esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Qubits and Quantum Gates
### Quantum Gates  
According to quantum mechanics postulates [1], the evolution of the quantum systems is allowed only through unitary operators. An arbitrary operator \(A\) is unitary if and only if \(\text{AA}^{\dagger}=A^{\dagger}A=I\), where \(A^{\dagger}=(A^{*})^{T}\). So, quantum gates are unitary operators. So far, quantum computations are performed practically through the quantum circuit model. The proposed competitive algorithm uses quantum negation gate \(X\), CNOT gate and finally the Toffoli-gate. The quantum negation gate, \(X=\left|1\right\rangle\left\langle 0\right|+\left|0\right\rangle\left\langle 1\right|\), negates the state of a qubit. CNOT gate, \(CNOT|u,v\rangle=\left|u,(u+v)\ mod\ 2\right\rangle\), is acting on two qubits together one of them is the control qubit, and the other is the target qubit. This gate negates the state of the target qubit only if the state of the control qubit is \"1\". On the other hand, Toffoli gate (\(T^{2}\)) acts up on three qubits, two of which are used as control qubits, and the third is the target qubit. The whole gate is described as follows [42]:  
\[T^{2}|uvw>=\left|uv,(w+(u.v))mod2\right\rangle,\]  
In other words, Toffoli gate (\(T^{2}\)) negates the third qubit only if the first two qubits are in the state \(\left|11\right\rangle\). The general form of applying the Toffoli gate (\(T^{\prime}\)) on \(j+1\) qubits is defined as:  
\[T^{j}|u_{1}u_{2}...u_{j}u_{j+1}>=|u_{1}u_{2}...u_{j},(u_{j+1}+\prod_{p=1}^{j}u _{p})mod2\rangle, \tag{1}\]  
where the superscript \(j\) represents the number of control qubits. Toffoli (\(T^{j}\)) gate negates the indexed qubit \(u_{j+1}\) only if the first \(j\) qubits are in the state \(\left|11...1\right\rangle\). These gates will be used to propose the quantum classification algorithm based on competitive neural network (see Section 5).
## 2 Materials and Methods
### Design Methodology  
Figure 7 depicts the flow chart of the design methodology of the proposed antenna. Initially, a quarter-wave rectangular monopole antenna was designed. Various parameters of monopole, including length and width of the radiator, feedline, and truncated ground plane, were optimized to get maximum bandwidth. Afterwards, lower corners of the monopole radiator were truncated by using an equilateral triangle such that the hypotenuse of the triangle is \(P_{X}\).  
The hypotenuse of the triangular slot and width of the radiator was further optimized to get the ultra-wide bandwidth. The resultant antenna shows an impedance bandwidth of 62.8%. In the next step, two triangular slots were etched from the radiator to achieve two notch bands from an already designed ultra-wideband antenna. The length and width slot along with separation between them were well-tuned to achieve the desire notch bands. The resultant antenna exhibits a tri-band operational mode along with two notch bands where the value of \(S_{11}\) is greater than \(-2\) dB. Finally, two RF-PIN diodes were inserted at the center of the slots, such that when the diode is in ON-state the slot becomes inactive  
Figure 7: Flow chart of the design methodology of the proposed antenna.  
while in OFF-state the slot becomes active and a notched band is achieved. Biasing circuits were designed at the backside of the antenna to minimize the effects of basing components and wires. Final tuning of various parameters was performed due to the addition of diodes, capacitors, and inductors to achieve the desired frequency reconfigurable antenna having dual-band, tri-band, and wideband operational mode.
# Tuberculosis Vaccines: An Update of Recent and Ongoing Clinical Trials
## 3 TB Novel Vaccines
### TB Subunit Vaccines
#### 3.1.6 AEC/BC02  
AEC/BC02 represents another promising vaccine candidate. It adds a new adjuvant system, BC02, based on BCG-derived CpG and aluminium salt, to the Ag85b antigen and the ESAT-6/CFP-10 (EC) fusion protein [19]. Previous studies have demonstrated the safety of BC02, and have observed an increase in both antigen-specific IL-12 secretion by peritoneal macrophages, and the number of antigen-specific T cells that release IFN-\(\gamma\)[19]. An investigation in animal models re-emphasized the induction of a strong cellular immune response by the vaccine, but found it did not protect against _M. tuberculosis_ when used as a pre-exposure vaccine [19]. In the latent infection animal model, however, the vaccine was found to successfully control the reactivation of _M. tuberculosis_[19]. A recently completed phase I clinical trial assessed the degree of immunity provided by the vaccine and also its safety profile (ClinicalTrials.gov: NCT03026972), while a further phase Ib clinical trial is currently active (ClinicalTrials.gov: NCT04239313).
