esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Principle of Encryption and Decryption
### Process of Encryption  
The multi-image encryption based on filtering diffusion and spherical diffraction is shown in Figure 3. Suppose the original gray images are represented by \(I_{N}\left(x,y\right)\), the proposed method can be described as follows:  
**Step 1**: First, the images are processed by filtering diffusion using the 2D kernel of size \(2\times 2\) individually. We set \(M_{2,2}\) as one, and the other three numbers are randomly generated integers from 0 to 255. In the filtering diffusion process, the leftmost column and the topmost row of the image do not have enough pixels for calculation. Hence, the rightmost and bottommost pixels can be used for expansion [43]. Starting from the upper left corner of the image, each pixel is processed in turn. The processed pixel value can be expressed as  
\[I^{\prime}_{N}(x,y)\ =\left(\sum_{i,j\in[1,2]}M(i,j)I_{N}(i,j)\right)\text{mod }F, \tag{8}\]  
where \(F\) denotes the grayscale level.  
Figure 3: Process of encryption.  
**Step 2**: To get a better encryption effect, the images are scrambled by the chaotic sequence individually. The chaotic sequence is obtained by  
\[x_{n+1}\ =\ \mu x_{n}(1-x_{n}). \tag{9}\]  
The range of \(x_{n}\) is (0, 1) and the range of \(\mu\) is (0, 4).  
**Step 3**: Combining the images into one image using the XOR operation. The superimposed result can be written as  
\[f(x,y)\ =\ I_{1}^{\prime}(x,y)\oplus I_{2}^{\prime}(x,y)\ldots\oplus I_{N}^{ \prime}(x,y). \tag{10}\]  
At the same time, \(P_{N}\) can be obtained by  
\[P_{N}(x,y)\ =\ f(x,y)\oplus I_{N}^{\prime}(x,y), \tag{11}\]  
where \(P_{N}\) is reserved as the private key of each image for decryption.  
**Step 4**: The combined image \(f\) (\(x,y\)) is mapped on the spherical surface with a radius of \(R\) after it is modulated by the random phase mask \(RPM\) (\(x,y\)) \(exp\) [\(iR_{1}\) (\(x,y\))]. \(R_{1}\) is a random array uniformly distributed between 0 and 2\(\pi\). According to the OIP model, the modulated image is transformed into a spherical coordinate. If we use U (\(x,y\)) to represent the complex amplitude after spherical diffraction, the expression can be written as  
\[U(x,y)\ =\ SpD_{R}[f(x,y)RPM(x,y)]. \tag{12}\]  
**Step 5**: To improve the anti-attack ability, we use the improved EMD [45] to decompose complex amplitude. The distribution of complex amplitude can be written as  
\[U\ =\ A\times\exp(i\beta)\ =\ C\times[\exp(iR_{2})+\exp(iPK)], \tag{13}\]  
\[R_{2}\ =\ 2\pi\times\text{rand}(), \tag{14}\]  
where \(A\) is the amplitude and \(\beta\) represents the phase of U, rand() denotes a random function. The ciphertext \(C\) and the private key \(PK\) can be expressed as follows:  
\[C\ =\ 0.5A\times[\cos(\beta-R_{2})]^{-1}, \tag{15}\]  
\[PK\ =\ 2\beta-R_{2}, \tag{16}\]  
where the ciphertext \(C\) and the private key \(PK\) are both real-valued, so that they are convenient for recording and transmission.
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 3 The Static Environment Modeling and Path Planning  
### Simulation Environmental  
In Figure 2a, the simulation environment includes an irregular coastline and two islands (circular and pentagram), the dimension of which is 463 pixels \(\times\) 613 pixels. Each pixel represents 1 m. According to the improved GoodWin ship domain model by [PERSON], when the obstacles are extended by a distance of half the ship's length, the ship can be seen as a particle [31]. Hence, the simulation environment is extended by a distance of half the USV's length, and added to the one-pixel-wide boundary in Figure 2b.  
