esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results and Discussion
### Efficiency Experiment  
#### 6.2.1 Feature Detection Performance  
First, our two step dynamic threshold feature extraction was compared with the feature extraction carried out by ORB-SLAM on the warehouse dataset. The parameter settings of the two feature extraction algorithms were the same. The number of expected features was 2000; the image was divided into 10 \(\times\) 10 cells; the initial threshold was set to 20; and the minimum threshold was set to 7.  
Figure 6 shows the feature extraction execution time of every frame using our algorithm and ORB-SLAM on the warehouse dataset. It can be seen from the figure that our algorithm execution time was significantly less than the feature extraction time of ORB-SLAM, and the operation was very stable. Furthermore, changing the scene had little effect on the running time of our algorithm, whereas it greatly affected the running time of the feature extraction algorithm in ORB-SLAM. A constant feature extraction time was necessary for constant tracking speed. Therefore, our algorithm was superior to the feature extraction algorithm in ORB-SLAM in terms of extraction speed and running stability.  
Figure 7 and Table 1 show the performance of the two feature extraction algorithms after reducing the number of desired features. When the number of expected features was reduced from 2000 to 500 in decrements of 500, our algorithm execution time was reduced by 9.83%, 20.43%, and 25.00%, respectively. However, the execution time of the feature extraction algorithm in ORB-SLAM was only reduced by 2.27%, 1.36%, and 2.86%, respectively. The reason for the relatively low effect on ORB-SLAM was that it must extract features from each cell regardless of whether the texture in the current frame was rich; thus, the overhead of the algorithm was constant irrespective of the number of desired features. However, our algorithm extracted enough features in the first overall extraction step, and it only re-extracted from cells with less texture. Therefore, when the number of desired feature points was reduced, our algorithm runtime significantly decreased.  
Figure 6: Time analysis of two different feature extraction algorithms on each image in the warehouse dataset.  
Figure 8 shows that the two different feature extraction algorithms extracted fast feature points from the same frame and obtained almost the same results. This was because, although the logic of the algorithm was different, the fast feature point extraction function of OpenCV was called at the bottom.
## 3 Results
### The Impact of the Marine Environmental Parameters on \(k_{sstda}\)  
As discussed in the previous section, the SCA is more sensitive to the SATDA than the SSTA, and the coupling coefficient \(k_{sstda}\) is greater than \(k_{ssta}\). Therefore, the following discussion focuses solely on the impacts of environmental parameters on the coupling coefficient \(k_{sstda}\). In order to reveal the reason why the coupling coefficient \(k_{sstda}\) varies with the seasons, the influence of the background marine environment on \(k_{sstda}\) is analyzed using the data from January 2002 to June 2009.  
As an example, the curves of the _RMMSATD_, _RMMAT_, _RMMSST_, and _RMMWS_ in the ARC region varying with seasons are shown in Figure 8. Here, _RMMSATD_, _RMMAT_, _RMMSST_, and _RMMWS_ denote the values of the regional monthly mean \(SATD\), regional monthly mean air temperature, regional monthly mean \(SST\), and regional monthly mean wind speed over the ARC region and can be considered the background environmental parameters. The corresponding scatter plots of the coupling coefficient and the environmental parameters are shown in Figure 8. From Figure 8, it is found that there are correlations between \(k_{sstda}\) and the background environmental parameters. Figure 8 illustrates that \(k_{sstda}\) exhibits a negative correlation with _RMMSATD_ and _RMMWS_, while showing a positive correlation  
Figure 7: Comparisons of the interannual variations in the coupling coefficients \(k_{sstda}\) and \(k_{ssta}\) in the four sea areas, namely the (**a**) ARC, (**b**) BMC, (**c**) KE, and (**d**) GS sea areas, from January 2002 to June 2009.  
