esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Low-Depth SHA3 Quantum Circuit for Fault-Tolerant Quantum Computers
## 2. Preliminaries  
### Quantum Computing  
Quantum computers can solve specific problems faster than classic computers by using the quantum mechanical properties of qubits for operation. In a quantum computer, data are expressed in qubits, and operations are performed by manipulating the state of qubits through a reversible circuit. The qubit exists in a superposition state that is probabilistically 0 and 1 at the same time throughout the operation until the final measurement. Thus, the qubit exists in multiple states at the same time. Due to this property, the operation for all cases of 0 and 1 can be probabilistically calculated at once, so the calculation speed is fast, and a probabilistic result is output at the last measurement. The measurement probability for a qubit in a particular state can be described by the probability amplitude associated with the state.  
The superposition of qubits via the Hadamard gate is expressed as:  
\[|\psi\rangle=\alpha|1\rangle+\beta|0\rangle,\quad|\alpha|^{2}+|\beta|^{2}=1\]  
A qubit in superposition produces an eigenvalue of either 0 or 1 after measurement, and the value is unknown before measurement [10]. In the above expression, \(\alpha\) and \(\beta\) refer to the probability amplitude, and if \(|\alpha|^{2}\) is 0, \(|\beta|^{2}\) is 1, and vice versa.  
Since all quantum gates used to control the state of qubits are reversible, inverse operations are possible. The placement of quantum gates is directly related to the depth of the quantum circuit, and many previous studies have been conducted to reduce the total number and depth of quantum gates by reducing the number or optimizing the placement of the quantum gates used in an operation [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. Representative quantum gates include the \(H\) gate, \(X\) gate, CNOT gate, \(Toffoli\) gate, and \(T\) gate, which are presented below:  
\[H=\frac{1}{\sqrt{2}}\bigg{[}\begin{array}{cc}1&1\\ 1&-1\end{array}\bigg{]}\qquad X=\frac{1}{\sqrt{2}}\bigg{[}\begin{array}{cc} 0&1\\ 1&0\end{array}\bigg{]}\qquad CNOT=\left[\begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ 0&0&0&1\\ 0&0&1&0\end{array}\right]\]\[Toffoli=\left[\begin{array}{ccccccccc}1&0&0&0&0&0&0&0\\ 0&1&0&0&0&0&0&0\\ 0&0&1&0&0&0&0&0\\ 0&0&0&1&0&0&0&0\\ 0&0&0&0&1&0&0&0\\ 0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&1\\ 0&0&0&0&0&1&0\end{array}\right]\qquad T=\left[\begin{array}{cc}1&0\\ 0&e^{i\pi/4}\end{array}\right]\]  
As a non-Clifford gate, the \(Toffoli\) gate can be decomposed into lower-level gates. In the proposed quantum circuit, the \(Toffoli\) gate decomposed as shown in Figure 1 was used for \(T\)-depth estimation. The Toffoli gate includes a non-Clifford \(T\) gate, and the steps of the \(T\) gate lead to the \(T\) depth. Minimizing the number of \(T\) gates is still important, as non-Clifford \(T\) gates have a long latency, and their implementation cost far exceeds that of Clifford gates in fault-tolerant implementation [27; 28].
## 3 Results and Discussion
### Antibacterialativity of PVA/BC/C-Ag Composite Membranes in Actual Water  
The tap water used was filtered through a 0.45 \(\upmu\)m filter (the simulated terminal water had passed the previous strainer and adsorption, and the impurity content is very small). Research showed that the tap water had little effect on the antibacterial performance of the composite membrane (after filtering 5 L). The composite membrane demonstrated excellent antibacterial ability when used in a practical simulation with microbially contaminated water. The bacteriostatic rate remained above 88% when filtering less than 3 L of sewage containing \(2.3\times 10^{5}\) CFU/mL of _E. coli_ (Figure 8a). After filtering 2 L of sewage, a composite membrane was removed and placed on sterilized solid medium coated with _E. coli_ and cultured at 37 \({}^{\circ}\)C for 24 h. Under these conditions, the PVA/BC/C-Ag composite membrane had a bacteriostatic rate of up to 90%. By comparison, after treating 4 L of sewage, the antibacterial ability of the composite membrane was reduced to approximately 20% (Figure 8a). This decreased bacteriostatic ability may reflect a lower release efficiency of silver ions as the void network became blocked by residual killed bacteria. To assess this hypothesis, a PVA/BC/C-Ag membrane that had filtered bacterial suspension was magnified 10,000 times under an electron microscope (Figure 7b). The morphology of the membrane had changed and unknown substances were visible on the surface; these may have been adsorbed fragments of bacterial cell membranes and cellular contents. The magnified area (Figure 8b inset) showed the remnants of a ruptured _E. coli_ bacterium, highlighting the antibacterial mechanism of the composite membrane.  
