esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation of the Pockels Coefficient of the Interfacial Water  
The Pockels coefficient \(r_{ij}\) defined by [25; 26]  
\[\Delta n_{i}=n_{i}-n_{w}=-n_{w}^{3}r_{ij}E_{j}/2 \tag{7}\]  
is related with the first order nonlinear refractive index \(n_{ij}\) (defined by \(\Delta n_{i}=n_{ij}E_{j}\)) as \(r_{ij}=-\frac{2\Delta n_{i}}{n_{w}^{2}E_{j}}=-\frac{2n_{ij}}{n_{w}^{2}}\). Since mutual conversion is trivial, we use the nonlinear refractive index as the Pockels coefficient \(r\) for the time being.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Incidence Angle \(\theta\)** & **Experiment Figure 2b** & **Calculation Equation (5) with \(\varepsilon_{b}\)=5.03** & **Calculation Equation (5) with \(\varepsilon_{b}\)=5.94** \\ \hline
67.4\({}^{\circ}\) & 1.58 eV & 1.72 eV & 1.84 eV \\ \hline
69.2\({}^{\circ}\) & 1.88 eV & 1.88 eV & 2.00 eV \\ \hline
70.3\({}^{\circ}\) & 2.08 eV & 1.95 eV & 2.08 eV \\ \hline \end{tabular}
\end{table}
Table 1: The background dielectric constant \(\varepsilon_{b}\) of Ag, experimentally determined from the fit of the observed surface plasmon polariton (SPP) resonance wavelength to Equation (5) with \(\hbar\omega_{p}=3.82\) eV.  
Even if the \(\Delta n\) is known, there are three additional factors that need to be considered in order to estimate the Pockels coefficient: (i) The fraction of the applied voltage that is distributed to the layer where the \(\Delta n\) occurs, (ii) the \(E\) field in the layer that is determined by the thickness of the layer and the voltage distributed, and (iii) the penetration depth of SPP waves into the layer. The layer referred to here is the EDL of water, which is a so-called diffuse layer and therefore has \(E\) field and refractive index gradients, but for simplicity, we assume that the \(E\) field is uniformly applied within the layer over its effective thickness and a uniform \(\Delta n\) occurs, following the assumption in the previous literature on the evaluation of the Pockels effect in water [3].
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 5 Results Analysis
### Two-Stage and Traditional Prediction Comparison  
To quantitatively evaluate the practical effectiveness of the two-stage track prediction method proposed in this article, a comparison with the prediction results of the traditional LSTM is made based on three effectiveness indicators, respectively. The computation of the Root Mean Square Error (RMSE) can be executed by squaring the difference between each predicted value and its corresponding true value, summing them, calculating the average, and, finally, taking the square root to derive the RMSE value. The computation of the Mean Absolute Error (MAE) can be executed by computing the average absolute variance between the predicted value and the actual value. The computation of the Mean Absolute Percentage Error (MAPE) can be executed by measuring the percentage of variance between the predicted value and the actual value. The smaller the value of the RMSE, MAE, and MAPE, the higher the accuracy of the model, as shown in Table 2.  
From the perspective of quantitative evaluation indicators, the accuracy of not only individual elements, such as track longitude, latitude, and altitude, but also the total prediction accuracy, but also the two-stage prediction outperform the traditional prediction method.
