esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We investigated the thermoluminescence (TL) properties of Mn-doped zinc zirconate (ZnZrO\({}_{3}\):Mn) phosphors under beta (\(\beta\)) radiation. SEM revealed morphological changes with varying levels of Mn doping (0-5%), while XRD confirmed a pure cubic phase. Mn doping introduced luminescent centers, enhancing emission efficiency. Mn\({}^{2+}\) ions facilitated green/red emissions (\({}^{4}\)T\({}_{1}\)\(\rightarrow\)\({}^{6}\)A\({}_{1}\)), while Mn\({}^{4+}\) contributed to deep-red emissions (\({}^{2}\)E \(\rightarrow\)\({}^{4}\)A\({}_{2}\)), making the material suitable for optoelectronic applications. Compared to conventional phosphors, ZnZrO\({}_{3}\):Mn exhibited superior thermal stability, enhanced luminescence, and tunable emissions. The TL dose\(-\) response showed a systematic peak shift to higher temperatures with increasing radiation dose, confirming its potential for use in accurate dosimetry. The TL glow curves displayed primary (349 K) and secondary (473 K) peaks that were influenced by heating-rate variations, which led to peak shifts and increased intensity. An innovative thermal-cleaning approach (110-336 \({}^{\circ}\)C) refined luminescence by stabilizing deeper traps while erasing shallow-trap signals. This combined effect of Mn doping and thermal treatment optimized ZnZrO\({}_{3}\) phosphors' structural, optical, and TL properties. These findings provide insights into their potential use in radiation dosimetry and display technologies, offering a new strategy for future perspective luminescent materials  
zinc zirconate; phosphors; dopants; glow curves; dose\(-\)response
## 5 Conclusions  
The controlled maximum possible cooling rate is limited due to the fundamental heat transfer limitation associated with the thermal resistance at the membrane/gas interface. Heat losses from the membrane due to thermal conductivity \(\lambda\) of the surrounding gas are proportional to the heat transfer parameter \(\alpha=\lambda/r_{0}\), which increases with decreasing of the radius of the membrane hot zone \(r_{0}\) if \(L_{t}\gg r_{0}\gg l_{mfp}\). However, the possibility of decreasing the radius \(r_{0}\) is limited by the temperature jump distance \(\delta\). Thus, it makes no sense to reduce the radius \(r_{0}\) to less than 1 \(\upmu\)m, at least for the most heat-conducting gases hydrogen and helium. In fact, this limitation is associated with the maximum possible value of the heat flux \(G_{max}(T-T_{t})\) at the membrane/gas interface, where the maximum interfacial thermal conductance \(G_{max}\) is about 0.9 MW/m\({}^{2}\)K and 0.42 MW/m\({}^{2}\)K for a blackened membrane (\(\sigma=1\)) and the most heat-conducting gases hydrogen and helium, respectively. Thus, for a free blackened membrane, the maximum cooling rate \(2G_{max}(T-T_{t})/(\rho_{m}c_{m}d_{m})\) is about 3.9 \(\cdot\) 10\({}^{8}\) K/s and 1.8 \(\cdot\) 10\({}^{8}\) K/s at \((T-T_{t})=\) 500 K for hydrogen and helium gases, respectively. Solving the heat transfer problem for the complete membrane-gas system, we find that the maximum possible controlled cooling rate is about \(3\cdot 10^{8}\) K/s for a silicon nitride membrane with a thickness of 1 \(\upmu\)m in helium gas. However, the controlled cooling rate can be increased using the combined membrane with a higher thermal conductivity. Thus, a controlled cooling rate of the order of billions of K/s (up to 10\({}^{10}\) K/s) is possible for helium gas, \(r_{0}=1\)\(\upmu\)m, and \((T-T_{t})=\) 1000 K.  
Conceptualization, A.A.M.; formal analysis, A.A.M.; funding acquisition, C.S.; methodology, A.A.M.; supervision, C.S.; visualization, A.A.M.; writing--original draft, A.A.M.; writing--review and editing, C.S. Both authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education and Science of the Russian Federation, grant number 14.Y26.31.0019. The APC was funded by Deutsche Forschungsgemeinschaft and Universitat Rostock within the funding program Open Access Publishing.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
CS acknowledges financial support from the Ministry of Education and Science of the Russian Federation grant 14.Y26.31.0019.  