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 5 Simulation Results
Here, the steering vector \(\mathbf{a}_{BS}(\vartheta_{1},\psi_{1})\) is calculated as \(\mathbf{a}_{BS}(\vartheta_{1},\psi_{1})=[1,\ldots,e^{\frac{2\pi(M-1)d^{B}}{2c}} \cos\left(\vartheta_{1}\right)\cos(\psi_{1})]\), where the \(d^{B}\) is the distance separating BS adjacent antennas; we set \(2d^{B}=\lambda_{c}\), and \(\cos(\vartheta_{1})\cos(\vartheta_{1})=(x_{l}-x_{B})/d_{g}^{l}\), where \(d_{k}^{l}\) is the distance between the \(l\)-th IRS and the BS. Similarly, the steering vector \(\mathbf{a}_{IRS}(\vartheta_{1J},\psi_{1J})\) can be calculated, in which \(\sin\left(\vartheta_{1J}\right)\cos(\psi_{1J})=(y_{B}-y_{l})/d_{g}^{l}\), and \(\sin\left(\psi_{1J}\right)=(z_{B}-z_{l})/d_{g}^{l}\). Note that channel reciprocity is assumed between downlink and uplink channels. Other simulation parameters include noise power \(\sigma_{k}^{2}=-80\) dBm, AO algorithm threshold \(\epsilon=10^{-1}\), and 1000 random vectors in the Gaussian randomization. To demonstrate the effectiveness of our proposed algorithms, we compare our algorithms with the benchmark schemes. The considered benchmark schemes and scenarios are as follows. * Without IRSs: no IRSs are deployed in the system, i.e., solving \((P1)\) with \(L=0\). * Random Phases: all IRS elements are set to be random phases and then we optimize \(\mathbf{W}\) by solving \((P2)\). * Iterative Approach: iteratively solving \(P2\) by random phases and, through iterations, update with variables, leading to minimum transmit power until some criteria are achieved. * Alternating Optimization: \((P1)\) is solved in conventional alternating manner until convergence is achieved [5; 14]. * AO Heuristic: \((P1)\) is solved by optimized near-optimum solutions obtained through the AO approach [38]. Simulation results are generated using the MATLAB environment R2021b. Moreover, the ML and DRL toolboxes, the CVX package 2.2 version, and the SeDuMi solver are used.
# Detecting Malicious Devices in IPSEC Traffic with IPv4 Steganography
## 2 Network Steganography
### Qualitative Measures  
All steganographic methods can be described using basic quality measures. They are often represented using a geometric figure, such as an equilateral triangle (Figure 4) [53].  
This signifies that fundamental quality measures like bandwidth, robustness, and undetectability are interdependent. They can be described as follows:  
* Bandwidth--this is the capacity of the steganography method. Bandwidth is frequently expressed in bits per second or bits per packet. It refers to the maximum data transfer capacity per time unit or one packet accordingly.
* Robustness--this is the feature that determines the degree to which a stegangram can withstand changes without compromising the integrity of the hidden data.
* Undetectability--this is defined as the inability to localize steganographic data in network protocol. Analysis of the statistical characteristics of network packets is frequently used to detect it.  
Therefore, improving one of these metrics typically causes the others to deteriorate. For instance, sacrificing some of the available bandwidth can be necessary to increase the concealment of information. The ideal steganographic method should have high bandwidth and be robust and undetectable. However, creating practical and efficient steganography techniques depends on striking the ideal balance.