## 4 Discussion
inner dentinal surface of the perforation defect. However, there were more live bacteria than dead bacteria in the dentin inside the dentinal tubules. This can be attributed to the buffering capacity of dentine, which decreases the pH in the dentine tubules as the distance from the perforation defect increases. To our knowledge, this is the first study to compare the colonization depth of _Enterococcus faecalis_ when using MTA-Angelus, Biodentine, and Endocem as perforation repair materials. All the tested materials in the present study were calcium silicate-based. However, unlike MTA-Angelus and Biodentine, Endocem contains fluoride and stannous fluoride, which can inhibit the growth of _Enterococcus faecalis_[50]. This might explain the observation that although no significant differences were found between the tested materials in terms of stained areas, there was a trend to lower values with Endocem repair. A previous study by [PERSON] et al. (2018) that compared the bacterial colonization depth of different materials for retrograde filling accords with our results in that MTA-Angelus and Biodentine displayed a similar performance [18]. However, the results of the present study indicate that the type of repair material did have an effect on the viability of the colonizing bacteria, with significantly more live bacteria detected in the MTA-Angelus group than in the Endocem and Biodentine groups. This is in contrast to a previous study by [PERSON] et al. (2019) who used confocal laser microscopy to evaluate the viability of a multispecies microcosm in the vicinity of biocercamic cements and concluded that MTA-Angelus was as effective as Biodentine in terms of antimicrobial activity [51]. This discrepancy may be explained by the different microorganisms involved. A limitation of the current study is that the use of an in vitro model may not completely recapitulate the exact clinical situation. Several recent studies from 2019-2021 [52; 53; 54] attempted to compare the sealing ability of different biocercamic materials, but these used dye extraction [52; 53] or protein leakage [54] models without any added bacterial contamination. Further in vivo studies will be needed to assess the antibacterial activity of various calcium silicate-based materials in more detail.
## 3 Results and Discussion
### Evaluation of the Mechanical Performance of the Selected Asphalt Mixtures
#### 3.5.1 Particle Size Adjustment of Mixtures with Natural Aggregates  
Initially, the grading curve of the asphalt mixtures with natural aggregates was adjusted (Figure 7) within their standard grading envelope, trying to overlap the equivalent grading curves of the asphalt mixtures with 75% SSA. The final solutions were obtained by combining the amounts of the NA fractions shown in Table 10. The asphalt mixtures with SSA or NA were defined to have the same binder content, using Equation (1) previously presented (5.0% for the conventional and 4.3% when incorporating 75% SSA).  
Figure 7 shows that the conventional mixtures' grading curves could closely overlap the equivalent mixtures' grading curves with 75% SSA. However, the smaller dimensions (0.125 to 2.0 mm) present slight differences, resulting from fewer particles of these dimensions (0/4 fraction) in the SSA than those in the natural aggregates.
## 3 Results and Discussion
### Structural Analysis  
An XRD analysis of samples 1 (SUNSPACE) and 2 (SUNSPACE with BA) is reported in previous work [20]. In Figure 3, the XRD patterns of samples 3, 4, 5 and 6 are shown. Samples 3 and 4 show similar patterns. The identified crystalline phases are calcium carbonate (CaCO\({}_{3}\)) and quartz (SiO\({}_{2}\)), both predominant in bottom ash powder [29], and calcium hydroxide (Ca(OH)\({}_{2}\)) used in the material synthesis. Moreover, calcium carbonate is also probably formed during the carbonation reaction between calcium hydroxide and CO\({}_{2}\), as already extensively discussed in literature [38; 39; 40; 41]. Some peaks can be attributed to aluminum silicate hydroxide chloride. From this analysis, it is possible to conclude that the hydrogen peroxide concentration does not seem to involve structural changes in the samples. Additionally, samples 5 and 6 show similar XRD patterns. They are characterized by the presence of a large halo (between 15\({}^{\circ}\) and 30\({}^{\circ}\) in 2 theta), that can be attributed to an amorphous phase due to the presence of silica fume, which is an amorphous material [17; 24; 42; 43]. Furthermore, cristobalite (SiO\({}_{2}\)) and lautarite (Ca(IO\({}_{3}\))\({}_{2}\)) peaks are identified. Calcium iodate is used in the synthesis of these samples.
# Guidelines for Telemetry Studies on Snow Leopards
## 1 Introduction  
For wildlife management and conservation programs to be impactful and cost-effective, foundational knowledge of the target species' biology is essential. To collect the information needed, it is often necessary to study some number of individual animals using tracking (telemetry) devices, especially for secretive species that are otherwise difficult to observe, such as many fields [1, 2]. Telemetry studies can provide valuable data relevant to species biology, including movement dynamics, resource selection, foraging behavior, and demographic processes like cause-specific mortality, fecundity, and dispersal, e.g., [1, 3, 4, 5, 6, 7]. Fitting tracking devices--typically collars in the case of terrestrial carnivores--requires that animals are captured and immobilized. To safeguard animal welfare, and to ensurethat data are representative of non-sampled individuals, it is vital that the transmitters affect individuals minimally and that capture protocols meet high standards of animal welfare [8; 9]. A telemetry study can fail, or produce biased results, if the transmitters affect the behavior, reproduction, or survival of tagged individuals [9; 10]. Both physical capture and chemical immobilization involve stress and the risk of injuries or mortality for the captured animals [11; 12]. Consequently, telemetry studies should only be considered if there is a realistic probability of achieving the stated research, conservation, or management goals. Poorly planned and executed capture programs can undermine those goals, compromise animal safety and welfare, jeopardize research and conservation outcomes, and damage perceptions of wildlife management and conservation efforts amongst the public [13; 14].  
