconium and metal oxides, thereby expanding the range of applications for universal adhesives. However, several other functional monomers can be found in UAs, including glycerol phosphate dimethacrylate (GPDM), 4-methacryloxyethyl trimellitic acid (4-MET), 4-methacryloxyethyl trimellitate anhydride (4-META), and dipentaerythnitol pentaacrylate phosphate (PENTA) [14]. The wide variety of adhesive systems available on the market from numerous manufacturers makes it challenging for dentists to choose the most suitable adhesive based on materials and techniques, especially when considering different clinical situations. Additionally, there is a lack of studies examining dentists' preferences and decision-making when selecting adhesive agents. The aim of this study was to explore the preferences of dentists in Romania when selecting adhesive agents for direct restorations, as well as the challenges they encounter in this process. Furthermore, this study sought to ascertain whether the time elapsed since graduation influenced the dentists' choices for each procedure.
# Impact and Control of Reflected Noise from an Overpass Bottom
## 1 Introduction
When traffic noise from vehicles on the road under the overpass hits the bottom of the overpass, sound is reflected. Figure 1 shows the formation of reflected noise from the bottom of the overpass. In Taiwan, it is normal to have residential buildings on both sides of the elevated road in urban areas. Therefore, it is common to encounter noise barriers at roadside measurement sites. Although acoustic barriers on either side of the old road were installed to block traffic flow noise, the acoustic barriers are usually not high enough to reach the bottom of the overpass. Therefore, direct noise is conveyed from the gap in the top of the acoustic barriers to the bottom of the overpass, generating reflected noise. The reflected noise then propagates to the residential area on both sides of the road, increasing the overall noise level. As the literature shows, reflected noise increases overall noise levels. Therefore, the effects of reflected noise on overall noise levels and the effects of feasible improvement measures for controlling reflected noise must be assessed. [PERSON] et al. [26], who explored the impact of reflected noise produced in the culvert on I-675 passing underneath Alexanderville-Bellbrook road in Dayton, Ohio, showed that reflected sound was the main cause of increased noise around the highway culvert. Reflected noise typically increases noise levels by at least 5 dB(A). A study by the Washington State Department of Transportation (WSDOT) found that noise around a double-decker road was mostly reflected noise caused by noise from the lower deck of the high-rise expressway hitting the bottom of the upper deck. Simulation results using the Highway Traffic Noise Model (TNM) v. 2.5 (Federal Highway Administration (FHWA), USA) showed that the noise increase caused by reflected noise could be as high as 11 dB(A) [34]. To date, although reflection of traffic noise caused by noise barriers [35; 36; 31] and building facades and structures [28; 37; 38] has been well studied, information regarding noise reflected by the bottom of overpasses is still limited. Through onsite measurements and model simulation, this study explored the effect of noises reflected by the bottom of overpasses under different conditions (different bottom materials, different overpass heights, and different road structures (the overpass is perpendicular or parallel to the source road)). This study also simulated the noise reduction performance of the sound absorbing material installed at the overpass bottom. Assessment results will determine the contribution of reflected noise to overall noise and the efficacy of sound absorbing materials for the abatement of noise levels. The obtained information will be helpful in controlling existed reflected noise near overpasses. The results will also provide useful information to urban design in the future.
