esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results  
The ROV's trajectory was simulated in Simulink under the influence of ocean currents and wake flows. We could accurately simulate the ROV's path in various scenarios by analyzing the wake flow from different directions and assessing its impact on the ROV's motion. The simulation explored multiple orientations the ROV may face, allowing us to observe the resulting changes in its trajectory. The results were as follows: we determined that the range of wake flow was \(R=2\) m, the ocean current on the surface was \(v=2\) m/s, and the depth of the ocean was 100 m.
## 4 Discussion
No age groups were considered in the sample, and no sex differentiation was made, which could affect the FD results, so the results do not allow conclusions to be drawn without the risk of bias. In addition, condylar hyperplasia was not classified, and the sagittal or vertical intermaximillary relationship was not considered. Despite the limitations of this study, the results obtained show that FD analysis could be used to differentiate hyperplastic condyles in patients with facial asymmetry. FD could be useful in screening condylar hyperplasia as a quick, simple and inexpensive examination. To extend this line of research and confirm the results of this study, future research on FD in diverse types of facial asymmetry and correlations with the metabolic activity of bone tissue by SPECT are needed. Prospective studies could lead to alternative investigations for analyzing the bone structure histopathology of the hyperplastic condyle after surgery and correlating its figures to the FD values calculated on the pre-surgery CBCT scans, as well as comparing the FD of hyperplastic condyles with the results of a micro-CT scan. Moreover, it would be interesting to extend FD analysis to non-pathologic condyles in patients with different skeletal patterns to study if there are baseline values of FD that could be considered as physiological. Similarly, it would be interesting to establish the FD for patients of different age and ethnic groups. This kind of investigation could provide an easy tool for clinicians to identify bone structure abnormalities of the condyles.
# O-Band Grating Couplers Using Silicon Nitride Structures
## 2 Grating Coupler Design and Theoretical Aspects
This reveals the relationship between the wave-vector \(k_{0}\) of a light beam through the waveguide and propagation constant \(\beta\) of the corresponding coupled light beam out of the waveguide to the SMF, and the Bragg condition is expressed as [1]:  
\[\beta=k_{0}sin(\theta)+m\frac{2\pi}{\Lambda} \tag{2}\]  
where \(\frac{2\pi}{\Lambda}\) denotes the grating vector, while \(m\) corresponds to the order of grating diffraction. To calculate the coupling efficiency between the modes of the waveguide and SMF, we need to calculate the overlap integral between the Gaussian profile of the waveguide and the Gaussian profile of the SMF [21]. \[2\kappa(z)=\frac{G^{2}(z)}{1-\int_{0}^{z}G^{2}(z)\mathrm{d}t} \tag{3}\]  
This equation is for two-dimensional and TE polarization, and \(\alpha(z)\) is the coupling efficiency to the fiber as a function of the propagation distance \(z\). The \(G(z)\) normalized Gaussian profile had a beam diameter of 9.6 \(\upmu\)m for coupling to the fiber.