The authors
###### Abstract  
In this study, we evaluate the influence of implant neck wall thickness and abtument screw size on alveolar bone and implant component biomechanical responses using nonlinear finite element (FE) analysis. Twelve internal hexagon Morse taper implant-abtument connection FE models with three different implant sizes (diameters 4, 5, and 6 mm), secured with 1.4, 1.6, and 1.8 mm abtument screws to fit with three unilateral implant neck wall thicknesses of 0.45, 0.50, and 1.00 mm, were constructed to perform simulations. Nonlinear contact elements were used to simulate realistic interface fixation within the implant system. A 200 N concentrated force was applied toward the center of a hemispherical load cap and inclined 30\({}^{\circ}\) relative to the implant axis as the loading condition. The simulation results indicated that increasing the unilateral implant neck wall thickness from 0.45 to 1.00 mm can significantly decrease implant, abtument, and abtument screw stresses and bone strain, decreased to 58%, 48%, 54%, and 70%, respectively. Variations in abtument screw size only significantly influenced abtument screw stress, and the maximum stress dissipation rates were 10% and 29% when the diameter was increased from 1.4 to 1.6 and 1.8 mm, respectively. We conclude that the unilateral implant neck wall thickness is the major design factor for the implant system and implant neck wall thickness in effectively decreasing implant, abtument, and abtument screw stresses and bone strain.  
biomechanics; dental implant; finite element analysis; implant neck wall; abtument screw Article
###### Abstract  
This paper mainly deals with the problem of short text classification. There are two main contributions. Firstly, we introduce a framework of deep uniform kernel mapping support vector machine (DUKMSVM). The significant merit of this framework is that by expressing the kernel mapping function explicitly with a deep neural network, it is in essence an explicit kernel mapping instead of the traditional kernel function, and it allows better flexibility in dealing with various applications by applying different neural network structures. Secondly, to validate the effectiveness of this framework and to improve the performance of short text classification, we explicitly express the kernel mapping using bidirectional recurrent neural network (BRNN), and propose a deep bidirectional recurrent kernel mapping support vector machine (DRKMSVM) for short text classification. Experimental results on five public short text classification datasets indicate that in terms of classification accuracy, precision, recall rate and F1-score, the DRKMSVM achieves the best performance with the average values of accuracy, precision, recall rate, and F1-score of 87.23%, 86.99%, 86.13% and 86.51% respectively compared to traditional SVM, convolutional neural network (CNN), Naive Bayes (NB), and Deep Neural Mapping Support Vector Machine (DNMSVM) which applies multi-layer perceptron for kernel mapping.  
short text classification; support vector machine; recurrent neural network; kernel mapping 1
## 2 Related Works  
### Optical See-Through and Video See-Through  
The techniques used in augmented reality are typically divided into optical see-through, and video see-through [6; 7]. Figure 2 shows that the optical see-through system can simultaneously view the images rendered by the graphics system while viewing the real world through the optical combiner.  
Figure 1: Recent Augmented Reality Devices (**Left**: Microsoft Hololens, **Right**: Epson Moverio).  
Figure 2: System Scenario of the Optical See-Through Method.  
At this time, a marker-based AR for recognizing a marker through a camera or a non-marker recognition AR using a camera and a sensor outputs a virtual object to the optical synthesizing lens, after processing through the device's graphics system. The video see-through system is a method of acquiring real-world information by using one or more cameras as shown in Figure 3, synthesizing the information with the virtual information using the graphic system, and transmitting the information through the display. Most video see-through systems have the advantage of high-quality resolution and wide viewing angle, because they depend on the performance of the connected computer, not the stand-alone system. However, because of the way the camera displays information, there is a high probability of causing 3D nausea. On the other hand, most optical see-through systems are stand-alone devices with their built-in computer, which is specialized in portability and activity.  
The computing power of the optical see-through system is somewhat lower than that of the video see-through system based on the conventional desktop connection. However, it can be used while looking at the real world without the video camera. Therefore, when choosing the augmented reality implementation method, it is appropriate to select the video see-through method if the performance effectiveness is more important, or optical see-through method if the realistic visualization of the real world is more important.