## 2 System Requirement
### Button Antenna Topology and Approach  
The process began with designing a button sensor antenna, as shown in Figure 3, for which simulations were conducted using CST Microwave Studio (MWS, Dassault Systems, Framingham, MA, USA). The antenna's radiating part was constructed on the top PCB button Rogers substrate (RT/Duroid5880, Rogers Corporation, Chandler, AZ, USA). This PCB button substrate has a thickness of 1.574 mm, a relative permittivity of 2.2, and a loss tangent of 0.0009, which was placed on a 1.50 mm thick layer of felt substrate with a relative permittivity of 1.4 and a dielectric loss tangent of 0.044 S/m. There is a 3.76 mm air gap between the PCB button substrate and the felt layer substrate. The conductive ground layer is made of ShieldIt, with an area of 45 mm \(\times\) 45 mm, a thickness of 0.17 mm, and an estimated conductivity of \(1.18\times 10^{5}\) S/ m. The ShieldIt layer was glued on the bottom side of the felt substrate. The conductive parts on the top side of the PCB with a radius of 8 mm are as follows: a pin-fed patch on the bottom side of the PCB, and an asymmetrical capacitive patch connected to the bottom patch on the top side, which is also short-circuited to the ground plane by a shorting via. The coaxial feed and the shorting via have diameters of 1.27 mm and 1.22 mm, respectively. Manual cutting tools were used to cut the felt substrate and the ShieldIt ground plane. Glue was used to adhere the ShieldIt ground plane to the felt substrate. For the SMA connector and its galvanic connection to the ShieldIt layer, a through-hole was made. The conductive ShieldIt layer can resist temperatures of up to 250 \({}^{\circ}\)C; therefore, soldering was possible. In addition, the center asymmetrical slot was capacitively coupled to the patch, and the addition of an extra load staircase shape was utilized to achieve a compact size, which is discussed in [49]. The radiating patch located on the top side of the PCB can be considered as a circular loop. Therefore, its radius (_a_) can be calculated by:  
\[a=\frac{F}{\left\{1+\frac{2h}{\pi\epsilon_{r}F}\left[\ln\left(\frac{\pi F}{2h }\right)+1.7726\right]\right\}^{\frac{1}{2}}} \tag{1}\]  
\[F=\frac{8.791\times 10^{9}}{\sqrt[]{\epsilon_{r}}} \tag{2}\]  
where \(h\) is the thickness of the substrate and _\(\epsilon_{r}\)_ is its dielectric constant. Equation (1) does not take the fringing effect into account. Since fringing makes the patch electrically larger, the effective radius (_a_) of the patch must be used, as given by [50].  
\[a_{e}=a\left\{1+\frac{2h}{\pi\epsilon_{r}a}\left[\ln\left(\frac{\pi a}{2h} \right)+1.7726\right]\right\}^{1/2} \tag{3}\]  
\[f_{r}=\frac{1.8412\mathrm{v}_{0}}{2\pi a_{e}\sqrt[]{\epsilon_{r}}} \tag{4}\]  
where \(\mathrm{v}_{0}\) is the free-space speed of light and \(f_{r}\) is the resonance frequency.
# Randomized Feature and Bootstrapped Naive Bayes Classification
## 2 Related Theory
### Gaussian Naive Bayes  
For continuous features, Gaussian naive Bayes assumes that each feature \(X_{i}\) follows a normal (Gaussian) distribution conditional on the class \(Y=y_{k}\). The likelihood \(P(X_{i}|Y=y_{k})\) is modeled as [29]  
\[P(X_{i}|Y=y_{k})=\frac{1}{\sqrt{2\pi\sigma_{ik}^{2}}}\exp\Biggl{(}\frac{-(X_{i }-\mu_{ik})^{2}}{2\sigma_{ik}^{2}}\Biggr{)}, \tag{6}\]  
where \(\mu_{ik}\) and \(\sigma_{ik}^{2}\) are the mean and variance of feature \(X_{i}\) for class \(y_{k}\). These parameters are estimated from the training dataset, with the mean and variance computed as  
\[\mu_{ik}=\frac{1}{N_{k}}\sum_{j=1}^{N_{k}}X_{i}^{(j)},\,\,\,\sigma_{ik}^{2}= \frac{1}{N_{k}}\sum_{j=1}^{N_{k}}\Bigl{(}X_{i}^{(j)}-\mu_{ik}\Bigr{)}^{2}, \tag{7}\]  
where \(N_{k}\) is the number of training samples in class \(y_{k}\) and \(X_{i}^{(j)}\) denotes the \(i-\)th feature value of the \(j-\)th training sample. The posterior probability for Gaussian naive Bayes becomes  
\[P(Y=y_{k}|X)\propto P(Y=y_{k})\prod_{i=1}^{n}\frac{1}{\sqrt{2\pi\sigma_{ik}^{ 2}}}\exp\Biggl{(}\frac{-(X_{i}-\mu_{ik})^{2}}{2\sigma_{ik}^{2}}\Biggr{)}. \tag{8}\]  
Using logarithms, the posterior simplifies to  