Compared with a water flux of 65,052 Jw/L (m\({}^{2}\cdot\)h) through the pure PVA membrane, the PVA/BC/C-Ag composite membrane had a flux of 18,039 Jw/L (m\({}^{2}\cdot\)h), indicating that it had a smaller pore size. However, experiments indicated that the membrane could become blocked due to its water flux drop after 4 L treatment. After filtering 4 L of contaminated water, the composite membrane had lost much of its antibacterial ability and its remaining bacteriostatic properties were mainly due to its retention capacity. PVA quickly swells and is scrapped during use, while composite membranes are sustainable. Indeed, when the membrane was reused, it still retained more than 90% of its antibacterial ability. In actual drinking water, microbial concentrations are generally much lower than in the simulated contamination experiment performed here, and the PVA/BC/C-Ag composite membrane is likely to perform well for extended periods of time in practical applications. Data from the inhibition zone size (Figure 8a) and rejection rate tests (Figure 8c) underscored the composite membrane's excellent antibacterial properties and effective retention performance.
## 2 Theory
### Application of the Lock-in Amplifier
The bandwidth of the lock-in response around the desired frequency \(f_{m}\) is determined by the length of the time average and is approximately proportional to 1/\(t\), _i.e._, a lock-in period of 1 s results in a contribution to the DC component over a bandwidth of approximately 1 Hz. In the ideal case of an infinite sine wave with an infinite lock-in period, the response is a delta function at frequency \(f_{m}\). In practice, the frequency response is described by \((\sin(x)/x)^{2}\), known as a \(\mathrm{sinc}^{2}\) function. This is important when selecting the frequencies for each of the channels. Optimally, the frequencies should be spaced such that one frequency is at a minimum of the \(\mathrm{sinc}^{2}\) functions of the other channels while keeping them as close as possible to minimize the impacts of variations in receiver gain characteristics. The time averaged \(I\) and \(Q\) components are then added in quadrature to retrieve the magnitude of the desired signal amplitude as,  
\[|A_{\lambda_{m}}|=2\times\sqrt{<I_{\lambda_{m}}>^{2}+<Q_{\lambda_{m}}>^{2}} \tag{5}\]  
**Figure 2.** An illustration of the lock-in amplifier's ability to reject broadband noise and to select frequencies that are outside of the larger 1/_f_ noise component. The factor of 2 is due to the fact that the lock-in method results in 1/2 of the actual amplitude when \(A_{L}\) equals unity, but since the multiplications from Equations (3) and (4) are done digitally, the amplitude of the reference sine wave \(A_{L}\) can be selected arbitrarily. In this example, \(A_{L}\) is selected to be 2, giving the actual amplitude of the mth channel. This is repeated for each of the \(N\) channels. The beauty of the lock-in amplifier is that it is relatively insensitive to broadband noise, since the output of the time-averaged \(I\) and \(Q\) components is only sensitive to noise at the frequency used for the sine and cosine multiplications as illustrated in Figure 2. Frequencies are chosen to avoid the typical \(1/f\) noise and exhibit constant system gain. An analog bandpass filter is used to limit the broadband noise outside of the range of frequencies selected for the N channels, and to avoid aliasing in the ADC.