# Measurement and Analysis of Vibration Levels for Truck Transport Environment in Korea
## 3 Results and Discussion
### Transport Vibration Level as a Function of Pavement Type and Travel Speed
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Kinds of Truck**} & \multicolumn{2}{c}{**Pavement Type**} & \multicolumn{2}{c}{**Travel Speed**} \\ \cline{2-5}  & **Concrete-Surfaced** & **Asphalt-Surfaced** & **30 \(\pm\) 10 km/h** & **60 \(\pm\) 10 km/h** & **90 \(\pm\) 10 km/h** \\ \hline Leaf-spring 5-ton truck & 0.2341 & 0.1924 & 0.1105 & 0.1651 & 0.2329 \\ Air-ride 5-ton truck & 0.1171 & 0.0873 & 0.0472 & 0.0836 & 0.1199 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall rms G values of vertical PSD profiles according to pavement type and travel speed in the frequency range of 1–250 Hz.
# Physical Layer Security for Military IoT Links Using MIMO-Beamforming at 60 GHz
## 2 MIMO-Beamforming Techniques for PHY Security
### Adaptive Tuning of Radios for Secure IoT Links  
The adaptive tuning of radio links between the TX and RX can be conducted either in a pseudo-random or deterministic manner using banks of phased array setup shown in Figure 2. The frequency switching block will select a particular sub-band in the 60 GHz frequency to be used at any time. For pseudo-random switching, the goal is to maximize PHY secrecy by changing the frequency of the communication link between the TX and RX several times during the communication, in a pseudo-random manner understood by both the TX and legitimate RX. Thus, if a third-party device were to eavesdrop on a particular frequency, it would only detect a portion of the message, which could also be encrypted. By minimizing the length of time that the TX and RX operate over a particular sub-band, PHY security can be maximized for IoT links. This approach will rely on the implementation of appropriate coding schemes to combat lossy links that would be encountered in the 60 GHz band. For the deterministic switching approach, the goal is to tune the radio link over a subset of the sub-band that maximizes a defined secrecy function, \(SF(f_{0}\cdot\cdot\cdot f_{k})\).
# Superparamagnetic Fe/Au Nanoparticles and Their Feasibility for Magnetic Hyperthermia
## 2 Materials and Methods
### Material Characterization and Methodology  
The crystal structure of Fe/Au core/shell particles was studied by X-ray diffraction [P-Analytical X'PERT MPD instrument]. Particle morphology was studied by scanning electron microscopy [FESEM JEOL6340 electron microscope] and transmission electron microscopy [H.RTEM] [JEOL-JEM-1230]. Vibrating sample magnetometry (V.S.M.) (Quantum Design, 3T Versalab) was used to study the magnetic properties. Feasibility for hyperthermia of 5 mg Fe/Au nanoparticles dispersed in 1 mL of water was measured using G2-D5 Series Multi-mode 1500W Driver from Nanoscale Biomagnetics. The sample solution was placed into the G2-D5 coil, where the alternating magnetic field amplitude and frequency can be applied. The induced temperature of the solution was detected and recorded using a fiber-optic sensor.
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 3 Experiments and Analysis
### Experiment Result  
#### 3.2.1 Experiment Results on AeroRIT Dataset  
Figure 7 shows the visually comparison of different methods on the AeroRIT test set. Overall, the classification map obtained through CTAFNet is highly consistent with the ground truth, and there are few misclassification phenomena. The classification map generated by CTAFNet is the smoothest, while ENLFCN and SS3 FCN have more salt and pepper noise. As can be seen in the enlarged images, the CTAFNet correctly extracted the racetrack and effectively preserved shapes. The car boundaries obtained from CTAFNet and SS3 FCN are clearer than that from the other methods, whereas the object edges generated by CTAFNet are the closest to the ground truth.  
Table 5 shows the experimental results of different methods in the AeroRIT test set. The experimental results demonstrate that the proposed CTAFNet method achieves the best performance, with 95.07% pixel accuracy and 81.41% mIoU. Specifically, CTAFNet proposed in this paper is 0.36% higher with regards to pixel accuracy and 1.69% mIoU with regard to the second-best model SS3 FCN, while SegFormer has the worst performance, with 88.51% pixel accuracy and 57.32% mIoU. CTAFNet achieved the best score in four classes of Buildings, Roads, Water and Cars, which is 1.10%, 0.71%, 1.53%, and 1.20% higher than the second place, respectively.