Here, we provide guidelines intended to help researchers, wildlife agencies, and conservation organizations evaluate, design, and implement plans for collaring studies and to determine whether a proposed study is sufficiently justified, given the risks to the animals involved. The veterinary procedures and considerations involved in such studies, while being critically important, lie outside the purview of our article. We nevertheless underscore that any effort to capture and handle live animals must involve direct collaborations with qualified veterinarians to develop and practice safe and ethical capture and immobilization protocols. While our guidelines are written with an emphasis on snow leopards, our recommendations for designing and evaluating collaring studies have general applicability across species, for a summary see Figure 1.
## 2 Materials and Methods  
### Reagents and Calibrators Preparation  
The analytical standards of six NEOs (\(\geq\)99.5%), namely ACE, CLO, IMI, THI, THIA, and NIT, were purchased from Sigma-Aldrich (St. Louis, MO, USA). DIN was obtained from ChemService (West Chester, PA, USA). Thiamethoxam-d\({}_{3}\), clothiandin-d\({}_{3}\), imidalcoprid-d\({}_{4}\), and acetamiprid-d\({}_{3}\) internal standards (IS) were obtained from Sigma-Aldrich. Analytical-grade acetic acid (99.7%) and HPLC-grade acetonitrile (ACN) and ethyl acetate (EA) were acquired from [PERSON] (Phillipsburg, PA, USA). Ultra-pure water was obtained using the Milli-Q system (Millipore, Burlington, MA, USA). A total of 31 commercial milk samples were randomly purchased from supermarkets in Miaoli (Taiwan), and were stored at 4 \({}^{\circ}\)C. The standard stock solutions of each NEO analyte (100 mg/L) were prepared in ACN and stored at \(-\)20 \({}^{\circ}\)C. The multicomponent standard working solutions (10 mg/L) were prepared by diluting each standard solution with ACN and were used for spiking the samples and preparing calibration curves of solvents and the milk matrix. Solutions of five concentrations (0.5, 1, 10, 25, and 50 \(\upmu\)g/L) were obtained by diluting standard solutions with ACN for preparing calibration curves of the seven NEOs. A blank milk sample was previously analyzed using UHPLC\(-\)MS/MS that the sample did not contain any target analyte. Matrix-matched calibration standards, prepared by spiking a blank sample extract, were in the 0.2-20 \(\upmu\)g/kg range.
###### Abstract  
In radar array signal processing, weak nonstationary polarization signal direction of arrival (DOA) estimation is a challenging and significant issue when both strong and weak nonstationary signals coexist. Time-frequency (T-F) analysis is an effective method to deal with nonstationary signals. In the last decade, spatial time-frequency distributions (STFDs) have been proposed for multiple dual-polarization antenna arrays and efficaciously used for nonstationary signal DOA estimation. In this article, we introduced a novelty means for estimating weak nonstationary polarization signal DOA by utilizing spatial polarimetric time-frequency distributions (SPTFDs) of cross terms when there are strong nonstationary polarization interference signals and additive Gaussian white noise. The cross terms' SPTFDs are considered via a replaceability matrix for data covariance in multiple signal classification (MUSIC) and the estimation of signal parameters using the rotational invariance technique (ESPRIT). Combining the STFDs of cross terms with polarization information about weak nonstationary signals improves signal DOA estimation accuracy. The combined MUSIC and ESPRIT are used in the algorithm to further ensure the success probability and accuracy of DOA estimation. Through simulation analyses, the proposed algorithm is more suitable for the required application scenarios than other algorithms and is superior to other algorithms.  