Moreover, the system removes all the height information in the environment, i.e., the three-dimensional (3D) environment is converted to a two-dimensional (2D) planar environment. According to the above hypothesis, USV can be seen as a particle motion in a 2D planar environment.  
Figure 2: The simulation environment. (**a**) The initial binary map; (**b**) The expanded map.
## 4 All-Silicon Photodetector Structures
### IPA-Based Devices
In particular, the authors measured a responsivity of about 0.8 mA/W at a wavelength of 1,550 nm and a bias voltage of \(-\)100 mV for a 35-\(\upmu\)m-long SPP detector obtained by an Al strip on n-Si; while the dark current was about 6 \(\upmu\)A. Recently, some research groups have published papers on IPA-based NIR silicon bulk photodetectors working at room temperature. [PERSON] _et al._[71] investigated the spectral responsivity of Al-porous silicon Schottky barrier photodetectors in the wavelength range 0.4-1.7 \(\upmu\)m. The structure of the PS photodetector was Al (finger type)/PS/Si/Al (ohmic), and the active area was 18 mm\({}^{2}\). The photodetectors show strong photoresponsivity in both the visible and the infrared bands, especially at 1.55 \(\upmu\)m. The photocurrent can reach 1.8 mA at a reverse bias of 6 V under illumination by a 1.55-\(\upmu\)m, 10-mW laser diode. The corresponding quantum efficiency is 14.4%; this high value comes from a very high surface-area-to-volume ratio, of the order of 200-800 m\({}^{2}\)/cm\({}^{3}\) of porous silicon. The dark current is \(-\)5 \(\upmu\)A at \(-\)10 V. The quantum efficiency _versus_ illumination power under 1.55 \(\upmu\)m semiconductor laser diode illumination with the bias voltage as a parameter is shown in Figure 16. The quantum efficiency is almost a linear function of the illumination power. In addition, the quantum efficiency increases and gradually saturates with reverse-bias voltage. The frequency response was 200 MHz and is limited by the low carrier mobility. More recently [PERSON] _et al._[72,73] presented the realization and the characterization of a resonant cavity enhanced (RCE) photodetector, completely silicon compatible and working at 1.55 \(\upmu\)m (see Figure 17). The resonant cavity is a vertical-to-the-surface Fabry-Perot structure. It is formed by a buried reflector, a top mirror interface and, in the middle, a silicon cavity. The buried reflector is a Bragg mirror, realized by alternating layers of amorphous hydrogenated silicon (a-Si:H) and silicon nitride (Si\({}_{3}\)N\({}_{4}\)). A Schottky metal layer (Cu), working both as active (absorbing) layer and as cavity mirror, is deposited above the silicon layer. The novelty of the device is the idea of enhancing the internal photoemission absorption by the microcavity effect. The room temperature responsivity measurements on the device reveal a peak responsivity of about 2.3 \(\upmu\)A/W and 4.3 \(\upmu\)A/W, respectively, for 0 V and \(-\)10 mV of applied reverse bias for a device having radius of active area of 2 mm [72]. Recently the same authors have improved device performances by reducing the radius of active area down to 40 \(\upmu\)m obtaining a responsivity of 8 \(\upmu\)A/W at \(-\)100 mV. In this device a bandwidth in the GHz range was estimated, too [73].
## 3 Multipath Parameter Estimate Algorithm
### CPSD Calculation  
For a sample set of the input baseband signal \(s_{r}(0),\ldots,s_{r}(N-1)\) with total length of \(N\) points, it can be expressed as:  
\[s_{r}(n)=s(n)+noise(n) \tag{9}\]  
where \(s(n)\) is the clear signal given by (2) and \(noise(n)\) denotes the thermal noise following normal distribution: \(noise(n)\sim N\big{(}0,\sigma^{2}\big{)}\).  