## 7 Experimental Results
### Realtime Implementation
#### 7.1.2. Evacuation Path
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Hardware Platform** & **Data Collecting Device** & **Network Simulation Analysis** & **Communication Technology** & **Evacuation Path Display** \\ \hline \multirow{4}{*}{[3]} & & RFM23B RFID, LM35 & & & \\  & & Temperature Sensor, & & & \\  & & HC-05 Bluetooth & Not implemented & Bluetooth & Audio Signal, Text Message on user \\  & & Module, PIR Sensor & & & \\ \hline \multirow{4}{*}{[8]} & MT(mobile terminal) & & & & \\  & indoor location & & & & \\  & technology and its & & & & \\  & Bluetooth & & & & \\  & & Temperature & Not implemented & Bluetooth Technology & \begin{tabular}{c} Sanding Alarm message to User \\ Mobile with fire \\ location \\ \end{tabular} \\ \hline \multirow{4}{*}{[9]} & Raspberry Pi & \begin{tabular}{c} Temp. Sensor (LM \\ 35), Smoke Sensor \\ and Gas Sensor \\ (MQ9) \\ \end{tabular} & Not implemented & \begin{tabular}{c} GSM \\ Wireless Sensor \\ Networks \\ \end{tabular} & \begin{tabular}{c} No work on \\ evacuation \\ \end{tabular} \\ \hline \multirow{4}{*}{[29]} & IoT & \begin{tabular}{c} Illuminance sensor, \\ Arduino Uno as a \\ sensor \\ \end{tabular} & Not implemented & Wireless & Application on \\  & & & & & Mobile \\  & & & & \\ \hline \multirow{4}{*}{[48]} & Microcontroller, wireless & & & & \\  & transceiver, and Wi-Fi & Temperature sensor & \
\begin{tabular}{c} Network simulation is \\ not carried out in the \\ study \\ \end{tabular} & Wi-Fi &
\begin{tabular}{c} Web application \\ based evacuation \\ path through Wi-Fi \\ connectivity \\ \end{tabular} \\ \hline \multirow{4}{*}{[52]} & Fire Dynamic & & & & \\  & Simulation (FDS) and & & & & \\  & building information & & & & \\  & modeling (BIM) are & & & & \\  & utilized to implement & & & \\  & fire evacuation & & & \\ \hline \multirow{4}{*}{[53]} & Pathfinder software is & & & & \\  & used to simulate fire & & & & \\  & locations and evacuation & & & & \\  & routes & & & & \\ \hline \multirow{4}{*}{[54]} & The proposed & & & & \\  & communication gateway & & & & \\  & for evacuation system & & & & \\  & & available & & & \\ \hline \multirow{4}{*}{[55]} & A data model and & & & & \\  & reference architecture of & & & & \\  & an intelligent evacuation & & & & \\  & system is proposed for & & & & \\  & large public building & & & & \\ \hline \multirow{4}{*}{[55]} & An intelligent & & & & \\  & automated fire exit & & & & \\  & guidance system using & & & & \\  & A* search algorithm & & & & \\  & has been presented & & & \\ \hline \multirow{4}{*}{[56]} & Caching strategy which & & & & \\  & expedites dynamic route & & & & \\  & generation with the & & & \\  & currently effective & & & & \\  & route & & & \\ \hline \multirow{4}{*}{Proposed} & Zigbee and LoRa based & Sensor and Vision & & & \\  & hardware & Node & & & \\ \hline \hline \end{tabular} A* is the name of algorithm implemented in the study.  
\end{table} 
Table 4: Comparison of Previous Studies with the Proposed Study.
## Materials and Methods
### Prototyping  
Producing the spherical occlusion device requires laser-cutting of the designed pattern onto a nitinol hypotube, followed by an alternating series of expansions and heat treatments as it is gradually shaped into a sphere using customized tooling. The repeated heat treatment in each expansion stage relieves all residual stresses within the device, thus allowing subsequent expansion to the next size without fractures. Through these procedures, a nitinol spherical occlusion device designed for the purposes of flow occlusion can be produced.  
A laser module consisting of a Rofin 100 W pulsed fiber laser, an Aerotech linear X-Y motor stage, and a Z-direction server motor were integrated, with Argon as the assisted gas. The linear motion and rotation of the hypotube were provided by the linear X-Y motor stage, while the distance between the laser source and hypotube surface was controlled by the Z-direction server motor to achieve the optimal focal depth. A 2-D drawing was first sketched on the X-Y plane and then coded into the 3-D cylindrical coordinate system. The design pattern was cut onto a seamless hypotube based on the coded geometry (Figure 4). The control algorithm used to coordinate the linear X-Y motor stage with the timing of laser firing was the Position Synchronized Output.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Total Element Number** & **Flow Rate at Inlet, cm3/s** & **Flow Rate at Outlet 1, cm3/s** & **Flow Rate at Outlet 2, cm3/s** \\ \hline
597,575 & 13.668 & 6.837 & 6.831 \\
1,042,898 & 13.658 & 6.834 & 6.823 \\
1,410,707 & 13.654 & 6.831 & 6.822 \\
1,936,959 & 13.651 & 6.830 & 6.821 \\
2,548,140 & 13.647 & 6.827 & 6.820 \\
3,155,138 & 13.647 & 6.826 & 6.820 \\ \hline \end{tabular}
\end{table}
Table 1: Mesh sensitivity study for stent computational fluid dynamics (CFD) model.  
