esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Root Parallelism in Extraction Cases Treated with Clear Aligners vs. Fixed Appliances
## 3 Method of Measurement:  
Panoramic radiographs of the patients before and after orthodontic treatment (taken by the same machine) were collected and measured blindly in a picture archiving and communication system (PACS system). The measurement of tooth angulation involved the canines, second premolar, first molar, and second molar.  
The maxillary reference line was drawn, connecting the lowest points of the orbital rims, and the mandibular reference lines were the tangent lines of the mandibular margin on both sides [17,19,20]. The root axes of these teeth were drawn connecting the midpoint of the mesiodistal diameter of the dental crown along the root canal path or root bifurcation (Figure 1).  
The angle of each target tooth was measured and collected, and the mesial angle between the long axis of the tooth and the reference line is shown in Figure 1. t3, t5, t6, and t7 indicated the pretreatment angulation of the canines, second premolars, first molars, and second molars, respectively. In addition, T3, T5, T6, and T7 referred to the post-treatment angulation of the aforementioned teeth, respectively. All the measurements were conducted by the same operator twice after a three-week interval; satisfactory consistency was achieved between this interval, and the average values were calculated as the final results.  
The t3-t5 angulation indicated the absolute values of the angulation of the canines minus the angulation of the second premolars in pretreatment, while the T3-T5 angulation indicated the same in post-treatment, and the angle difference value indicated the absolute values of the t3-t5 angulation minus the T3-T5 angulation.  
In general, for the difference in T3-T5, the satisfactory parallelism ranges from \(-6^{\circ}\) to \(+6^{\circ}\); it is called apical convergence if it is less than \(-7^{\circ}\) or apical divergence if more than \(7^{\circ}\)[21]. If the difference value in t6-T6 and t7-T7 are below \(-7^{\circ}\), the dental crown of the molars can be considered distal tipping; on the contrary, the dental crown of the molar is mesial tipping, if it is more than \(7^{\circ}\), and if it is between \(-7^{\circ}\) and \(7^{\circ}\), the tooth axis is assumed to be non-tipping. To ensure the two treatment methods were comparable, the aligner and fixed groups were divided into two subgroups of parallelism (P\({}_{1}\)) and  
Figure 1: Measurement of the root axes. The red box shows the determination of the long axis of the posterior tooth); the mesial angle between the long axis of the tooth and the reference line is the angulation of these teeth (canines, second premolars, first molars, and second molars).  
nonparallelism (NP\({}_{1}\)), according to the absolute value of the t3-t5 angulation (7\({}^{\circ}\)) before treatment. Moreover, they were divided into another two subgroups of parallelism (P\({}_{2}\)) and nonparallelism (NP\({}_{2}\)), according to the absolute value of the T3-T5 angulation (7\({}^{\circ}\)) after treatment.
# W-Band Modular Antenna/Detector Array for the Electron Cyclotron Emission Imaging System in KSTAR
The performance of each unit antenna/detector module is also optimized to have higher gain and more flat frequency response as compared with the conventional module. This study is the first published work that discusses in detail about the improved antenna/detector array for the ECE imaging system at KSTAR.
# Extracting the Secrets of OpenSSL with RAMBleed
## 5 Analysis of OpenSSL and Apache
### Investigating Whether Physical Memory is Allocated for Secret Information
#### 5.2.1 Identifying the Virtual Addresses of Secret Information  
In the pre-fork model, child processes are created by the fork-system call. Immediately after a fork is executed, the parent and child processes share the same virtual memory using copy on write (COW) to suppress unnecessary memory consumption. However, when a child process executes a write operation on a virtual memory page, the corresponding page in the shared virtual memory is duplicated, and only the child process can access that page. This is because a page fault occurs, and the kernel allocates a new physical memory page as no physical memory page is allocated for the duplicated virtual memory page.  
We focused on this behavior and analyzed it, theorizing that secret information is written to virtual memory when the child process performs the TLS handshake and that a physical memory page allocation occurs. To simplify the analysis, MaxSpareServers was set to one; therefore, there was always one idle child process, which limits the number of child processes communicating. When the TLS handshake was executed, the SSL/TLS version was specified by the client as TLS1.3 and the cipher suite as AES256-GCM-SHA384. The investigation was conducted using the following procedure.  
1. Obtain a virtual memory dump, A, of the idle child process using the gcore command;
2. Send a TLS request and conduct a TLS handshake with only one child process. After the handshake, a virtual memory dump, B, of the child process conducting the TLS communication is obtained;
3. Search virtual memory dumps A and B, and identify the virtual address of the secret information. If there is a virtual address for secret information that exists only in B, we know that the secret information with that address was written to the virtual memory of the child process when the TLS handshake was executed.  