The segmentation and overlap are illustrated in Figure 2. The total signal is first divided into \(M\) segments. The starting points of these segments are \(D\) units apart. Then we will have \((M-1)D+L=N\) and the overlap rate \(r\) is \((1-D/L)\times 100\%\). If we let \(s_{r}^{1}(n)\) denote the first segment with \(n=0,1,\ldots\ L-1\), namely \(s_{r}^{1}(n)=s_{r}(n)\), \(n=0,1,\ldots\ L-1\). Similarly, we obtain \(s_{r}^{2}(n)=s_{r}(n+D)\), and at last \(s_{r}^{M}(n)=s_{r}(n+(M-1)D)\).  
Figure 1: The scheme of the proposed multipath estimation method.  
After segmentation, the samples of each segment will be weighted by a window function \(w(n)\) for the sake of reducing the harmful effects of spectral leakage. Let \(S_{r}(k)\), \(S(k)\) and \(Noise(k)\) be the discrete Fourier transform coefficients of \(s_{r}(n)\), clear signal and system noise at frequency bin \(k\), respectively. The size of this frequency bin is defined by \(f_{s}/L\), where \(f_{s}\) is the system sampling rate. For the \(m\)-th segment, the discrete Fourier transform coefficients can be calculated by:  
\[S_{r}^{m}(k)=S^{m}(k)+Noise^{m}(k) \tag{10}\]  
where:  
\[S^{m}(k)=\frac{1}{L}\sum_{n=0}^{L-1}s^{m}(n)w(n)e^{-j\frac{2\pi}{L}kn} \tag{11}\]  
\[Noise^{m}(k)=\frac{1}{L}\sum_{n=0}^{L-1}noise^{m}(n)w(n)e^{-j\frac{2\pi}{L}kn} \tag{12}\]  
and \(j=\left(-1\right)^{1/2}\). For the \(m\)-th segment \(s_{r}^{m}(n)\), the CPSD is given by:  
\[G_{r}^{m}(k)=\frac{L}{U}S_{r}^{m}(k)S_{nom}^{*}(k) \tag{13}\]  
where \(*\) represents conjugate operation and \(S_{nom}(k)\) is the discrete Fourier transform coefficient of ideal BOC signal with \(L\) points. In order to reduce the effect of window on the total power, the CPSD is further divided by a coefficient \(U\), which is given by:  
\[U=\frac{1}{L}\sum_{n=0}^{L-1}w^{2}(n) \tag{14}\]  
The final CPSD estimate \(G_{r}(k)\) can be calculated by averaging \(G_{r}^{m}(k)\), \(m=1\), \(2\),..., \(M\):  
\[G_{r}(k)=\frac{1}{M}\sum_{m=0}^{M-1}G_{r}^{m}(k) \tag{15}\]  
The CPSD obtained from (15) is less noisy. It effectively enhances the robustness of the multipath parameter estimation in the next subsection. It should be noted that a nonrectangular time window, such as hamming window, is used to reduce spectral leakage by attenuating both ends of each segment. This unequal weighting of the samples increases the uncertainty of the power spectrum estimation considerably. So we employ a certain level of overlap between two adjacent segments to eliminate this unequal weighting, which is helpful to reduce the estimation variance. A overlap rate of 50% could be a reasonable value.  
Figure 2: Explanation of segmentation for the proposed method. The entire signal falls into M segments.
# Strengthening Polymer Concrete with Carbon and Basalt Fibres
## 3 Results and Discussion
### Mechanical Tests of the Samples for Bending Strength  
Based on the experimental work, graphs were made showing the functional dependence of the bending moment and compressive strength on the percentage of the basalt and carbon fibre reinforcement. Figure 6 shows the graph of the dependence of the failure bending moment on the percentage of the fibre reinforcement in polymer concrete beams.  