## 3 Data and Methods
### Ancillary Data
Figure 3: The Landsat 7 ETM+ false-colour (R = Band 7, Shortwave Infrared, 2.09–2.35 \(\upmu\)m; G = Band 5, Shortwave Infrared, 1.55–1.75 \(\upmu\)m; B = Band 1, Blue, 0.45–0.52 \(\upmu\)m) composite image of (**a**) 05/12/2013 at 9.31 and (**b**) 24/12/2014 at 9.33 GMT; (**c**) change detection map showing the water affected areas, depicted in magenta, over the region of interest (ROI). The VNG is a satellite-based flood extent product developed using SNPP/VIIRS imagery to derive near real-time flood maps for the National Weather Service (NWS) River Forecast Centres (RFC) in the USA [11]. It exploits a multi-step (decision trees) approach based on SNPP/VIIRS imagery to automatically generate near real-time flood maps in any land region between 80 \({}^{\circ}\)S and 80 \({}^{\circ}\)N [11]. Geolocation (GITCO) and VIIRS data, such as the SNPP/VIIRS 750-m resolution cloud mask intermediate product (IICMO) and M-band terrain-corrected geolocation data (GMTCO) are also used in combination with several static ancillary datasets (e.g., global land cover, global land and water masks, digital elevation models, etc.). These data are integrated into the VNG software, using fixed threshold, contextual and change detection approaches to determine the presence of flooded areas, with a big effort toward the cloud and terrain shadow removal [11]. Since March 2018, the software has been delivered by CSPP in a version adapted for direct broadcast satellite receiving systems [44]. The software can process either full pass length SDRs by default or user defined latitude/longitude ROIs. The flood maps generated by VNG for the ROI shown in Figure 1 have been produced (Figure 5), considering the cloud-free VIIRS daytime imagery of the event. The output is a colour enhanced 8 bit GeoTIFF image with the main identified features differently depicted on the basis of the provided legend [44]. Figure 4: The flood hazard map (PAI) for the ROI. Areas are depicted according to the palette, ranging from black to yellow when the return period increases. Figure 5: _Cont._
## 4 Simulation Research Results
### Simulation of the Model  
In all simulation tests, it was assumed that the object and the measurements were distorted by Gaussian disturbances with known parameters.  
Figure 3 presents cases in which changes in the value of matrix \(A\) elements caused the most varying distributions. Change in \(k_{1}\) and \(k_{3}\) from the smallest to the largest possible (see value of parameter 4) caused the largest differences in the waveform of BOD and DO.  
Assuming different parameters of the model, differences in the waveforms of the generated signals were examined; the results of these experiments are shown in Figure 4.  
Figure 4: Distributions of generated signals for different parameters of the model: (**a**) \(k_{1}\), \(k_{2}\), \(k_{3}\) identical for algorithms and model, (**b**) \(k_{2}\) different for algorithms and model, (**c**) \(k_{1}\), \(k_{3}\) different for algorithms and model, (**d**) comparison of all cases.  
Figure 3: Distributions of waveform BOD and dissolved oxygen (DO) for different values of model parameters.  
Selected changes in the parameter values of the model causing the most different distributions were used to generate monitoring signals.  
Figure 4 shows courses of prototype signals and those generated using the Kalman filter and the adaptive algorithm. Figure 4a demonstrates courses for which parameters \(k_{1}\), \(k_{2}\), and \(k_{3}\) were identical for algorithms and prototype courses. Figure 4b presents a situation in which the algorithms included changes of only parameter \(k_{2}\) whereas Figure 4c shows changes in parameters \(k_{1}\) and \(k_{3}\). The summary of all cases is shown in Figure 4d.  
Research was also carried out in which the values of the object model parameters were changed over time. The experiments were carried out with the adaptive algorithm and the Kalman filter. The biggest differences in distributions were obtained for the BOD signal. The algorithm for DO exhibited slight discrepancies, which resulted from the signal's online measurements.