time-frequency analysis; polarization direction of arrival estimation; cross terms; spatial polarimetric time-frequency distribution Article
## 3 Results and Discussion
### The Development of the Empirical Correlations  
As shown below, three empirical correlations for non-dimensional parameters were developed using the following independent variables: Reynolds number (\(Re\)) ranging from 5500 to 14,500, Prandtl number (\(P_{r}\)) at 0.3 for cooling, and blockage ratio (\(R_{b}\)) at 0.28, 0.35, and 0.42.  
\[Nu=0.037\,{R_{e}}^{0.995}P_{r}^{0.3}{R_{B}}^{0.639}, \tag{17}\]  
\[f=1098.424\,{R_{e}}^{-0.775}{R_{B}}^{0.72},\ \text{and} \tag{18}\]  
\[\eta=23.811\,{R_{e}}^{-0.297}{R_{B}}^{0.307}, \tag{19}\]  
A plot of the predicted data derived from the empirical correlations is compared with the plots of experimental results, as shown in Figures 9-11. For all conditions, the relative deviation between the experimental data and predictive data were \(\pm 4.36\%\) for the Nusselt number (\(Nu\)), \(\pm 7.48\%\) for the friction factor (\(f\)), and \(\pm 3.47\%\) for the thermal performance factor (\(\eta\)). It is evident that there is a close correlation between the predicted and empirical results.  
Figure 8: Effect of blockage ratio (\(R_{b}\)) on the thermal performance factor (\(\eta\)) with Reynolds number (\(Re\)).  
Figure 9: Comparison between the experimental \(Nu\) with predicted \(Nu\).
## 4 Conclusions  
In this work, we studied Ni silicidation of silicon thin films and top-down fabricated SiNWs by using the RTA technique. We paid special attention to the formation of the required silicide phase (NiSi\({}_{2}\)), the quality of the Schottky junctions, and the reproducibility of the silicide length along the nanowires, which are very important for device performance and scalability. To investigate the influence of the NW orientations on the silicidation process, SiNWs with different crystallographic orientations were silicided. Although large scattering in the silicidation lengths of nanowires was observed even for the nanowires of the same orientation, the silicidation in \(<\)100\(>\) nanowires is found to be faster than that in \(<\)110\(>\) nanowires. Furthermore, TEM and FFT analyses revealed the formation of sharp Schottky junctions with [111] interfaces and the NiSi\({}_{2}\) phase of silicide required for the fabrication of RFETs. Density functional theory calculations showed that [111] interfaces are energetically favorable. Control over the diffusion length of silicide into the NW was not achieved. This makes placing of top gates on the Schottky junctions and downscaling of devices a challenge. Alternative annealing techniques with shorter annealing times, like, for example, flash lamp annealing, may be employed to have better control over the silicidation process. Transfer characteristics of the back-gated devices with single unoxidized NWs patterned in \(<\)100\(>\) and \(<\)110\(>\) orientations illustrate  
Figure 10: Electrical characterization with back-gate sweeping of single NWs-based devices with (**a**) \(<\)110\(>\) and (**b**) \(<\)100\(>\) orientations. The devices show ambipolar behavior. To facilitate a \(p\)- and \(n\)-current comparison, curves were shifted left by 17 V to center. Currents in \(<\)110\(>\)-oriented devices are higher than those in \(<\)100\(>\). Channel width and length for both types of nanowires are 20 nm and 3 μm, respectively.  
-ambipolar behavior, with currents in \(<\)110\(>\)-oriented devices being higher compared to the currents in \(<\)100\(>\) devices.  
Conceptualization, D.D. and M.B.K.; methodology, D.D., M.B.K. and J.K. (device fabrication), F.F., S.G. and J.S. (simulations), M.L., S.B. and U.M. (lamella preparation and TEM); writing--original draft preparation, M.B.K., D.D. and F.F.; writing--review and editing, Y.M.G., M.L., F.F., S.G., J.S., A.E. and W.M.W.; supervision, A.E. and Y.M.G. All authors have read and approved this paper for submission.  