The results of the analysis of the graph presented in Figure 6 show that, in the range of reinforcement percentage values from 0.1 to 2.0%, a linear dependence of bending strength on the reinforcement percentage is observed for carbon fibre. For basalt fibre, a linear dependence is observed in the range of 0.1 to 1.5% of the reinforcement. Further increase in the reinforcement percentage leads to failure of samples mainly in the compression zone. For samples with the percentage of the carbon fibre reinforcement in the range from 0.1 to 2.0%, failure starts from the tensile zone, indicating that the reinforcement has reached its yield strength [27]. A similar pattern is observed for samples with basalt fibre. When the fibre content ranges from 0.1 to 1.5%, failure starts in the tensile zone. At the same time, in samples with a higher percentage of the fibre reinforcement, failure occurs already in the compression zone. The difference in the fibre content, during a transition of fracture from the tensile zone to the compressive zone, is explained by different properties of basalt and carbon fibres. Carbon fibres have a much higher modulus of elasticity. This allows the fibres to stretch longer without collapsing. Therefore, the maximum hardening for carbon fibre is 73.6% and 35.6% for basalt fibre. For polymer concrete samples, where the percentage of the fibre reinforcement exceeded 4.5-5.0%, brittle failure in the compression zone is observed.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 5 Evaluation Results
### Effect of \(L_{q}\)  
Figure 7 shows the effect of the unit latency \(L_{Q}\) when handling one observation in the relay satellite on the average delay \(\zeta_{D}\). From Figure 7, it can be observed that the average delay of all schemes except GROUND increases as \(L_{Q}\) increases. This is because the queuing delay at the relay satellite increases as \(L_{Q}\) increases. However, because the observation satellites in RNA-OTS maintain a small length of queue in the relay satellite by considering the other observation satellites' actions, its increasing rate is smallest. Note that, because GROUND does not use the relay satellite, the average delay \(\zeta_{D}\) is constant regardless of \(L_{Q}\).
## 5 Performance Evaluation
### Results
#### 5.2.3 Overhead of Computing the Checksum  
We also evaluate the response time _versus_ the number of hops. We consider two cases: (i) low overhead means that remote devices take only a little time for the computing time; and (ii) high overhead means that remote devices take much time for an obvious computing of the time against the network delay.  
Figure 7a shows the results for the first scenario. As we can see, with the increase of the number of hops, the verification time that a verifier takes gradually increases. The network delay of normal nodes is larger than the extra computing time of compromised nodes when the number of hops reaches 12. Therefore, it is difficult for a verifier to distinguish the extra computing time caused by an attack from the network delay.  
Figure 7b shows the response time _versus_ the number of hops for the second scenario. As we can see, the increased number of memory accesses makes the extra computing time of the compromised nodes significantly lager than the network delay. Nonetheless, the total overhead is uncommonly lager, as well.  
Therefore, our proposed scheme can distinguish the extra computing time of compromised nodes from the network delay with a very low overhead.
## 3 Results
### Com-COP Inclination Angle  
The COM-COP inclination angle was not different between limbs, so right and left limb data were combined for both the CON and PD groups. At FDS, the PD group had a COM-COP mean inclination angle of 15.66\({}^{\circ}\) (\(\pm\)standard deviation (SD) = 1.57\({}^{\circ}\)), whereas the CON group had a mean of 16.39\({}^{\circ}\) (\(\pm\)1.73\({}^{\circ}\)). At MS, the PD group had a mean of 9.59\({}^{\circ}\) (\(\pm\)1.35\({}^{\circ}\)), whereas the CON group had a mean of 11.14\({}^{\circ}\) (\(\pm\)1.44\({}^{\circ}\)). At SDS, the PD group had a mean of 17.93\({}^{\circ}\) (\(\pm\)2.45\({}^{\circ}\)), whereas the CON group had a mean of 19.43\({}^{\circ}\) (\(\pm\)3.58\({}^{\circ}\)). There were no statistical differences between groups for the COM-COP inclination angle at FDS (\(p\) = 0.171) and SDS (\(p\) = 0.255). At MS, there was a marginally significant difference (\(p\) = 0.006) between groups; however, there were no significant differences (\(p\) < 0.0028). Both groups demonstrated a similarly shaped COM-COP inclination angle curve throughout the gait cycle (Figure 7). At FDS and SDS, the COM-COP inclination angle increased as both feet were in contact with the ground, whereas the angle decreased at MS.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 5 LOS Velocity Calibration and Uncertainties
### LOS Direction Evaluation  
A two-step statistical analysis of the calibration data allows evaluation of the LOS direction \(\theta_{los}\). The advantage of the described method is that \(\theta_{los}\) values are obtained in the frame of the reference sensor used for wind direction measurement.