## 4 Experimental Analysis and Results
### Characteristics of the Study Area  
As outlined in Section 3 \(15,640\) \"high edit\" polygons and polylines from the United Kingdom in Ireland were chosen. These polygons and polylines represent the following features. \"Amenity\" are represented by polygons and usually represent places such as schools, hospitals, _etc_. \"Highway\" represents all types of roads, streets, laneways, highways, _etc_. Polygons marked exclusively with \"Landuse\" tags can represent many different types of polygons such as forest, woodland, tillage, grass, _etc_. \"Natural\" usually indicate lakes, ponds, and other bodies of water but can also represent features such as grassland, scrub, woods, and beaches. Finally \"waterway\" represent canals, rivers, streams, _etc_. These five are chosen as they represent five of the most \"popular\" features in OSM in terms of user contributions and edits. One can easily obtain information about the number of features with these tags in the global OSM database from services such as TagInfo ([47]). In February \(2012\) there were: \(48\) million \"highway\" ways, \(6.37\) million \"waterway\" ways, \(5.8\) million \"natural\" ways, and \(1.1\) million \"amenity\" ways in the OSM database.  
These \(15,640\) geographic features yield a total of \(316,949\) unique versions. \"Highway\" is the dominant feature amongst the five chosen and it represents \(87\%\) of the data. \(994\) of the objects are from the island of Ireland while the remaining \(14,646\) are from the United Kingdom. The earliest contribution is May \(2007\) with the most recent contribution occurring in April \(2011\) when the data was downloaded. There are \(4128\) unique contributors. There are \(58\) contributors who have edited all five chosen \"popular\" feature types.
## 3 Results
### Analysis and Synthesis
#### 3.1.4 OB EMSs
This work responded to this problem by investigating a receding horizon control (RHC)-based EMS together with a predictive model of terminal SOC constraint constructed by the partial least squares method. Simulation results, which compared the proposed EMS to DP results and to a common RB EMS, showed that the proposed predictive model was reasonable and was able to optimally predict the terminal SOC constraint at every receding horizon. In [42], the optimization problem of controlling the transmission ratio and the power-split ratio was solved by using a standard convex quadratic programming (QP) modeling method. A minimum equivalent fuel consumption problem was formulated and converted to a QP problem. The results obtained indicated that the computational efficiency and effectiveness of the QP-based modeling method were high enough to be applied to real-world vehicle controllers and resulted in a good fuel economy compared to DP results. A different approach from the commented above was presented by [PERSON] and [PERSON] [39] that proposed the optimal primary source strategy (OPSS) that was built on two fundamental design principles: threshold changing and load leveling. As compared to conventional RB control strategies, the OPSS was found to deliver significantly improved fuel economy which was remarkably close to that achieved by the optimization-based ECMS, while the design of the OPSS was simple and robust as compared to other optimization-based strategies. This made it more suitable for real-time implementation. Finally, in [45], the real-time implementation of particle swarm optimization (PSO) algorithm was proposed to reduce the fuel consumption of power-split HEVs. A basic RB control was implemented as the base model with an initial EMS, and the simulation result was compared to the manufacturer data. Compared with the RB control, PSO could find the minimum instantaneous fuel consumption at each moment, achieve real-time control and improve the fuel economy of power-split HEVs.
## 1 Introduction
### Using Numerical Methods
Accordingly, the continuum can be decomposed into a set of arbitrarily distributed particles with no connectivity (mesh-free). The integral representation method is adopted for field function approximation. The physical quantities of newly generated particles should be consistent with the corresponding finite elements [43]. The physical variable \(f(x)\) is a function of the three-dimensional vector \(\mathbf{x}\) of newly generated SPH particles, which are determined by Equation (1). \[\left\langle f(x)\right\rangle=\int\limits_{\Omega}f\big{(}x^{\prime}\big{)}W \big{(}\big{|}x-x^{\prime}\big{|}\big{)},h\big{)}dx^{\prime} \tag{1}\]  
where \(W\big{(}\big{|}\mathbf{x}-\mathbf{x}^{\prime}\big{|},\mathbf{h}\big{)}\) is the smoothing Kernel function that depends on the distance between the studied point and the neighborhood particle, \(h\) is the smoothing length which defines the influence area of \(W\). The smooth length of particles can be obtained according to the conservation of particles' mass, as in Equation (2) [44; 45]. \[h^{{}^{\prime}}=(\frac{1}{N_{e}})\sum_{j}^{N_{e}}r_{oj}(\frac{\rho_{oj}}{\rho _{j}})^{1/3} \tag{2}\]  
This hybrid approach, initially applied in entirely different contexts such as simulating bird strikes in aviation [46; 47; 48], has also gained popularity for investigating the interactions between ice and water or between ice and marine structures [49; 50; 51]. At the same time, there appears to be no existing research on the interaction of ice with composite structures using SPH-FEM coupling. Therefore, this work offers an entirely new perspective on the impact of ice on composite structures.
