esa-sceva/satcom-chunk-collection huggingface dataset:
# Safety Control for Satellite Formation Flying via High-Order Control Barrier Functions
## 3 Safety Control Design
### Quadratic Programming-Based Safety Control  
Based on the high-order control barrier function designed in the above subsection, this subsection introduces how to solve for a safety controller that guarantees forward invariance of the safe set, i.e., ensuring the safety of satellite formation flights. During the design of safety control, it is necessary to ensure the stability of satellite formation flights as much as possible. Currently, various control methods can be utilized to design controllers that guarantee stability, such as traditional control methods like PID control, PD control, sliding mode control, and deep reinforcement learning algorithms. Any of these control methods can be employed to design a baseline controller that ensures the stability of satellite formation flights. In this section, aiming at System (9), the PD control method is adopted to design a controller that guarantees the system stability. The PD controller is formulated as follows.  
\[u_{1} = K_{p1}x_{1}+K_{d1}x_{2},\] \[u_{3} = K_{p2}x_{3}+K_{d2}x_{4},\] \[u_{5} = K_{p3}x_{5}+K_{d3}x_{6},\]  
where \(K_{p1}\), \(K_{d1}\), \(K_{p2}\), \(K_{d2}\), \(K_{p3}\), and \(K_{d3}\) are controller gains to be designed.  
Then, the safety control problem for the satellite formation flying can be solved by solving the following optimization problem.  
\[\min\|u_{safe}-u_{PD}\|\] (10) s.t. \[L_{f}^{2}h_{i}(x)+L_{g}L_{f}h_{i}(x)u_{i}+L_{f}\alpha_{1,i}(H_{0, i}(x))\] \[+\alpha_{2,i}\big{(}\dot{H}_{0,i}(x)+\alpha_{1,i}(H_{0,i}(x))\big{)} \geq 0.\]  
Based on the description of the aforementioned quadratic programming problem, it can be seen that the objective is to make the safety controller \(u_{safe}\) approximate the PDcontroller \(u_{PD}\) as much as possible under the constraints of the high-order control barrier function. In this scenario, the stability of the system can be ensured as much as possible, and when there is a conflict between stability and safety, the solved controller can effectively guarantee the safety of the satellite formation.  
Furthermore, if a baseline controller is not designed, the safety controller can also be solved by introducing constraint conditions of the control barrier function and the control Lyapunov function. To prioritize safety, relaxation factors must be introduced into the constraints of the control Lyapunov function. The specific formulation of the optimization problem is similar to the aforementioned optimization problem and will not be described in detail here.
# Removal of Cochineal Dye Color through Atmospheric Pressure Plasma Discharge Jet
## 2 Materials and Methods
### Analysis of Plasma Composition Using Optical Emission Spectroscopy  
Optical emission spectroscopy was employed to investigate the spectral lines of the primary and secondary reactive species generated during the discharge of an atmospheric-pressure plasma jet (APPJ) [25; 38]. The emission spectrum of the plasma discharge was measured across a broad range of wavelengths, from 200 to 1100 nm, using the ULS4096 CL-RS spectrometer (Avantes, Apeldoorn, The Netherlands). To obtain a higher resolution in the ultraviolet region, the spectral range of 200-550 nm was analyzed using the ULS4096 CL-RS-EVO high-resolution spectrometer (Avantes). Furthermore, a detector with a collimator connected to the spectrometer via an optical fiber was utilized to enhance information collection during the analysis of the spectral lines of the reactive species generated by the APPJ discharge.  
The Kinetics and Efficiency of the Decolorization of Cochineal Dye through the Use of Absorption Spectroscopy  
UV-visible spectroscopy was employed to identify the absorption spectra of the dye solution using a Libra S22 UV-visible spectrophotometer (Biochrom US, Holliston, MA, USA) in the spectral range of 200-1100 nm. To assess the color removal efficiency of cochineal dye during interaction with APPJ plasma, samples were taken between 0 and 10 min for analysis via absorption spectroscopy. The kinetic decoloration behavior was modeled utilizing expression (1), which is consistent with a first-order kinetic model.  
\[C_{t}=C_{0}e^{-kt} \tag{1}\]  
Here, \(C_{t}\) (ppm) is the concentration of the dye after plasma treatment for t min, \(C_{0}\) (ppm) is the concentration of the untreated sample, and \(k\) (min\({}^{-1}\)) is the rate constant.  