We acknowledge funding by the Helmholtz Initiative and Networking Funds for support through the International Helmholtz Research School NanoNet via grant VH-KO-606 (M.B.K, D.D., F.F., S.B., W.M.W, S.G., J.S., A.E.) and the W2/W3 Programme for the first-time appointment of excellent female scientists via grant W2/3-026 (S.G., F.F.).  
Authors thank [PERSON] and [PERSON] for their help in conducting experiments. We are thankful to [PERSON] for his support in administrative affairs. We also thank [PERSON] for his help in data compilation.  
The authors declare no conflicts of interest.
## 4 Performance Evaluation and Discussion
### Simulation Setup and Scenarios  
Consider a C-V2X network consisting of a number of VUE that are spatially distributed using a 1-D Poisson point process with a variable density. The highway length is set to 2 km with three lanes in each direction, and the width of each lane is 4 m [25; 26; 27]. In this scenario, VUE moves with a predefined speed and when they reach the end of the road, they loop around and enter the opposite direction, as shown in Figure 3.  
Figure 3: The HPR-DCC based congestion control in a highway scenario.  
The VUE periodically broadcasts CAMs via V2V sidelink communication. For the C-V2X system, single carrier frequency-division multiple access (SC-FDMA) is used in a 10-MHz-wide channel. Each VUE automatically selects radio resources using the allocation procedure of SB-SPS. The reselection counter is randomly selected to be between 5 and 15 in the SB-SPS [27; 28; 29; 30]. And the self-interference cancellation coefficient is set as \(-\)110 dB to effectively eliminate interference between its own transmission and reception. The average duration of the interval for the CBR calculation is set to 100 ms to ensure the provide more real-time performance metrics of channels. To evaluate the efficacy of HPR-DCC under varying traffic congestion conditions, we manipulate vehicle densities to 40, 80, and 120 vehicles/km while maintaining an average speed of 140 km/h. This manipulation represents low, medium, and high-traffic scenarios, respectively. We further set the standard deviation of vehicle speeds at 3 km/h to approximate real-world traffic conditions. In the initialization of the simulation, under low-stress conditions, we establish the initial transmission power at 10 dBm and the initial transmission rate at 10 Hz [31; 32; 33; 34]. As traffic conditions transition, HPR-DCC facilitates the necessary adjustments. Consequently, the transmission power fluctuates within a 0-23 dBm range, and the packet transmission frequency range varies between 1 and 10 Hz [31]. The main simulation parameters are the same as those listed in Table 3.  
This simulation scenario, by using the enhanced LTEV2 Vsim simulator [35], allows us to evaluate the proposed DCC; thus, we can verify its effectiveness in maximizing packet reception rate and minimizing the CBR while still maintaining a low collision rate.
# Deep-Learning-Based Models for Pain Recognition: A Systematic Review
## 1 Introduction  
Deep learning is an important branch of machine learning used to solve many applications. It is a powerful way of achieving supervised learning. The history of deep learning has undergone different naming conventions and developments [1]. It was first referred to as _cybernetics_ in the 1940s-1960s. Then, in the 1980s-1990s, it became known as _connectionism_. Finally, the phrase _deep learning_ began to be utilized in 2006. There are also some names that are based on human knowledge. For example, an artificial neural network (ANN) refers to the biologic aspect of deep learning. However, some problems cannot be solved by neural inspiration, and many deep-learning perspectives therefore depend on recently developed statistics and applied mathematical principles.  
Automated pain recognition is a complex problem that needs to be solved by a powerful method based on deep learning. Recently, some studies of pain recognition have employed machine learning for this approach. Therefore, this study aims to review deep-learning applications for pain recognition only. It provides a systematic review that incorporates a search strategy and inclusion criteria, and we describe it in the next section. To best of our knowledge, there are only one review paper on pain recognition models in general [2]. This study focuses on the deep models for pain recognition. This review will help the researcher in Artificial intelligence and affective computer community to know the deep ANN algorithms and datasets which have been used for automating the task of pain recognition. Therefore, they can recognize the problems and solve them in a new effective model to outperforms the available algorithms.  
The main contributions of this study are summarized as follows:  
* Review of the pain-recognition studies that are based on deep learning;
* Presentation and discussion of the main deep-learning methods employed in the reviewed papers;
* Review of the available data sets for pain recognition;
* Discussion of some challenges and future works.