To further analyze which background environmental parameter primarily affects the coupling coefficient \(k_{satda}\), the scatter plots of \(k_{satda}\) varying with these parameters over the four regions are plotted together in Figure 9. The solid lines in Figure 9 denote the fitting lines based on the overall data of the four regions. When the data over the four typical sea areas are all considered, the results in Figure 9 show that the coupling coefficient \(k_{satda}\) has the strongest correlation with _RMMSATD_, with a correlation coefficient of \(-0.81\). Comparing the correlation coefficient with that in Figure 8, the value of the correlation coefficient between \(k_{satda}\) and _RMMSATD_ remains almost unchanged. However, the absolute values of the correlation coefficients between \(k_{satda}\) and the other three oceanic environmental parameters (i.e., _RMMAT_, _RMMSST_, and _RMMWS_) all decrease significantly. These results show once again that _RMMSATD_ is the key parameter affecting the coupling coefficient \(k_{satda}\).
# Quantum Eigenstates of Curved and Varying Cross-Sectional Waveguides
## Appendix A Validation  
In this section, we will validate the 1D equation by comparing it to accurate full 2D calculations. The reason we consider 2D examples is that it would be very expensive (in terms of computer power and time) to obtain the required accuracy for full 3D calculations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\beta L\) & \(E_{1}\) **[eV]** & \(E_{2}\) **[eV]** & \(E_{3}\) **[eV]** & \(E_{4}\) **[eV]** & \(E_{5}\) **[eV]** \\ \hline
0 & 2.10 & 2.21 & 2.21 & 2.52 & 2.53 \\ \(\pi\) & 1.54 & 1.90 & 2.27 & 2.64 & 3.00 \\ \(2\pi\) & 1.80 & 1.80 & 2.54 & 2.56 & 3.17 \\ \(3\pi\) & 2.10 & 2.11 & 2.12 & 3.13 & 3.13 \\ \(4\pi\) & 2.41 & 2.46 & 2.46 & 2.53 & 3.48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The first five eigenvalues for the tennis ball curve with \(b=a/20\), length \(25\,\mathrm{nm}\), average width \(1\,\mathrm{nm}\) (\(\epsilon=0.5\,\mathrm{nm}\)), no turn, and trigonometric variation of width \(h=1+\frac{\pi}{2}\sin(\beta s-\psi_{0})\), \(\alpha=0.5\), \(\psi_{0}=0\).  
Figure 8: Three square tubes with length \(25\,\mathrm{nm}\) and constant thickness \(1\,\mathrm{nm}\) around a tennis ball curve with \(b=a/20\). In the middle plot, the case with one quarter turn is shown. To the right with one half turn.  
In 2D, we have \(\kappa_{1}=\kappa\) and \(\kappa_{2}=\omega=0\). We let \(v\in[-1,1]\). Then, the transversal ground state is \(\cos\left(\frac{\pi}{2}v\right)\), \(\lambda_{0}=\frac{\pi^{2}}{4}\), and if we let \(\mathcal{R}=v\frac{\partial}{\partial v}\) act on it, we obtain  
\[v\frac{\partial}{\partial v}\cos\left(\frac{\pi}{2}v\right) =-\frac{\pi}{2}v\sin\left(\frac{\pi}{2}v\right)\,,\] \[\left(v\frac{\partial}{\partial v}\right)^{2}\cos\left(\frac{\pi} {2}v\right) =-\frac{\pi}{2}v\sin\left(\frac{\pi}{2}v\right)-\frac{\pi^{2}}{4}v ^{2}\cos\left(\frac{\pi}{2}v\right)\,,\]  
and hence  
\[\langle\mathcal{R}\rangle =-\frac{\pi}{2}\int_{-1}^{1}v\sin\left(\frac{\pi}{2}v\right)\cos \left(\frac{\pi}{2}v\right)\,\mathrm{d}v=-\frac{1}{2}\,,\] \[\langle\mathcal{R}^{2}\rangle =\langle\mathcal{R}\rangle-\frac{\pi^{2}}{4}\int_{-1}^{1}v^{2} \cos^{2}\left(\frac{\pi}{2}v\right)\,\mathrm{d}v=-\frac{\pi^{2}}{12}\,.\]  
The approximative 1D equation Equation (15) then reads  
\[-\left(\frac{\mathrm{d}}{\mathrm{d}u}+\frac{1}{2}\frac{h^{\prime}}{h}\right) ^{2}\chi+\frac{1}{4}\left(\left(\frac{h^{\prime}}{h}\right)^{2}\left(1+\frac{ \pi^{2}}{3}\right)-\kappa^{2}+\frac{\pi^{2}}{\epsilon^{2}h^{2}}\right)\chi= \lambda\chi\,.\]