The above analysis was conducted both on a child process that had performed the TLS handshake after its creation and a child process that had not. The virtual addresses of \(p\) and \(q\) did not change before and after the TLS handshake, and no new secret information was written for the child process that had already executed the TLS handshake. Conversely, we discovered that new prime numbers \(p\) and \(q\) were written to virtual memory after the TLS handshake was executed for the child process that had not executed the TLS handshake; we could identify their virtual addresses.
## 4 Method Validation  
To assess the effectiveness of the enhanced algorithm, two sets of comparative experiments were devised in this study. In the initial set of experiments, we selected seven unimodal test functions, characterized by having only one extremum, to verify the algorithm's convergence speed, optimization accuracy, and local search capability. The second set of experiments involved the selection of five multimodal test functions, characterized by having multiple local extremum points, to examine the algorithm's performance in escaping local optima and possessing global exploration capabilities. To reduce the bias in single-run results, we conducted 30 iterations for each function and documented their optimal values, mean values, and standard deviations. Simultaneously, we compared the improved algorithm with the PSO, MVO, WOA, and SSA. Table 1 provides the detailed parameter settings of each algorithm. All experiments were executed on a computer featuring an Intel i7 processor operating at 2.30 GHz and 16 GB of RAM, utilizing the MATLAB 2023a environment. This experimental design and environment setup aimed to comprehensively and reliably evaluate algorithm performance and facilitate an objective comparison. Figure 2: **(a)** Three-dimensional plot of \(F_{1}\); (b) convergence curve.
## 5 Analysis of Numerical Model Results
### Analysis of Sudden Water Surges in Tunnels under Coupled Blast Load and Water Pressure  
Taking the calculation model of rock wall thickness \(d\) = 1.2 m as an example to analyze the evolution process of tunnel water inrush under blasting conditions, it can be seen from Figure 9 that, when \(t\) = 9 ms, the blasting stress wave is transmitted to the aquifer, and the SPH particles on the near-explosion side in the aquifer are squeezed against each other due to the impact of the stress wave. The SPH particles are distributed in a waveform. When \(t\) = 360 ms, under the action of gravity and the blasting stress wave, the SPH particles in the aquifer begin to move to the tunnel along the blasting crack and squeeze the surrounding rock of the tunnel face, accelerating the development of the cracks in the surrounding rock of the tunnel face, and consequently expanding the tunnel's water-gushing channel. From the calculation diagram of \(t\) = 810 ms\(\sim\)1800 ms, it can be seen that, as the SPH water particles surge along the interior of the tunnel under the action of gravity, the cracks on the tunnel face evolve from the initial burst cracks to the thick water-gushing channels, and the SPH particles in the aquifer are accelerated. The surge into the tunnel is embodied in the accelerated increase in the cumulative particle number inside the tunnel and the emergence of larger and larger voids at the top of the aquifer.  
The changing trend of the vibration velocity curve of the second measuring point under the coupling action of blasting load and hydraulic pressure is analyzed. The selection of measurement point #2 is shown in Figure 6. It can be seen from Figure 10 that, during the initial stage, the vibration velocity reaches a peak value of 1.15 cm/s in the first 10 ms, and the vibration velocity during the initial stage is mainly affected by the explosion load, and then decreases rapidly. However, the vibration velocity curve starts to increase rapidly  
Figure 8: Stress\(-\)time curve at measurement point #1 under blast load and water pressure: (**a**) Stress\(-\)time curve at measurement point #1 under blast load. (**b**) Stress\(-\)time curve at measurement point #1 under water pressure.  
Figure 9: Indication of water inrush in the tunnel (\(d\) = 1.2 m).  
from \(t=500\) ms, and the peak value increases continuously. When \(t=2\) s, the vibration velocity rapidly increases from 0.28 cm/s to 1.72 cm/s, which is mainly because the sudden change in the tunnel occurs at \(t=500\) ms. In the process of water inrush deformation, SPH water particles enter the tunnel along the crack channel, and the movement speed increases greatly. This process is reflected in the second half of the vibration velocity curve, and the vibration velocity of the second measurement point increases rapidly.
## 5 Simulation Analysis
### Simulation Results
#### 5.2.2 Performance of Different Packet-Sending Probability  
The SQL-CBRP proposed in this paper makes routing decisions based on the queue length of neighboring nodes. Therefore, the number of packets in the network is likely to affect the performance of the algorithm. To test the impact of the number of network packets, this section sets different packet-sending probabilities and tests the performances of the Dijkstra algorithm, GPSR algorithm, and SQL-CBRP algorithm under different sending probabilities.  