## 3 Performance Analysis
### Channel Fading
#### 3.4.1 Log-Normal Fading  
We take into account a system that operates in a quasistatic flat fading channel where the route gains are independent and constant during a frame period. The corresponding PDF is provided by [41]  
\[f_{\gamma}(\gamma)=\frac{1}{\sigma\gamma\sqrt{2\pi}}e^{(-\frac{(\ln\gamma-y)^{ 2}}{2e^{2}})}. \tag{14}\]The outage probability, \(P_{out}\), is the likelihood that the received signal's SNR \(\gamma\) will go below the SNR threshold, \(\gamma_{th}\), which may be expressed as  
\[\begin{array}{ll}P_{out}&=P(\gamma<\gamma_{th})=\int_{0}^{\gamma_{th}}f_{ \gamma}(\gamma)d\gamma\\ &=\int_{0}^{\gamma_{th}}\frac{1}{\sigma\gamma\sqrt{2\pi}}e^{(-\frac{(\gamma_{ th}\gamma-p)^{2}}{2\sigma^{2}})}d\gamma=-\frac{1}{2}erf\Big{(}\frac{\log\gamma-p}{ \sigma\sqrt{2}}\Big{)}.\end{array} \tag{15}\]
# Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
## 3 Results
### Case Study for TOA  
The analysis focuses on four alternative routes generated using the TomTom service, based on the specified coordinates: (44.92599019228926 North Latitude and 26.02940758681691 East Longitude), (44.92498748173771 North Latitude and 25.994731989529296 East Longitude), (44.94394486315496 North Latitude and 26.019451227199674 East Longitude), and (44.92599019228926 North Latitude and 26.02940758681691 East Longitude).  
Figure 7 presents the alternative route 1. This route is relatively short (11.129 km) with no traffic delays. Moderate road safety and road sensitivity contribute to the 31.46%. The black squares in Figures 7-10 indicate events such as potholes or unpleasant incidents.  
Figure 6: Gyroscope Z-axis behavior during pothole detection.  
Route 2 (Figure 8) achieves the highest _GRQlk_ as the worst-ranked route due to its poor road safety conditions and moderate travel time (34.33 min). Despite slightly poorer road sensitivity, its minimal traffic delay and efficient travel distance (11.324 km) conduct 34.04% for the _GRQlk_.  
Route 3 (Figure 9) features a longer distance (13.083 km) and higher traffic delay, as depicted in Figure 10. The combination of moderate safety and road conditions lowers its overall ranking. This route is acceptable but less optimal compared to Route 4, having a _GRQlk_ of 29.44%.  
Figure 10 presents the best result, with 26.40% for the _GRQlk_. Route 4 has the longest distance (16.473 km) and the highest traffic delay. Its high safety and average road conditions make it the best alternative.  
Figure 8: TOA for route 2.  
Figure 7: TOA for route 1.  
The TOA algorithm effectively provides the optimal route by leveraging advanced technologies and unique device capabilities. Its precision and ability to balance travel efficiency, safety, and road conditions underscore its uniqueness, setting a benchmark for intelligent routing solutions in modern navigation systems.
###### Abstract  
Manganese (II) chloride tetrahydrate, classified as an inorganic phase-change material (PCM), can be used as a thermal energy storage material, saving and releasing thermal energy during its phase transitions. In this study, thermophysical properties, such as phase change temperatures, latent heat, and thermal conductivities, of four types of MnCl\({}_{2}\)\(\cdot\)4H\({}_{2}\)O PCMs were investigated under single and dual phases (liquid-, solid-, and dual-phase PCMs) using differential scanning calorimetry (DSC) and a heat flow meter. PCMs with a liquid or dual phases exhibited superheating issues, and their melting temperatures were 7 to 10 \({}^{\circ}\)C higher than the reference melting temperatures. The PCMs had latent heats between 146 and 176 J/g in the temperature range of 23 to 45 \({}^{\circ}\)C under the endothermic process. Severe supercooling during the exothermic process was observed in all as-received specimens, but was mitigated in the homogenization-treated specimen, which sustained an increase in solidification temperature of about 15 \({}^{\circ}\)C compared with the as-received and treated PCMs. The diffusivities of PCMs were between 9.76 \(\times\) 10\({}^{-6}\) and 2.35 \(\times\) 10\({}^{-5}\) mm\({}^{2}\)/s. The diffusivities of the PCMs in the solid phase were higher than those in the liquid phase. During the initial holding time of the endothermic process, the PCM in the liquid phase could not be fully solidified due to an insufficient initial holding time and very low diffusivity, which caused superheating during the DSC measurement. Moreover, in the exothermic process, a fast cooling rate of 5 \({}^{\circ}\)C/min and low thermal diffusivity caused supercooling. In particular, the diffusivity of the liquid PCM was lower than those of the solid PCM and other PCMs, which caused extremely high supercooling during the DSC measurement. This paper provides the thermophysical properties of MnCl\({}_{2}\)\(\cdot\)4H\({}_{2}\)O PCMs, which are not available in the literature. The homogeneity of PCMs in their initial states and their heating/cooling rates were identified, and constitute important factors for accurately measuring the thermophysical properties of PCMs.  