## 3 Materials and Methods
### Ablation Study of the Proposed Model (Optimized ANN)
#### 3.7.3 Ablation Study 3: Changing Kernel Initializer  
A number of kernel initializers were investigated to obtain the best train and validation accuracies for the new proposal. The numerous types of kernel initializers are shown in Table S6. Applying the standard as the kernel initializer gave us the best accuracy, with a train accuracy of 98.12% and a validation accuracy of 96.25%. When we utilized He normal and normal as kernel initializers, the accuracy decreases by 90.62% and 93.44% for train accuracy, respectively, and 92.50% and 95% for validation accuracy, respectively.
## 3 Results and Discussion
### Biomarker Response Index (BRI)  
Biomarker weights and scores were calculated for the studied parameters of the exposed fish and integrated to calculate and estimate the BRI. BRI is indicative of the general health status of the fish [54]. In the present study, the BRI value of diazinon-exposed fish for 15 days was 3.42 for T\({}_{1}\) and 3.25 for T\({}_{2}\), which indicates slight alterations in the treated fishes' health status compared to the control [54]. The BRI value for 45 days was three, which reflects moderate alteration compared to the control group. The BRI value of diazinon was 2.75, which specifies significant modifications of the health status compared to the control [54]. Overall, BRIs exhibited significantly concentration-effect responses, showing an apparent decrease with the increasing diazinon exposure levels. In addition, the BRI appeared to show marked differences compared to the IBR. The advantage of this integrated index obtaining an accurate toxicity scale has been reported [111].  
Figure 6: _Cont._  
Figure 6: Star plot of the studied biomarkers in _Clarias batractus_ after (**a**) 15 days, (**b**) 30 days, and (**c**) 45 days diazinon exposure.  
Figure 7: _Cont_.
## 3 Results and Discussion
### Dross Formation Process  
As shown in Figure 8, the temperature distribution in different areas was very large: when the laser scans the solid support zone, heat can be conducted at the bottom because of the higher thermal conductivity of the currently established area. However, when the laser scans the powder support zone, the thermal conductivity of the powder is only 1% of the entity; therefore, it is difficult for heat to be conducted at the powder layer. The heat can only be conducted in the formed area in the current layer. Due to the accumulation of the heat in the powder support zone and a dramatic increase in temperature, the size of the molten pool increased. The overhanging bottom was not connected to the substrate, so the excessive molten pool melted the powder at the bottom, thus exceeding the original contour. Figure 4 shows the part of the experimental result in which there are a large number of spherical formations at the bottom of overhang structure. Figure 9 shows the schematic diagram of the process of dross formation. The molten pool in the powder support area sank to the bottom powder area due to gravity and the capillary force [10], resulting in many powder particles attaching to the bottom. The thermal cycle in the LPBF process makes the situation more complex, and the previously formed area also affects subsequent processing. These factors made the parts under the overhang area of the surface form a large number of globular cross formations, leading to the actual forming thickness in the process being greater than the design requirements. Therefore, the poor surface quality and dimensional accuracy do not meet the actual requirements.