According to Figure 7, when the packet-sending probability is 60%, the delay and packet loss rate of SQL-CBRP are slightly higher than those of GPSR, but the difference is negligible. When the packet-sending probability is 80% and 100%, the delay and packet loss rate of SQL-CBRP are reduced to some extent compared to GPSR. Specifically, the delay is reduced by up to 18.0% relative to GPSR, and the packet loss rate is reduced by up to 22.1% relative to GPSR.  
Figure 6: Packet loss rate and delay of voice, message, and video service in Dijkstra, GPSR, and SQL-CBRP. (**a**) Packet loss rate of different types of packets; (**b**) Delay of different types of packets.  
Figure 7: Packet loss rate and delay of Dijkstra, GPSR, and SQL-CBRP when the probability of sending packet is 60%, 80%, and 100%. (**a**) Packet loss rate of different packet-sending probability; (**b**) Delay of different packet-sending probability.
## 4 Experimental Results
However, as modern NVIDIA GPU can launch up to 1024 threads simultaneously, the dimensions smaller than 1024 used by hipSYCL cause frequent context changes, leading to lower theoretical occupancy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**API** & **CUDA** & **HipSYCL** \\ \hline cudaMemcpy (Host to Device) & 319 μs & 504 μs \\ \hline Kernel Launch & 8.18 ms & 1.14 s \\ \hline cudaMemcpy (Device to Host) & 338 μs & 537 μs \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Execution time of individual CUDA application programming interface (API). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operation** & **Size** & **Intel Generation 9** & **NVIDIA Maxwell** \\ \hline \multirow{4}{*}{Vector Addition} & 32 & 88 ms & 2.960 ms \\  & 1024 & 88 ms & 3.048 ms \\  & 4096 & 88 ms & 3.044 ms \\  & 1,048,576 & 100 ms & 31.513 ms \\  & 16,777,216 & 257 ms & 117.690 ms \\ \hline \multirow{4}{*}{Matrix Multiplication} & 32 & 140 ms & 3 ms \\  & 256 & 143 ms & 18 ms \\  & 1024 & 252 ms & 80 ms \\  & 2048 & 1148 ms & 584 ms \\ \hline \multirow{4}{*}{Matrix Multiplication (Local Memory)} & 32 & 165 ms & 3 ms \\  & 256 & 170 ms & 15 ms \\ \cline{1-1}  & 1024 & 382 ms & 59 ms \\ \cline{1-1}  & 2048 & 1995 ms & 285 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Execution time of operations on NVIDIA and Intel graphics processing unit (GPU). Moreover, older architecture like NVIDIA Fermi has only 32 registers available per thread. Therefore, a hipSYCL generated kernel code may lead to hazardous security issues during compilation or execution on GPUs with outdated architecture.
## 1 Introduction
The purpose is to enhance video compression efficiency and reduce transmission cost to the sink. * A sequential compressed sensing (SCS) approach is explored to decide whether enough measurements are obtained to limit video reconstruction error to a specified threshold. With the approach, we select collaborative nodes to carry out DCS in a cluster without any a priori assumptions regarding signal sparsity. * A distributed spectrum-aware QoS routing framework is presented to transmit the compressed video data with consideration of the condition for video frame decoding. The goal to minimize energy consumption subject to delay and reliability constraints. * The effectiveness and superiority of CDCS are validated through extensive simulations. The results show that CDCS could achieve energy efficient QoS communication while maintaining acceptable image quality. The remainder of this paper is structured as follows. Section 2 introduces background and existing research about in-network processing and QoS routing. Section 3 motivates this work. We present the implementation details including clustering, node collaboration, and route selection in Section 4. Performance evaluation is given in Section 5, followed by the conclusions in Section 6.