Figure 3: Hepatic artery bifurcation model for numerical simulations.  
Nitinol possesses the properties of super elasticity and shape memory, allowing the device to memorize and return to its designed shape upon release. In order to create such behavior, special heat treatment was implemented after laser cutting to shape the device to the desired levels. Steel balls with diameters of 2.0 mm, 3.0 mm, 4.0 mm, and 5.2 mm were used sequentially to expand the device to its target size (Figure 5a). After the final diameter was reached, a special fixture was used to help shape the device into a perfect sphere from the outside (Figure 5b). Heat treatment was applied after each expansion stage to shape the device and relieve its residual stresses. The two most important parameters for heat treatment are time and temperature. The high temperatures associated with laser cutting and heat treatment may create spatter, oxide layers, and other debris that must be removed with further surface finishing. Surface finishing was accomplished in two major steps: sand-blasting and electro-polishing. Experiments were conducted to optimize the combination of processing parameters for heat treatment and surface finishing.
# Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature
## 3 Methodology
### Tensor Feature Decomposition and Shape Classification Based on Tensor Analyzing  
As represented in Section 3.2, there are many kinds of geometric structures encoded in the tensor of each point, ranging from 1-dimensional, 2-dimensional, to \(N\)-dimensional structures. Through the eigenvalue decomposition, eigenvalues and eigenvectors are computed as follows:  
1. eigenvalues \(\left(\gamma_{\mathbf{1}}\gamma_{\mathbf{2}},\cdots,\gamma_{N}\right)\) with descending order, i.e., referred to as the intensity of each eigenvector;
2. eigenvectors \(\left(\overset{\rightarrow}{\kappa}_{\mathbf{1}},\overset{\rightarrow}{ \kappa}_{\mathbf{2}},\cdots,\overset{\rightarrow}{\kappa}_{N}\right)\), i.e., referred to as the dimension of the normal space, which is orthogonal to the manifold structures of \(G\).  
According to Equation (7), the \(d\)-dimensional geometric structure can be computed by the eigenvalues, along with the related eigenvectors. For example, as depicted in Figure 4, in the space \(S\) (\(N=3\)):  
1. the geometric structure with the 1-dimensional stick-shaped normal space, where there is just one normal vector \((\overset{\rightarrow}{\kappa}_{\mathbf{1}})\), tends to be the \"surface\", and the geometric structure saliency \(s_{\mathbf{1}}=\gamma_{\mathbf{1}}-\gamma_{\mathbf{2}}\);
2. the geometric structure with 2-dimensional surface-shaped normal space, where there are two normal vectors \((\overset{\rightarrow}{\kappa}_{\mathbf{1}},\overset{\rightarrow}{\kappa}_ {\mathbf{2}})\), tends to be the \"line\" and the geometric structure saliency \(s_{\mathbf{2}}=\gamma_{\mathbf{2}}-\gamma_{\mathbf{3}}\);
3. the geometric structure with 3-dimensional ball-shaped normal space, where there are three normal vectors \((\overset{\rightarrow}{\kappa}_{\mathbf{1}},\overset{\rightarrow}{\kappa}_ {\mathbf{2}},\overset{\rightarrow}{\kappa}_{\mathbf{3}})\), tends to be the \"point\" and the geometric structure saliency \(s_{\mathbf{3}}=\gamma_{\mathbf{3}}\).  