Keywords:phase-change material; MnCl\({}_{2}\)\(\cdot\)4H\({}_{2}\)O; phase-change temperature; thermal diffusivity; supercooling +
Footnote †: journal: Applied Physics Letters
## 4 Experiments and Results
### Evaluation  
This section evaluates the proposed methods using the video recorded by the webcam mounted on the robot manipulator in the lab environment. Figure 6 shows the case study for detecting the target CNC machine and HMI display. The robot system moves from the lab door toward the target CNC machine and records the real-time video through the mounted camera. Based on the proposed SiameseRPN method, the target CNC machine and HMI display are recognized and located using green and red bounding boxes, respectively. Figure 7 presents particular examples of recognizing the text information on the HMI display using the proposed working status recognition method, such as the basic instruction information and G-codes. Once identifying the text, it is converted into machine-readable text that assists the autonomous robot system in recognizing the real-time working status of the machine, which is significant for the robot system to achieve further decision-making and execution actions to tackle the emergency and abnormal conditions in a completely autonomous environment.  
Figure 6: Validation results for the target CNC machine and HMI detection. (**a–d**) Detect the target CNC machine in the green bounding box, and obstacles such as the table in the yellow bounding box; (**e–g**) detect the HMI in the red bounding box.
# Fused Gromov-Wasserstein Distance for Structured Objects
## 3 Mathematical Properties of \(Fgw\)
### Interpolation Properties between Wasserstein and Gromov-Wasserstein Distances  
\(FGW\) distance is a generalization of both Wasserstein and Gromov-Wasserstein distances in the sense that it achieves an interpolation between them. More precisely, we have the following theorem:  
**Theorem 3**: _Interpolation properties._  
_As a tends to zero, one recovers the Wasserstein distance between the features information and as a goes to one, one recovers the Gromov-Wasserstein distance between the structure information:_  
\[\lim_{\alpha\to 0}d_{FGW,\alpha,p,q}\big{(}\mu,\
u\big{)}=\big{(}d_{W,qp} \big{(}\mu_{A},\
u_{B}\big{)}\big{)}^{q} \tag{20}\]\[\lim_{\alpha\to 1}d_{FGW,\alpha,p,q}(\mu,\
u)=(d_{GW,qp}(\mu_{X},\
u_{Y}))^{q} \tag{21}\]  
Proof of this theorem can be found in Section 7.4.  
This result shows that \(FGW\) can revert to one of the other distances. In machine learning, this allows for a validation of the \(\alpha\) parameter to better fit the data properties (i.e., by tuning the relative importance of the feature _vs_ structure information). One can also see the choice of \(\alpha\) as a representation learning problem and its value can be found by optimizing a given criterion.
## 2 Grape Composition
### Grape-Derived Aroma Compounds
#### 2.1.3 Methoxypyrazines (MPs)  
Nitrogen-containing grape-derived volatiles, 3-Alkyl-2-methoxyyrazines (MPs), are found abundantly in the stems (79.2%) rather than in the berries (20.8%) [45]. The precise biosynthesis pathway of MPs is still unclear, although they are suggested to be derived from the metabolism of amino acids [4; 30]. However, the last step in the synthesis of MPs (O-methyltransferaseases (OMT) methylation of hydroxyyprazine precursors to methoxypyrazines) is explicit, as several identified genes correlated positively with the precursors [46; 47; 48; 49].  
The most important MPs, 2-methoxy-3-isobutylpyrazine (IBMP), 2-methoxy-3-secretbutylpyrazine (SBMP), and 2- methoxy-3-isopropylpyrazine (IPMP), out of the seven detected in grapes, impact grassy, herbal, bell pepper, leafy, and asparagus-like odorants in several wines such as Cabernet Sauvignon, Sauvignon Blanc, Chardonnay, Cabernet franc, Carmenere, and Merlot [33; 49; 50; 51; 52]. The most abundant among the three important MPs is IBMP, mostly found in the grape skin [4; 45]. [PERSON] et al. [53] studied the accumulation of IBMP in 29 different grapes and reported high levels of IBMP in some cultivars compared to trace levels or undetected IBMP in other cultivars. Several studies have shown that grape variety and other factors such as maturity, climate, leaf removal, and light exposure [39; 50; 54; 55; 56] influence the accumulation and concentrations of MPs.