# Radiator Enablers for Wireless Communication Evolution
## 4 Conclusions
\begin{tabular}{l l} ABF & Analog beamformer. \\ ACS & Asymmetric Coplanar Stripline. \\ AI & Artificial intelligence. \\ AiP & Antenna in Package. \\ BS & Base station. \\ CCA & Conical Conformal Array. \\ CELC & Complementary electric inductive-capacitive. \\ CP & Circularly polarized. \\ CPW & Coplanar Waveguide. \\ CSI & Channel state information. \\ CVD & Chemical vapor deposition. \\ DBF & Digital Beamformer. \\ DBI & Dual-Band Independent. \\ DMA & Dynamic metasurface antenna. \\ DRAs & Dielectric resonator antennas. \\ DULA & Distributed uniform linear arrays. \\ EBG & Suspended electromagnetic bandgap. \\ EM Waves & Electromagnetic waves. \\ eMBB & Extreme mobile broadband. \\ F2M & Fixed-to-mobile. \\ FER & Frame error rate. \\ FPGA & Field programmable gate array. \\ FPGAs & Field programmable gate arrays. \\ HMA & Holographic metasurface-based antenna. \\ \end{tabular}   
HMIMOS & Holographic Multiple-Input and Multiple-Output Surface. HRLLC & Hyper Reliable and Low-Latency Communications. IoTs & Internet of Things. IRS & Intelligent Reflecting Surface. ITS & Intelligent transmitting surfaces. KPIs & Key performance indicators. LEO & Low Earth Orbit. LHCP & Left-Handed Circular Polarization. LP & Linear polarization. LTCC & Low-Temperature Co-fired Ceramic. MIMO & Multiple Input Multiple Output. ML & Machine learning. mmMIMO & Modular massive MIMO. mMTCs & Massive Machine Type Communications. mmWaves & Millimeter waves. NTNs & Non-terrestrial networks. OAM & Orbital angular momentum. OFDM & Orthogonal Frequency Division Multiplexing. PCB & Printed circuit board. PHY & Physical layer. PPW & Parallel Plate Waveguide. PRAs & Parabolic reflector antennas. PSIs & Planar spiral inductors. PUCAs & Primary uniform circular arrays. PVD & Physical vapor deposition. QF-UCA & Quasi-fractal uniform circular array antenna. RAs & Reflect Array Antennas. RF & Radio Frequency. RF MEMS & Radio Frequency Micro-Electro-Mechanical Systems. RHCP & Right-handed circular polarization. RHS & Reconfigurable holographic surface. RIS & Reconfigurable intelligent surfaces. RLLC & Reliable Low-Latency Communication. RRS & Reconfigurable Refractive Surface. SAM & Spin angular momentum. SatCOM & Satellite Communications. SICs & Substrate Integrated Circuits. SIM & Stacked intelligent metasurface. SIWs & Substrate integrated waveguides. SLL & Side Lobe Level. SNR & Signal-to-Noise Ratio. SOC & System On Chip. SOP & System on Package. SPDT & Single Pole Double Throw. SPP & Spiral phase plates. TDM & Time division multiplexing. TTI & Transmission time interval. UCAs & Uniform circular arrays. UCPA & Uniform Circular Planar Array. UE & User Equipment. UE-CoMIMO & End user collaborative MIMO.
## 2 Problem Formulation and Preliminaries
### AUV Auxiliary State Dynamics System  
Let us consider a continuous-time reference model described as follows:  
\[\begin{array}{rcl}\dot{x}_{r}(t)&=&A_{r}x_{r}(t)\\ y_{r}(t)&=&C_{r}x_{r}(t).\end{array} \tag{7}\]  
where \(x_{r}\in\mathbb{R}^{n_{r}}\) and \(y_{r}\in\mathbb{R}^{q_{r}}\) are the state and the output of the reference model, respectively. We define an auxiliary state vector \(z(t)\) as follows:  
\[z(t)=x(t)-Gx_{r}(t). \tag{8}\]  
where \(G\in\mathbb{R}^{n\times n_{r}}\) is a matrix, as defined in [19]. By using outputs (1) and (13), we have an output tracking error:  
\[e(t)=y(t)-y_{r}(t)=Cz(t). \tag{9}\]  
Now, by combining the systems (4) and (13), the auxiliary state dynamics system is obtained:  
\[\begin{array}{rcl}\dot{z}(t)&=&(A+\Delta A)z(t)+(A_{d}+\Delta A_{d})z(t-\tau (t))+Bu(t)\\ &&+B_{w}w(t)+\Delta AGx_{r}(t)+(A_{d}+\Delta A_{d})Gx_{r}(t-\tau(t)).\end{array} \tag{10}\]  
Next, by combining the systems (6) and (13), the auxiliary state dynamics system is obtained:  
\[\begin{array}{rcl}\dot{z}(t)&=&(A+\Delta A)z(t)+(A_{d}+\Delta A_{d})z(t-\tau (t))+Bu(t)+B_{d}u(t-\tau(t))\\ &&+B_{w}w(t)+\Delta AGx_{r}(t)+(A_{d}+\Delta A_{d})Gx_{r}(t-\tau(t)).\end{array} \tag{11}\]  