Using the above method, each point \(p\) is labeled with a shape descriptor \(p(s_{\mathbf{1}},s_{\mathbf{2}},\cdots,s_{N})\), ranging from dimension 1 to dimension \(N\). In addition, the normal vector can be further refined by using \(\overset{\rightarrow}{\kappa}_{\mathbf{1}}\), under the assumption that objects captured by the LiDAR point cloud have continuous surfaces, and the vector \(\overset{\rightarrow}{\kappa}_{\mathbf{1}}\) is the main vector that can be encoded into the 1-dimensional normal space.
## 1 Introduction
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Year** & **Sample** & **Sampling** & **Modulator** & **Column Setup** & **Reference** \\ \hline
2005 & wine & HS-SPME & thermal modulator & BPX5, BP20 & [11] \\
2005 & wine, beer & SPE (derivatization) & thermal modulator & BPX5, BP20 and Sologel Wax, BP1 & [12] \\
2007 & beer & derivatization & thermal modulator & Chirasil-L-Val, BPX50 and & [13] \\
2007 & wine & HS-SPME & thermal modulator & Equity-5, Supelcowax-10 & [14] \\
2010 & wine & HS-SPME & thermal modulator & ZB-Wax, BPX5 & [15] \\
2010 & wine & HS-SPME & thermal modulator & HP-5, DB-FEAP & [16] \\
2011 & wine & HS-SPME & thermal modulator & VF-5 MS, VF-17 MS & [17,18] \\
2011 & wine & HS-SPME & thermal modulator & VF1-MS, SolGel-Wax & [19] \\
2011 & wine & HS-SPME & thermal modulator & VF-1, SolGel–Wax & [20] \\
2011 & wine & HS-SPME & thermal modulator & RTX5, VF-WAXms & [21] \\
2011 & wine & SPE & thermal modulator & DB-FFAP, DB-5 & [22] \\
2011 & wine & HS-SPME & thermal modulator & HP-5, DB-FEAP & [23] \\
2012 & wine & HS-SPME & thermal modulator & dimethylpolysiloxane; & [24] \\  & & & & and DB-Wax, DB-17 ms & \\
2012 & wine & LLE & thermal modulator & BPX5, BP20 & [25] \\
2013 & wine & HS-SPME & thermal modulator & DB-Wax, DB-17 ms & [26] \\
2013 & wine & HS-SPME & thermal modulator & DB-5, DB-225 & [27] \\
2013 & wine & HS-SPME & thermal modulator & DB-5 MS, Supelcowax-10 & [28] \\
2014 & wine & HS-SPME & flow modulator & SD-5, Supelcowax-10 & [29] \\
2014 & wine & HS-SPME & thermal modulator & Carbovax, DB-17 ms & [30] \\
2015 & wine & HS-SPME & thermal modulator & DB-5, DB-17 ms & [31] \\
2015 & wine & Cumulative SPME & thermal modulator & DB-FFAP, BPX5 & [32] \\
2016 & hop & hydro-distillation & thermal modulator & MEGA-Wax MS, BPX5 & [33] \\
2016 & wine & HS-SPME & thermal modulator & VF-Wax, RTX-200 MS & [34] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Articles on applying GC\(\times\)GC to beer and wine analysis.
## Appendix A
### Inverse Chebyshev Filter  
To obtain the transfer function of the inverse Chebyshev high-pass filter, the transfer function of the direct Chebyshev low-pass filter should be subtracted from unity [38]. In other words, the algorithm for obtaining the transfer function of the inverse Chebyshev high-pass filter is as follows:  
1. In the transfer function of the normalized inverse Chebyshev low-pass filter, transition to the given cut-off frequency is performed by substituting the value into the following: \[\rm s\rightarrow\frac{s}{\omega_{cut}}\] (15)The resulting expression is subtracted from 1 to obtain the transfer function of the high-pass inverse Chebyshev filter.  
The AFR of the inverse Chebyshev high-pass filter is obtained according to Equation (15). The AFR of the inverse Chebyshev high-pass filter is shown in Figure 14c.  