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems
### Case Study 1: Coupling of Continuous Substructures
#### 3.1.4 Results: Comparison Between Acceptance Coupling from Different Methods  
Figures 4 and 5 illustrate the comparison of receptance coupling using frequency-based substructuring (RCFBS), the modal method, and numerical finite element analysis (FEA). In the frequency range of up to 100 Hz, which encompasses the first two dominant modes, all methods show a strong agreement in the resonance and anti-resonance peaks. However, at higher frequencies, particularly around 400 Hz, discrepancies arise. While FEA and RCFBS maintain consistency, the modal method deviates, with noticeable differences in the fourth mode compared to the third.  
Figure 4: Comparison of receptance components H\({}_{11}\), H\({}_{14}\), H\({}_{22}\), and H\({}_{33}\) for frequencies of up to 100 Hz.  
The material properties and structural parameters used for modeling are presented in Table 1.
# Ka-Band Doppler Scatterometry: A Strong Wind Case Study
## 2 Data and Methods
### General Definitions  
Doppler radar data are computed from in-phase, \(I\), and quadrature, \(Q\), signal components sampled at 40 kHz for each (VV and HH) polarization. These raw signals yield \"instantaneous\" radar characteristics which are estimated from Doppler frequency spectra computed over consecutive 0.2-s intervals, \(S(f_{\mathrm{d}},t)=|\mathrm{FFT}(I+iQ)|^{2}\), where FFT is  
Figure 2: Environmental conditions during 24-25 October 2018. (**a**) Wind speed and wind direction with shaded rectangles indicating record spans. Radar azimuth for each record is shown by horizontal dashed lines. Vertical solid line indicates the acquisition time of the SAR image shown in Figure 1a. (**b**) Temporal evolution of wave elevation frequency spectrum. Significant wave height (SWH) is shown by red bold line against the right y-axis. (**c**) Current speed and direction at 10 m depth.  
the Fourier transform. Integral characteristics of \(S(f_{\rm d},t)\) are defined via its zeroth and first moments,  
\[\begin{split} m_{0}&=\int S(f_{\rm d},t)\,df_{\rm d},\\ m_{1}&=\int f_{\rm d}S(f_{\rm d},t)\,df_{\rm d}\end{split} \tag{1}\]  
Doppler spectrum variance, \(m_{0}\), is a measure of received signal power. The Normalized Radar Cross-Section (NRCS), \(\sigma_{0}\), of the sea surface is estimated from \(m_{0}\) multiplied by the known calibration coefficient and divided by the effective footprint area. The calibration coefficient is obtained from calibration measurements conducted with known backscattering targets [23].  
The Doppler centroid frequency (also referred to as DC), \(f_{\rm D}=m_{1}/m_{0}\), is related to the line-of-sight Doppler centroid velocity, \(V_{\rm D}=\lambda_{\rm r}/2\cdot f_{\rm D}\), where \(\lambda_{\rm r}=0.8\) cm is the radar wavelength. For our notations, positive/negative Doppler centroid frequency shifts, \(f_{\rm D}\), correspond to approaching/moving away targets, respectively.
## 5 Experimental Results
### Experimental Verification of Stable V/f Control with CPF  
In this section, a series of experiments for the \(V/f\) control with stabilization and CPF that were performed are described. The input parameters for this control strategy are presented in Table 13 of Appendix A. In Figure 21, the ramp-up to the nominal reference speed, change in speed direction, and 50% load applied at steady-state, with a required power factor of 1, is presented. The ramp was set up with a slope of 1000 rpm/s. The 50% load was applied from 8 s to 19 s in the forward direction and from 29 s to 40 s in the backward direction. The command to change the speed direction was set up at 21 s. The boost voltage of 3 V was applied to \(d\)-axis during motor start-up until 1000 rpm to overcome the starting issues. These issues are associated with a resistive voltage drop, which cannot be neglected at low speeds. Above the rotational speed \(n_{r}\) of 1000 rpm, the boost voltage is set back at 0 V, as is evident from the required \(d\)-axis voltage \(v_{d}^{*}\) in Figure 21.  
In Figure 22, the same conditions were applied except for the applied load, which was increased to 75%.  