**Definition 1** ([26]).: _System (1) is said to satisfy robust \(H_{\infty}\) performance if the following conditions are satisfied:_  
1. _The closed-loop system from (_1_) with_ \(w(t)=0\) _is asymptotically stable for admissible uncertainties satisfying (_3_)._
2. _Under the zero initial condition, one satisfies:_ \[\int_{0}^{\infty}y^{T}(t)y(t)dt\leq\gamma^{2}\int_{0}^{\infty}w^{T}(t)w(t)dt\] _where_ \(\gamma>0\) _is a given constant._  
Note that the output tracking error \(e(t)=y(t)-y_{r}(t)\) depends on the auxiliary state dynamics system (10); we define the \(H_{\infty}\) tracking performance index as follows:  
\[\int_{0}^{\infty}e^{T}(t)e(t)dt\leq\gamma^{2}\int_{0}^{\infty}w^{T}(t)w(t)dt.\]  
**Lemma 1** ([27]).: _(Schur complement) For a given matrix \(S=\begin{bmatrix}S_{1}&S_{3}\\ *&S_{2}\end{bmatrix}\) with \(S_{1}=S_{1}^{T}\) and \(S_{2}=S_{2}^{T}\), then the following conditions are equivalent:_  
1. \(S<0\)_;_
2. \(S_{2}<0\)_,_ \(S_{1}-S_{3}S_{2}^{-1}S_{3}^{T}<0\)_._  
**Lemma 2** ([28]).: _(Jensen's Inequality) For any symmetric positive definite matrix \(S\in\mathbb{R}^{n\times n}\), scalars \(\tau_{m}\) and \(\tau_{M}\) satisfying \(\tau_{m}<\tau_{M}\), vector function \(x:[\tau_{m},\tau_{M}]\rightarrow\mathbb{R}^{n\times n}\), the following integral inequality holds:_  
\[\left(\int_{\tau_{m}}^{\tau_{M}}x(s)ds\right)^{T}\!\!S\!\left(\int_{\tau_{m}}^{ \tau_{M}}x(s)ds\right)\leq(\tau_{M}-\tau_{m})\int_{\tau_{m}}^{\tau_{M}}x^{T}( s)Sx(s)ds.\]
## 2 Theory and Methods
### Energy Theory  
The total energy involved in the loading process of rock specimens is mainly generated by external forces and stored in the form of strain energy. The total energy within the specimen can be expressed as a field function, namely, the strain energy density, and can be divided into two categories: (1) the strain energy stored in the rock unit as a result of deformation as a continuous mechanical medium, referred to as elastic strain energy; (2) the strain energy consumed due to discontinuous displacements such as friction or the micro-slip of the mineral grains in the rock specimen, referred to as dissipation strain energy. The composition of strain energy density in the principal stress space can be represented as  
Figure 1: Schematic diagram of cracked granite specimen.  
Figure 2: MTS 815 rock mechanics testing system.  
shown in Figure 3. The shaded area represents the elastic strain energy stored in the rock specimens, while the blank area represents the dissipation strain energy.  
By applying the first law of thermodynamics within the closed system, we can obtain the following:  
\[U=U_{e}+U_{d} \tag{1}\]  
where \(U\) is the total strain energy density, \(U_{e}\) is the elastic strain energy density, and \(U_{d}\) is the dissipation strain energy density.  
The strain energy in the principal stress space is defined as:  
\[U=\int_{0}^{\epsilon_{i}}\sigma_{i}d\epsilon_{i} \tag{2}\]  
where \(\sigma_{i}\) and \(\epsilon_{i}\) represent the stress and strain components in the principal stress direction, respectively.  
The formula for calculating the elastic strain energy of a continuous medium is as follows:  
\[U_{e}=\frac{1}{2}\sigma_{i}\epsilon_{i} \tag{3}\]  
By substituting the generalized Hooke's law into Equation (3), we obtain the following:  
\[U_{e}=\frac{1}{2E_{0}}\left[\sigma_{1}^{2}++\sigma_{2}^{2}+\sigma_{3}^{2}-2v( \sigma_{1}\sigma_{2}+\sigma_{1}\sigma_{3}+\sigma_{2}\sigma_{3})\right] \tag{4}\]  
where \(E_{0}\) represents the initial elastic modulus, and \(v\) represents the [PERSON]'s ratio.  
In uniaxial compression tests, \(\sigma_{2}=\sigma_{3}=0\). Thus, the total elastic strain energy and the dissipation strain energy can be expressed as:  
\[U_{e}=\frac{1}{2E_{0}}\sigma_{1}^{2} \tag{5}\]  
\[U_{d}=\int_{0}^{\epsilon_{1}}\sigma_{1}d\epsilon_{1}-\frac{1}{2E_{0}}\sigma_{ 1}^{2} \tag{6}\]