\[\log P(Y=y_{k}|X)=\log P(Y=y_{k})-\sum_{i=1}^{n}\Biggl{(}\frac{1}{2}\log\Bigl{(} 2\pi\sigma_{ik}^{2}\Bigr{)}+\frac{\left(X_{i}-\mu_{ik}\right)^{2}}{2\sigma_{ik }^{2}}\Biggr{)}. \tag{9}\]
## 3 Methodology
### Pre-Processing  
After the inspection of the signal characteristics in the frequency domain, the filter parameters and type were defined accordingly to the frequencies of interest. In terms of the EMG signals, analysis of the periodograms revealed that the majority of the content of interest was above 20 Hz. Considering the absolute and relative error, coefficient of variation, and signal-to-noise ratio (SNR), the filter with the best response to the frequency of interest was the Butterworth high-pass filter of order 4, with a cutoff frequency of 20 Hz (notice that the electrical interference, at 50 Hz, was removed by the signal collection software). Analysis of the ECG signals' periodograms revealed that the content of interest was less than 40 Hz. Several filters were then applied, and based on the absolute and relative error, and coefficient of variation, we applied a Butterworth band-pass filter of order 4 for the pass band (0.5 Hz, 40 Hz).  
Figure 1: Odour Perception Task.
## 4 Discussion
### Limitations and Future Perspectives  
The inherent laboratory nature of the included studies can serve as a limitation. In vitro studies do not replicate chewing load conditions or the thermal changes that occur orally. Likewise, setting conditions at a clinical level are not as controllable as in a laboratory and may affect microleakage. Due to the heterogeneity of the included studies, a meta-analysis could not be performed. Diverse materials were tested under different situations (different study groups, methodologies in manipulating the sample, primary or permanent, Class I and Class V, microleakage assessment method, and additional outcome measures). In addition, regarding newly modified GICs, only one study was included in the review for each assessed formulation, hindering the comparison of the obtained results. Therefore, in vitro studies and clinical trials are necessary to complete the information regarding their properties and clonal applications.  
Another limitation could be the sample size of the studies included in the review, given that an adequate sample size calculation was presented in only two studies. Studies could also be influenced by reporting bias, as shown by the unfulfilled items in the quality assessment. At a review level, studies not included in the databases used could have been left out of the quality assessment.  
The findings of the present systematic review represent the available evidence on the microleakage of various types of GICs in primary and permanent human teeth and may serve for future studies. Future investigations should use standardized laboratory procedures to evaluate the microleakage of newly modified GICs as restorative materials for primary and permanent human teeth in order to confirm the obtained results.
# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 3 System Model
### Link-State Aware Hybrid Routing Algorithm
#### 4.2.2 Hybrid Routing Algorithm Based on Bipartite Graph
Since satellite-3 can receive a beacon packet from satellite-1 and satellite-2, they will be marked as alive neighbors of satellite-3. The link between satellite-1 and satellite-3 is interrupted, thus satellite-1 will be deleted from the alive neighbor list of satellite-3. In this way, the connectivity of adjacent satellites can be gathered by TSEs, and will not increase the signaling overhead. Input:Network topology;  
Output: Link planning of hybrid routing \(R_{h}\)  
1:Initialize:  
Update \(R_{s}\) by executing Algorithm 2  
2:If \(t\leq D\)then  
Construct bipartite graph of \(G^{{}^{\prime}}(t)\)  
Calculate link metric of \(G^{{}^{\prime}}(t)\) according to (24)  
Calculate the minimum weight match of \(G^{{}^{\prime}}(t)\) by using Algorithm3,  
Extract hybrid path planning in t-th \(R(t)\)  
Write in link connection planning \(R_{h}\)  
Update data volume of link according (25)  
\(t\gets t+1\)  
End  
10:Return \(R_{h}\)  
**Algorithm 4** KM-based Link-state Aware Terrestrial-Satellite Hybrid Routing algorithm