## 3 Optimization Techniques
### Hybrid CSAOA  
A prominent algorithm that uses greedy search to continuously increase the fitness function in each iteration has a major impact on the proposed hybrid CSAOA [52; 55]. Through the process of substituting the disparity between the upper and lower limits of variables with the optimum solution that is generated from the current iteration of the solution set, Equation (17) of AOA is changed. A representation of the alteration may be seen in Equation (20) below.  
Figure 1: Proposed AOA flow diagram [55].  
formity of the various methods may be assessed by generating a boxplot over several iterations, which visually displays the range of ideal values achieved in each iteration. A boxplot displays the highest and lowest values as cross symbols at the top and bottom, respectively. The rectangular box represents the range where 50% of the data are located. It is obvious how the suggested methods prevent users from becoming stuck in local optimums and how the distribution of optimal values acquired over runs may be viewed.  
Figure 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Function & Dim & Range & \(f_{min}\) \\ \hline \(f_{1}(x)=\sum\limits_{i=1}^{n}x_{i}^{2}\) & 30 & \([-100,100]\) & 0 \\ \hline \(f_{2}(x)=\sum\limits_{i=1}^{n}|x_{i}|+\prod_{i=1}^{n}|x_{i}|\) & 30 & \([-10,10]\) & 0 \\ \hline \(f_{3}(x)=\sum\limits_{i=1}^{n}-x_{i}\sin\left(\sqrt{|x_{i}|}\right)\) & 30 & \([-500,500]\) & \(-418.9829\times 5\) \\ \hline \(f_{4}(x)=-20\exp\left(-0.2\sqrt{\frac{1}{n}\sum_{i=1}^{n}x_{i}^{2}}\right)-\) & 30 & \([-32,32]\) & 0 \\ \(\exp\left(\frac{1}{n}\sum_{i=1}^{n}\cos(2\Pi x_{i})\right)+20+e\) & & & \\ \hline \(f_{5}(x)=-\sum\limits_{i=1}^{5}\left[(X-a_{i})(X-a_{i})^{T}+c_{i}\right]^{-1}\) & 30 & \([0,10]\) & \(-10.1532\) & Fixed Dimension \\ \(f_{6}(x)=-\sum\limits_{i=1}^{10}\left[(X-a_{i})(X-a_{i})^{T}+c_{i}\right]^{-1}\) & 30 & \([0,10]\) & \(-10.5363\) & Functions \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the benchmark functions [54].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Function & & F1 & & F2 & \\ \hline Algorithm & & CSA & AOA & CSAOA & CSA & AOA & CSAOA \\ \hline Best & Unimodal benchmark & 82.18 & 117.14 & 0.0096 & 4.19 & 3.58 & 0.01 \\ \hline Worst & functions & 332.1716 & 1061.23 & 0.14 & 9.00 & 14.25 & 0.07 \\ \hline Mean & & 150.3337 & 409.08 & 0.03 & 6.02 & 6.60 & 0.04 \\ \hline SD & & 48.6507 & 246.52 & 0.02 & 1.13 & 2.50 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical study of benchmark function results using “CSA”, “AOA”, and “CSAOA”.  
Figure 2: Benchmark function statistical analysis boxplot and cost convergence characteristics (**a**) F1, (**b**) F2, (**c**) F3, (**d**) F4, (**e**) F5, and (**f**) F6 [55].
## 4 Experiment and Results  
### Experiment Configuration and Preparation  
To avoid overly ideal experimental results obtained by the injection method [19], the irradiation method is adopted in this study. Meanwhile, because of the weakness of the navigation system signal and the susceptibility to interference, the near field radiation that is produced by cabinet leakage could remarkably impact the working of the navigation receiver. Hence, except the signal transmitting antenna, receiving antenna and interference antenna, others including the navigation signal simulator, interference signal generator and navigation receiver are placed outside the shielding room. The experiment environment configuration is shown in Figure 3. The specifications and models of the main components in the experimental system are illustrated in Table 2. The signal intensity of the navigation simulator is set by using simulator control software. The working signal is emitted via a broadband horn antenna. An interference signal generator can generate a single-frequency interference signal, which is amplified by 35 dB power amplifier and generates a radiation field through a broadband horn antenna. The special circular polarization receiving antenna for the navigation receiver is used to receive the working signal and interference signal of the system at the same time. The positioning altitude of the receiver and the C/N0 variation of each satellite can be observed through the receiver software. The distance between the transmitting antenna and receiving antenna should be greater than 3 m. The position and angle of each antenna is adjusted so that the navigation receiver works in the most sensitive state. The relative position of each antenna should not be moved. In the experiment, nine satellites of the navigation signal simulator are turned on, and then the output power should be set properly. Next, the navigation receiver is turned on so that the system is in a positioning state. Furthermore, the jamming frequency and power of the signal generator are adjusted to interfere the normal working of the navigation receiver with electromagnetic radiation.  