As can be seen from Figure 14c, the AFR of the inverse Chebyshev high-pass filter has a smooth character in the passband, and there are oscillations in the stopband.
# An Integrated Cellular Automata Model Improves the Accuracy of Secondary Fragmentation Prediction
## 4 Mine-Scale Application  
Because each mining operation defines \"fine material\" according to its particular conditions (type of rock, presence of water) and the problem being addressed (mud rushes, fine bursts, cohesive hangings, etc.) different classification criteria were applied to evaluate the generation of fine material during secondary fragmentation. The fragment size that constitutes fine material according to three distinct mining operations' definitions was used in the scaled mine-model application described below. For example, in Esmeralda (El Teniente), fragments smaller than 5 cm are considered fine material; in Diablo Regimiento (El Teniente), those less than 7 cm; and in Palabora Lift 1, fragments smaller than 1 cm.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 6 Results and Discussion
### Evaluation Summary  
In this section, we summarize our findings after studying the performance results under normal operating conditions and under the challenges. We found that, under the challenges, the proposed MPResiSDN scheme improved data delivery in terms of the obtained goodput by up to 100% compared to Spanning Tree when a suitable value for \(k\) diverse paths was selected. However, to maintain this enhancement, the value of \(k\) should be increased as the severity of the challenge increases in terms of the number of nodes it affects and whether the affected nodes belong to the used paths. However, selecting a value larger than the required value of \(k\) results in massive unnecessary overhead, i.e., overhead can increase by up to 200% or more. Thus, under normal operation without any challenge, selecting a value that is greater than 1 for \(k\) diverse paths is completely inefficient because overhead increases without providing any observable benefit.  
Considering the effect of the proposed MPResiSDN scheme on end-to-end delay, it is evident that the end-to-end delay in the running phase is not affected. However, the proposed system outperformed Spanning Tree in terms of response time initially when the topology was identified; topology identification is faster with the proposed scheme. Moreover, the proposed system outperforms Spanning Tree when it comes to response time to changes in network topology.
# Supporting Privacy of Computations in Mobile Big Data Systems +
## 6 Privacy Preserving Search for the Nearest Bank/ATM  
In this section, we consider the special scenario of a mobile device seeking to obtain the result of a computation that depends on its own \"private inputs\", and also on \"public inputs\" obtained from other servers on the Internet. More specifically, we examine the following privacy preserving application. A mobile client, which is located at the intersection of two streets, needs to determine the location of the nearest Chase or Wells Fargo banks or an ATM machine in a privacy-preserving manner. We evaluate our application using real-world data available for Salt Lake City, UT, whose streets are arranged in a \"grid pattern\". Our application assures the privacy of the following: (i) the mobile client's input location; (ii) the computed bank/ATM location nearest to the client; and (iii) the computed distance to the nearest ATM. Note that these secrets are revealed to the evaluator only if it colludes with all the \(n\) servers that participate in the creation of the garbled circuit.  
We consider an area of Salt Lake City, UT that lies between the Main Street (which represents the 0 East Street), the 1300 East Street, the South Temple Street (which represents the 0 South Street), and the 800 South Street. This area consists of \(L=10\) ATM locations that are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Bank/ATM** & **Location** \\ \hline Chase & 201 South 0 East \\ Chase & 185 South 100 East \\ Chase & 376 East 400 South \\ Chase & 531 East 400 South \\ Wells Fargo & 299 South 0 East \\ Wells Fargo & 381 East 300 South \\ Wells Fargo & 79 South 0 East \\ Wells Fargo & 778 South 0 East \\ Wells Fargo & 570 South 700 East \\ Wells Fargo & 235 South 1300 East \\ \hline \hline \end{tabular}
\end{table}
Table 2: Locations of Banks and ATMs in Salt Lake City, UT (_source: Chase [19], Wells Fargo [20])_.  
Figure 9: Time taken to evaluate one garbled gate.  