## 3 Data Collection and Elaboration  
This study used a gully erosion inventory map and several geo-environmental variables, such as gullying predisposing factors, to define a gully erosion susceptibility map. The collected data were processed using a combination of ancillary data, remote sensing images, and GIS technologies. The latter allowed the elaboration of thematic layers.
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## 4 Channel Sum-Capacity in LoS and Favorable Propagation Conditions
### Outage Probability  
Therefore, based on the knowledge of the approximated PDFs, it is possible to use the Gamma's cumulative density function (CDF) as a way to analyze the outage probability, \(C_{\text{out}}\), in low- and high-SNR regimes. Results comparing the PDF and CDF of the actual random variable and those of a Gamma random variable are presented and discussed in Section 6. Outage probability is the probability that a certain sum-capacity cannot be reached and is defined as  
\[P_{\text{out.}} = \Pr\{C_{\text{inst.}}\leq C_{\text{out.}}\} \tag{33}\] \[\stackrel{{ M++\infty}}{{\approx}} \frac{1}{\Gamma(\kappa)}\gamma\left(\kappa,\frac{C_{\text{out}}}{ \theta}\right), \tag{34}\]  
where \(\Gamma(.)\) is the gamma function, \(\gamma(.,.)\) is the incomplete gamma function, and \(\kappa\) and \(\theta\) are given by Equations (28) and (29) for the low-SNR regime and by Equations (31) and (32) for the high-SNR regime, respectively.
###### Abstract  
In order to study the effect of high temperature on mechanical properties of rock, two groups of coarse sandstone samples were subjected to the uniaxial compression and triaxial compression test at room temperature of 25 \({}^{\circ}\)C and high temperatures of 100-1000 \({}^{\circ}\)C. The study comes to some conclusions: (1) With the increase of temperature, the longitudinal wave velocity gradually decreases, and the damage factor of temperature gradually increases. (2) For uniaxial compression tests at different temperatures, the high temperature action within 500 \({}^{\circ}\)C has a strengthening effect on the compression strength, and the high temperature effect has a weakening effect on the compression strength when temperatures exceed 500 \({}^{\circ}\)C; so 500 \({}^{\circ}\)C is the temperature threshold. (3) For triaxial compression tests at different temperatures, the rock strength is positively correlated with temperature and confining pressure when the temperature is lower than 800 \({}^{\circ}\)C and the confining pressure is lower than 15 MPa; the rock strength is negatively correlated with temperature and confining pressure when the temperature is over 800 \({}^{\circ}\)C and confining pressure is above 15 MPa, so 800 \({}^{\circ}\)C is the temperature threshold, and 15 MPa is the confining pressure threshold. (4) In the triaxial compression, the actual fracture angle of the sample after high temperature is basically the same as the theoretical calculation value, high temperature has little effect on the actual fracture angle of the sample, and the actual fracture angle is negatively correlated with the confining pressure.  
Keywords:high temperature; uniaxial compression; triaxial compression; strength properties; deformation properties +
Footnote †: journal: Applied Physics Letters
# Design of a Bionic Spider Robot with a Two-Degrees-of-Freedom Leg Structure and Body Frame
## 1 Introduction
Therefore, although the multi-degree-of-freedom structure design endows biomimetic spider robots with the ability to simulate spider movements well, it poses obstacles to their widespread adoption and development. This work aims to tackle these problems by specifically examining and analyzing the limb configuration and body frame structure of biomimetic spider robots. Through the utilization of a composite linkage mechanism and the modification of the leg structure, we have successfully developed a two-degree-of-freedom leg design for the biomimetic spider robot. This design not only improves the robot's ability to move and bend but also streamlines the leg muscles and hydraulic systems, resulting in more effective control of leg motion using a dual-motor system. We have implemented a transmission system that modifies the force exerted on the motors, therefore safeguarding the robot against external forces and minimizing energy consumption caused by resisting torques in different directions. Through the comparative analysis of various gaits, the triangular gait and its corresponding hexagonal body structure are selected as the gait and structure of the bionic spider robot. This structure can ensure that the bionic spider robot can align its center of gravity with its support point, ensuring stability during movement, and at the same time, it can prevent excessive weight on one leg. This research provides a comprehensive approach for creating biomimetic robot systems, and will also provide indispensable resources in the future robot design process to simulate biological systems. The main work completed in this paper is as follows:  
1. A new two-degree-of-freedom bionic spider robot leg mechanism is designed. Compared with the existing structure, it has less control difficulty, lower cost and an optimized force structure. 2. A body frame selection method is proposed to provide a basis for the selection of bionic spider robot frames with different gaits. 3. MATLAB R2023a is used to complete leg motion simulation and center-of-gravity offset analysis of the frame structure, and the results meet the design requirements.