Figure 2: The process of C/N0 recovery when the interference frequency offset is 9 MHz.
# Numerical Investigation of the Collapse of a Steel Truss Roof and a Probable Reason of Failure
## 4 Results and Discussion
### Global Analysis with Only Mechanical Loads  
The displacements and the stresses occurring in the truss roof system were calculated by using the finite element analysis software. Figure 11 shows the vertical displacement (in meters) under the considered loads. The largest displacements of about 4.7 cm occurred in blocks A, C, and E, where the gap between the columns was the largest (28 m), as was expected. It should be noted here that due to the positive direction of the axis, which is upwards, blue colours are around zero and red is the largest in terms of absolute values. The region of the larger resultant displacements is not where the real damage happened. The deformations of the column structures are so small that they do not suggest any safety issues. The analyses demonstrate clearly that the system is safe and that the results are consistent with the results obtained by the engineering bureau.  
Considering the stress analysis, the equivalent stresses using the von Mises criteria were calculated for all the structures. Figure 12 gives the von Mises stresses in the roof structure (in Pa). It can be seen that all stresses of the roof structure together with the columns are within the safety limits. The collapsed regions are also within the safety limits. As well as the displacements, the stresses occurring in the column structures are well below the safety limits. Although exaggerated loads are applied on the roof structure, the stresses and displacements in the beams and rods show that the structure would not fail under these combinations of excessive loads.  
The greatest stresses occur at these \"U\" shaped connections shown in Figure 13; however, the displacements are not excessive. It is important to note that this connection structure does not exist in the real application. These connections are used in the CAD/FE model in order not  
Figure 11: Vertical displacement of the steel-reinforced concrete column system.  
Figure 12: Equivalent von Mises stress distribution of the steel-reinforced concrete columns.  
to distort the dimensions of the truss structure. In reality, there are walls with certain thicknesses whereas the FE model used beam elements. To connect the trusses with the supports, such a \"dummy\" structure was used. Therefore, the stress values that were read at these connections were disregarded.
## 3 Case Study: Lishanyuan Landslide
### Overview of Landslide Monitoring  
The monitoring equipment was deployed on 15 April 2021 (Figure 3), and the automated monitoring of the landslide began. Two GNSS monitoring stations, named LISHAN-DB01 (DB01) and LISHAN-DB02 (DB02), were deployed on the main slip profile of the landslide, and one GNSS base station was deployed on the lower side of the road. The automated monitoring system received the first monitoring data at 17:00 on 15 April. The default collection interval of the GNSS monitoring station was 1 h, but this was adjusted to 30 min, due to the obvious acceleration of the landslide on 17 May. A rain gauge, LISHAN-YL01 (YL01), was deployed at the same location as DB02 and its collection interval was set to 20 min. Until 10 o'clock on 2 June 2021, a total of 6031 monitoring data have been collected, including 2814 GNSS monitoring data and 3217 rainfall monitoring data (Figure 4).  
Figure 3: Location of the Lishanyuan landslide and distribution of the monitoring stations.  
As Figure 4 shows, the two monitoring stations have similar deformation trends, but the deformation amplitude of DB02 is significantly larger than that of DB01, which is consistent with the on-site investigation. We take DB02 as an example. The monitoring data first showed a fluctuation of 10 mm in the first week. Affected by the rainfall on 22 April, the landslide started an acceleration process from 4:00 a.m. on 23 April, and this acceleration process gradually slowed down at 11:00 a.m. on 24 April. A new round of constant deformation processes started at 18:00 a.m. on 25 April. On 17 May, affected by the continuous heavy rainfall, the landslide began a new round of deformation acceleration. This acceleration continued until 12 o'clock on the 20 th, then it turned to deceleration, and entered a new round of constant deformation processes.