Each East/South coordinate in this area is an \(l=max(\lceil log_{2}1300\rceil,\lceil log_{2}800\rceil)=11\)-bit unsigned number Therefore, the location of the mobile client at an intersection, or any bank/ATM in this area can be identified using \(L_{ind}=2l=22\) bits.
## 6 Performance Evaluation
### Efficiency Evaluation
Figure 10a and Figure 10b investigate the impacts of various update intervals (_i.e._, varying \(T_{u}\)) on the CPU time and the number of accessed cells for the proposed approaches (that is, the KNN approach and the PNNQ approach), respectively. In the experiments, we vary \(T_{u}\) from 5 to 25 time units and measure the performance for the two approaches. As shown in Figure 10a, for the larger \(T_{u}\) (e.g., \(T_{u}=20\) or 25 time units), the frequency of object's location being changed is lower so that the degradation of the CPU time for the PNNQ approach is not significant. However, as \(T_{u}\) decreases (_i.e._, more location changes occur), the PNNQ approach performs significantly worse than the KNN approach. In Figure 10b, we measure the number of accessed cells for the KNN and PNNQ approaches as a function of \(T_{u}\). The experimental result shows that the number of accessed cells is basically constant for different \(T_{u}\), which implies that the performance of the proposed approaches is insensitive to the value of \(T_{u}\). The result also confirms again that applying the KNN approach can greatly improve the performance of processing the \(K\)NN query. Figure 8: Effect of object distribution. Figure 9: Effect of number of objects. The last experiment evaluates the effect of the number of nearest neighbors (_i.e._, \(K\)) on the CPU time and the number of accessed cells of the KNN approach and the PNNQ approach. As shown in Figure 11a, when the value of \(K\) increases, the CPU overhead for both the KNN approach and the PNNQ approach grows. The reason is that as \(K\) becomes greater, the number of qualifying objects inevitably increases so that more distance computations of these objects are required. Nevertheless, in terms of CPU time, the KNN approach outperforms the PNNQ approach by a factor of up 10 in all cases. As for the effect of \(K\) on the number of accessed cells shown in Figure 11b, the curve for the KNN (PNNQ) approach has the increasing (constant) trend. The increasing trend for the KNN approach is because as \(K\) increases, the \(K^{\text{th}}\) NN becomes farther from the query point and hence more cells would be accessed. Nevertheless, the KNN approach reduces the number of accessed cells by almost two orders of magnitude compared to the PNNQ approach.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 3 System Model and Problem Formulation
### Received Signal  
Let \(h_{d}\in\mathbb{C}\) denote the direct complex channel gain from the NTN node to the user. Let \(\mathbf{g}\in\mathbb{C}^{M}\) be the channel vector from the RIS to the user, where \((\cdot)^{H}\) denotes the Hermitian (conjugate) transpose operation. This operation is used to compute the contribution of the reflected signal from the RIS. Similarly, let \(\mathbf{h}\in\mathbb{C}^{M}\) represent the channel vector from the UAV to the RIS.  
The RIS phase-shift matrix is defined as  
\[\mathbf{\Phi}=\text{diag}\Big{(}e^{i\phi_{1}},e^{i\phi_{2}},\ldots,e^{i\phi_{m }}\Big{)},\]  
where \(\phi_{m}\in[0,2\pi)\) represents the phase shift induced by the \(m\)-th RIS element.  
The received signal at the user is given by  
\[y=\underbrace{\Big{(}h_{d}+\mathbf{g}^{H}\mathbf{\Phi h}\Big{)}s}_{\text{ Desired signal}}+\underbrace{n}_{\text{Noise}} \tag{1}\]  
where \(s\sim\mathcal{CN}(0,P)\) is the transmitted symbol, modeled as a circularly symmetric complex Gaussian random variable with zero mean and power \(P\); and \(n\sim\mathcal{CN}(0,\sigma^{2})\) denotes the additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma^{2}\).