## 3 Resource Allocation Problem
### User Pairing Using ILP  
ILP is proposed to perform the user pairing scheme because user pairing is addressed as a discrete problem [18; 23]. The ILP method is a decision-making scheme that can improve decision quality.  
The user pairing problem formulation is described as follows. The first user is firstly selected on each channel as a high channel gain user. The second user is then matched to the same channel as a low channel gain user. In this problem, user pairing begins by selecting the high channel gain user and then choosing a second user with low channel gain in each channel. Let us denote the \((U\times N_{sc})\) gain matrix \(M\). Let \(Z\) be a  
\[Z^{1}\leq 1, \tag{24b}\] \[Z_{ij}=\{0,1\},\ i=1,...,U,\ j=1,\ldots,N_{sc}, \tag{24c}\]  
The newly updated matrix is denoted as the optimal solution \(Z^{H}\). Once the high gain users are selected, the channels are updated with the first user selection. The second objective is to select one user from each channel to minimise the sum of the gains. Similarly, this optimisation problem can be expressed as follows:  
\[\textit{Minimize}\ \sum_{i=1}^{U}\sum_{j=1}^{N_{sc}}M_{ij}Z_{ij}, \tag{25}\]  
\[\text{Subject to}\ \ 1^{T}Z=1, \tag{25a}\] \[Z^{1}\leq 1,\] (25b) \[Z_{ij}=0,\ if\ Z^{H}{}_{ij}=1,\] (25c) \[Z_{ij}=\{0,1\},\ i=1,...,U\,\ j=1,\ldots,N_{sc}, \tag{25d}\]  
The solution to this problem is denoted as \(Z^{L}\). The final selection matrix is \(Z^{*}=Z^{H}+Z^{L}\), which has a pair of users on each channel. This process is encapsulated by a function \(S:\mathbb{Z}\rightarrow\mathbb{R}^{2\times N_{sc}}\). This function performs a mapping from a permutation number \(z\) to the vectors of the sorted gain values in all the channels, concatenated vertically.  
\[S(z)=\left[\begin{array}{cccc}G_{1,1}&G_{1,2}&\ldots&G_{1,N_{sc}}\\ G_{2,1}&G_{2,2}&\ldots&G_{2,N_{sc}}\end{array}\right] \tag{26}\]  
A value \(z\) is given. To obtain the gain value of a particular user \(i\) with either high or low channel gain on a channel \(j\), i.e., \(Gi\), we assess the \((i\text{th},\ j\text{th})\) element of \(S(z)\), denoted \(Z_{ij}(z)\).
# A New Foot Trajectory Planning Method for Legged Robots and Its Application in Hexopod Robots
## 6 Experiment on Physical Robot in Real Environment
### Movement Control Flowchart  
The movement control flowchart based on the above triangle gait is shown in Figure 12. For any leg, the follow situations may happen. When the leg is in the support phase, judging whether this phase has been completed needs to be performed. If not, keep moving along the planned trajectory; if it is performed, this means that the leg is ready for phase changing. When the foot is not in the support phase but already on the ground, this means that the foot touches the ground or obstacles in advance; here, the foot needs to replan its trajectory based on the ground's height information, which is sensed by the designed foot sensing structure. When the foot is not on the ground, one situation is that the leg is in the transfer phase, then the foot moves along the planned trajectory. The other situation is that the leg has already finished the transfer phase but has not touched the ground, this situation happens when the ground's height is lower than planned. Then, a new height \(Z=Z+\Delta Z\) is set, and the foot needs to perform new trajectory planning in the adjustment phase based on \(Z\) information. When all legs are ready for phase changing, the phase is changed, and the robot keeps moving.