# SAViP: Semantic-Aware Vulnerability Prediction for Binary Programs with Neural Networks
## 3 Vulnerability Prediction Model
### Statistical Features  
Different from the complex semantic features which are hard for a computer to understand, statistical information can directly represent some characteristics of the assembly codes. As shown in Table 1, our statistical features mainly consist of three parts: instructions, operands, and special strings.  
First, for the choice of instructions, V-Fuzz [12] arbitrarily selects 244-dimensional instruction-related features, resulting in a total feature count of 255 dimensions, which greatly increases the difficulty of training the neural networks. To reduce training overhead, improve efficiency, and avoid confusion caused by useless features, we should reduce the number of instructions considered. Inspired by Gemini [40], we count the four most common types of instructions: data transfer instructions, binary arithmetic instructions, logical instructions, and control transfer instructions. These instructions tend to influence memory and easily lead to vulnerabilities. Table 2 shows the details of the instruction list.  
There are eight common operands in total. Each type of operand has a different connotation, and we perform separate statistics on each of them. In addition, we pay attention to five specific strings, \"malloc,\" \"calloc\", \"free\", \"memcpy\" and \"memset\", which are related to specific memory operations. The detection of these strings can help guide the improvement of the ability of the model to detect memory-related vulnerabilities in the evaluations.  
Figure 4: Comparison between static masking and dynamic masking. Dynamic masking can mask more positions than static masking.  
Figure 5: Instruction embedding.  
In summary, we screen 43 instructions, 8 operand types, and 5 special strings, and obtain a 56-dimensional vector of statistical features, and, thus, each basic block can also be represented as a 56-dimensional vector containing statistical features. To verify the effectiveness of reducing the dimensions of statistical features, we conduct a comparative experiment in the experimental part of the paper. The experimental results demonstrate that our new statistical objects can retain most characteristics and reduce time costs.
# Rapid and Precise Zoom Lens Design Based on Voice Coil Motors with Tunnel Magnetoresistance Sensors
## 3 Rapid and Precise Zoom System Design Example
### Driving Force Simulation Analysis  
Whether the VCM can meet the driving demands for rapid zooming primarily depends on whether the peak driving force provided by the VCM exceeds the required force under the maximum acceleration conditions. For the analysis case of Zoom Group 1 in this study, the peak acceleration \(a_{\text{max}}\) is 7.6 m/s\({}^{2}\), as calculated in Section 3.2. The load to be driven during the motion includes the weight of the Zoom Group 1 lens assembly, the positioning sensor, and the weight of the copper coil.  
According to the calculations in Section 3.3.2, the weight of the coil \(m_{\text{c}}\) is 12.2 g. Additionally, the weight of the positioning sensor \(m_{\text{a}}\) is 2.3 g, and the weight of the Zoom Group 1 lens assembly \(m_{x1}\) is 30 g. The peak required driving force (\(F_{\text{r}}\)) is given by Equation (11).  
\[F_{\text{r}}=\left(m_{\text{z1}}+m_{\text{a}}+m_{\text{c}}\right)a_{\text{max }}=0.338\text{ N} \tag{11}\]  
According to the structural parameters of the VCM in Section 3.3.1, the magnetic field strength of the magnets and yoke is simulated in ANSYS. The simulation results are shown in Figure 9. The results indicate that throughout the entire travel range, the minimum magnetic field strength is greater than 0.15 Tesla.  
By substituting the dimensions of the coil and the voltage parameters into Equation (10), we obtain a driving force of \(F_{\text{drive}}\) = 2.076 \(N\) for the VCM. This value exceeds the peak driving force requirement, indicating that the VCM is capable of meeting the system's minimum zoom time requirement.  
Figure 9: Magnetic flux density simulation results of magnet and yoke.