In Figure 23, the applied load to the machine was increased to 100%. However, as evident from the figure, the machine cannot hold such a load, and the system becomes unstable. The reason behind the control failure lies in the required power factor. At the power factor is equal to unity, the required \(d\)-axis voltage \(v_{d}^{*}\) decreases with the higher loads, leading the system to an unstable region.  
At the lower power factor, e.g., equal to 0.95, the \(V/f\) control can operate under full-load conditions, avoiding system instabilities. This is demonstrated in Figure 24, where the control performance under 100% load is presented.  
The speed profiles with changes to nominal reference speed by 1000 rpm in both directions with no-load are shown in Figures 25 and 26. The first shows the speed profile for a power factor equal to 1, while the second one shows the profile for the lower power factor of 0.95, which was previously verified under full-load conditions. As can be seen from both figures, the real motor speed \(n_{r}\) follows the reference speed \(n_{r}^{*}\) and the accuracy at steady-state is spectacular. The main difference lies in the start-up current, which  
Figure 21: Ramp-up and ramp-down with the nominal reference speed and 50% load applied at steady-state, with a required power factor of 1: speed, \(dq\) voltages, \(dq\) currents.  
Figure 24: Ramp-up and ramp-down with the nominal reference speed and 100% load applied at steady-state, with a required power factor of 0.95: speed, _dq_ voltages, _dq_ currents.  
Figure 25: Speed profile up to nominal reference speed with no-load and required power factor of 1: speed, _dq_ voltages, _dq_ currents.
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
## 7 Conclusions and Future Work  
In this paper, we analyzed and compared the response times of three database technologies with respect to handling interconnected spatio-temporal data. The technologies compared were two open source database technologies, PostgreSQL and JanusGraph, and one enterprise level technology,TigerGraph. The linear growth in the relational model was clearly illustrated in the results whereas the graph database solutions scaled more horizontally. This can be attributed mostly to the improved scalability of NoSQL database over traditional relational models when querying large volumes of data. The three systems were evaluated by employing a set of spatio-temporal queries similar to those that would be found in real world scenarios when analysing data in a dataset such as the Yelp Challenge Dataset and the Ambulance Simulation Dataset.  
In particular, our results prove that for spatio-temporal data, graph database technology outperforms PostgreSQL in all kernels used in our experiments. This result is partially due to the fact that the kernels produce complex queries due to the interconnected nature of the data. This dataset produced a dense graph on which graph databases have the ability to perform effective traversals, compared to multi-join style queries produced by the relational implementation. The spatio-temporal multi-dimensional aspect has shown to be supported well in all of the databases, as evident by the response times of the queries with constraints of this nature.
## 2 Light Propagation in Water
### Absorption and Scattering  
The most important inherent optical properties of water are absorption and scattering, which determine the light attenuation. Due to absorption, the photons lose their energy throughout the propagation path, whereas scattering spreads the light beam from the propagating direction. Hence, it cannot be captured by the receiver [6; 13; 14]. The Beer-Lambert law is the most commonly used model to describe light attenuation in the underwater medium. This model takes absorption and scattering into account. Hence, the received signal intensity under attenuation in the underwater channel, in the absence of turbulence, can be expressed as  
\[I_{t_{0}}=I_{t}\cdot e^{-c(\lambda)\cdot L}, \tag{1}\]  
where \(I_{t}\) is the intensity of the transmitted signal, \(c(\lambda)\) is the underwater optical attenuation coefficient, which is wavelength (\(\lambda\)) dependent, and \(L\) is the propagation distance. The attenuation coefficient is defined as the sum of the absorption coefficient \(a(\lambda)\) and the scattering coefficient \(b(\lambda)\). These coefficients are determined experimentally and expressed as functions of wavelength in [7]. For our numerical simulations, we use the attenuation coefficient at blue to green wavelengths for typical clean ocean water types, as suggested by [7], and as listed in Table 1.