# Wheel Hub Defects Image Recognition Base on Zero-Shot Learning
## 5 Experiment and Analysis
### Experiment Results  
For the experiment, a two-layer feed-forward neural network for semantic embedding was employed. For the aPY and WHD-9 data sets, the dimensions of the hidden layer were selected as 1600 and 1200 respectively, and the activation function was ReLU. Image features were acquired by ResNet-101.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Training** & **Testing** & **Attribute** & **No.** \\  & **(Seen)** & **(Unseen)** & **Dimension** & **of Samples** \\ \hline aPY & 20 & 10 & 65 & 15,339 \\ WHD-9 & 6 & 3 & 16 & 6380 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset information.  
The proposed method was compared with previous ones. The first is to complete the baseline model (DEM) [35]. Then the DEM+R model only includes structural matching components using loss functions \(L_{2}\) in the training phase. DEM+RA model includes structural loss components and domain adaptation components using loss functions \(L3\) and \(L4\). The DEM+ARC model includes all strategies: structural matching, domain adaptation, and anti-bias calibration. The parameters of the aPY, WHD-9 dataset (\(\lambda_{r},\ \rho,\ \lambda_{g},\ \gamma\)) were set to (\(10^{-4}\), \(10^{-1}\), \(10^{-1}\), \(1.1\)), and (\(10^{-3}\), \(10^{-1}\), \(10^{-1}\), \(1.1\)), respectively. In Table 3, class-wise accuracy results of the traditional unseen classes (TU), the generalized unseen classes (GU), the generalized seen classes (GS), and the generalized harmonic mean (H) are recorded.  
As shown in Table 3, the proposed method is more effective than previous popular methods, and compared with the baseline model, the harmonic mean of the proposed method was significantly increased. The performance improvement can be owed to the three-step strategy. For both data sets, only using structural matching (DEM+R) yielded better performance than the baseline model, with 22.7% (aPY) and 8.7% (WHD-9) improvements. Additional use of domain adaptation (DEM+RA) showed much better results than DEM+R, increased by 62.3% (aPY), 51.2% (WHD-9), but DEM+RAC with calibration components produced only marginal improvements, 3.5% (aPY), 1.2% (WHD-9). This is because the relational matrix-based component produces class-specific adaptation (DEM+RA) to the semantic embedding of unseen classes, whereas the calibration component is not category-specific, just distinguishing between seen classes and unseen classes, so it is understandable that the effect of improving performance is not obvious.
## 5 Maximum Power Point Tracking
### MPPT Simulation  
In this subsection, the simulation of the proposed MISO circuit applied to a thermoelectric conversion system is used to verify its feasibility based on the power simulation (PSIM) software. Therefore, the simulation environment is constructed by analog devices as shown in Figure 23. From this figure, it can be seen that both thermoelectric modules can be traced to individual maximum power points without need of staggered switching; the first thermoelectric module outputs 18.56 W shown in a green circle, almost corresponding to18.52 W, shown in Table 2; the second thermoelectric module outputs 9.03 W, shown in the purple circle, almost corresponding to 9 W shown in Table 3; both the two thermoelectric modules output 27.23 W shown in the red circle, almost corresponding to 27.51 W, shown in Table 4; the output voltage can be increased to 399.85 V, shown in a blue circle, almost corresponding to 400 V, shown in Table 4.  
Figure 23: Simulation of the proposed MISO circuit applied to a thermoelectric conversion system.  
Figure 22: Flow chart of the three-point-weighting MPPT.