## 2 Materials and Methods
### CBCT Segmentation  
Image segmentation was performed using the open source software ITK-SNAP (version 3.6.0, [[http://www.itkssnap.org/](http://www.itkssnap.org/)]([http://www.itkssnap.org/](http://www.itkssnap.org/)), accessed on 30 December 2021) [18] by a single operator expert in oral and maxillofacial radiology. ITK-SNAP allows to combine manual and semiautomatic tools for extracting structures in 3D image data [18]. Prior to beginning the study, pre-training on a sample of 20 datasets which were not part of the study was performed in order to improve intra-operator repeatability. The inferior alveolar nerve, the mandibular third molar, and the buccal and lingual cortex were identified and delineated through semiautomatic segmentation on sagittal, axial, and coronal slices. Subsequently, manual segmentation was performed to assure correct segmentation. Once automatic and manual segmentation were completed, 3D rendering was obtained using 3D MPR Viewer tool (Figures 1-3).  
Figure 1: Software interface for image segmentation; in yellow impacted third molars can be seen, while inferior alveolar nerve is marked in red. **(A)** Axial view; **(B)** Sagittal view; **(C)** Segmented volume; **(D)** Coronal view.  
Figure 2: Segmented images. The relationship between bilaterally impacted third molars and inferior alveolar nerve can be observed. **(A)** 3D reconstruction of the mandible with segmented inferior alveolar nerve (red) and third molars (yellow); **(B)** Axial view.
# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 2 User-Perceived Capacity  
We define \(C\) and \(R(t)\) as the user-perceived capacity and transmission rate, respectively. For the time duration \([0,T]\), the user-perceived capacity \(C\) satisfies  
\[Ct\leq\int_{0}^{t}R(\tau)d\tau,\ \ w.p.\ 1,\ \forall t\in[0,T]. \tag{1}\]  
In discrete-time systems, the integral transforms into the form of a summation \(\sum\limits_{t=0}^{T}R(t)\). Specifically, the user-perceived capacity refers to the capacity that enables users to avoid any interruptions during reading. Then, we obtain the following three propositions.  
**Proposition 1**.: _In the AWGN channel, the user-perceived capacity \(C\) is equal to_  
\[C=W\log_{2}(1+\gamma)\text{ bit/s}, \tag{2}\]  
_where \(W\) and \(\gamma\) represent the bandwidth and the signal-to-noise ratio at the receiver, respectively._  
**Proposition 2**.: _In the fading channel with the channel state information at the receiver, each positive constant \(c>0\) satisfies_  
\[\Pr\left\{ct>\int_{0}^{t}R(t)dt\right\}>0,\quad\forall t\in[0,T], \tag{3}\]due to the unbounded power under deep fading. As such, the user-perceived capacity \(C\) is equal to zero._  
**Proposition 3** (Our previous work [9]).: _In the fading channel with the channel state information at the transmitter and the receiver, the user-perceived capacity is equal to the hard-delay-constrained capacity with power adaptation only, i.e.,_  
\[C =W\log_{2}(1+\gamma)\text{ bit/s}, \tag{4a}\] \[P =\mathbb{E}_{h}\bigg{\{}\frac{\gamma\sigma^{2}}{h^{2}}\bigg{\}}, \tag{4b}\]  
_where \(P\) denotes the expectation of transmission power over the random channel gain \(h\). Meanwhile, \(\sigma^{2}\) denotes the noise variance. (The condition for Proposition 3 to hold is that the power \(P\) is bounded as stated in Equation (4b). The reader may have observed that Equation (4b) does not hold under Rayleigh fading channels. In response to this case, we have demonstrated in our paper [9] that when the frequency or spatial diversity order exceeds two, Equation (4b) can be satisfied.)._  
In this paper, leveraging a cross-layer approach, we achieve the user-perceived capacity with the rate adaptation. Our proposed policy yields a user-perceived capacity that is inevitably superior to that presented in Proposition 3.