The percentage degradation of the cochineal dye was also defined from the absorption measurements using the following equation:  
\[D_{degradation}[\%]=\frac{A_{0}-A}{A_{0}}*100 \tag{2}\]Here, \(A_{0}\) is the absorbance of the dye solution without plasma treatment and \(A\) is the absorbance after plasma treatment at the wavelength of maximum absorption. For cochineal dye, the absorption peak at 496 nm is the visible range [39; 40].
# Development of a 3-DOF Cylindrical Ultrasonic Motor Based on Non-Standard Modes
## 2 Configuration of the Motor
### Operating Principle  
Groups A and B are the two ceramic groups of the bending vibration piezoelectric ceramic set. The longitudinal vibration ceramic group is called group C. The stator uses any two of the modes generated by the three ceramics to synthesize the multi-DOF elliptical motion. Assuming that the longitudinal deformation caused by the bending vibration is neglected, the amplitude and frequency of each excitation voltage are the same, and the excitation of the piezoelectric ceramics produces a tiny vibration.  
High-frequency A.C. voltages \(V_{A}\), \(V_{B}\), and \(V_{C}\) are applied to the three sets of piezoelectric ceramics:  
\[\left\{\begin{array}{l}V_{A}=V\cos(\omega t)\\ V_{B}=V\cos(\omega t-\alpha)\\ V_{C}=V\cos(\omega t-\beta)\end{array}\right., \tag{1}\]  
where \(V\) is the excitation voltage amplitude, \(\omega\) is the frequency, \(t\) is the time, and \(\alpha\) and \(\beta\) are phase angles.  
Taking a mass point \(M\) on the surface of the stator, the vibration displacements of \(M\) in three directions are:  
\[\left\{\begin{array}{l}u_{x}=W_{M}\cos(\omega t)\\ u_{y}=W_{M}\cos(\omega t-\alpha)\\ u_{z}=W_{M1}\cos(\omega t-\beta)\end{array}\right., \tag{2}\]  
where \(W_{M}\) and \(W_{M1}\) are the maximum bending deflection of the vibrations at point \(M\). Since the bending vibrating ceramic sets are in a symmetrical position in the stator, the resulting amplitudes are of the same magnitude.  
The motion mechanism is illustrated by the example of two sets of mixed ceramic vibrations, A and B, where \(u_{z}\) = 0. When \(\alpha\) = \(\pm 90^{\circ}\), the radial and circumferential components \(u_{rM}\), \(u_{\theta M}\) of the displacement resulting from the two-phase drive are:  
\[\left\{\begin{array}{l}u_{rM}=u_{x}\cos\theta+u_{y}\sin\theta\\ u_{\theta M}=u_{y}\cos\theta+u_{x}\sin\theta\end{array}\right., \tag{3}\]  
\[\left(\frac{u_{rM}}{W_{M}}\right)^{2}+\left(\frac{u_{\theta M}}{W_{M}}\right) ^{2}=1, \tag{4}\]  
where \(\theta\) is the angle between \(M\) and the circular line connecting the stator surface to the \(x\)-axis. According to Equation (4), the motion of point \(M\) is projected as a circle in the horizontal plane. The displacement of point \(M\) along the stator axis is:  
\[u_{zM}=r\frac{\partial W_{M}}{\partial z}\cos(\omega t)\cos\theta+r\frac{ \partial W_{M}}{\partial z}\sin(\omega t)\sin\theta, \tag{5}\]  
\[\left(\frac{u_{zM}}{r\frac{\partial W_{M}}{\partial z}}\right)^{2}+\left( \frac{u_{\theta M}}{W_{M}}\right)^{2}=1, \tag{6}\]  
where \(r\) represents the stator radius.  
From Equation (4), the trajectory of the mass point is elliptical perpendicular to the stator end face. The vertex of the elliptical motion is in contact with the rotor. Tangential friction drives the rotor around the \(z\)-axis.  
In the same analysis, when \(u_{x}\) or \(u_{y}\) are 0, the trajectory of point \(M\) is an elliptical motion, and the ball rotor can move around either the \(x\)- or \(y\)-axis.
## 5 Experimental Verification and Analysis
### Model Training and Orientation Recognition  
First, the 38 groups of samples in the difficult grinding direction and the non-difficult grinding direction are collected, and the first 19 groups of samples in each direction type are used as training samples. The time domain characteristic parameters, namely peak value and skewness, are calculated, respectively, for each group of vibration signals to form a 2 \(\times\) 19 feature vector sequence. The time domain characteristic parameters, namely peak value, are calculated, respectively, for each group of sound signals. A 1 \(\times\) 19 feature vector sequence is formed, and the above 19 groups of 3 \(\times\) 19 feature vector sequences are, respectively, input the HMM model in the difficult grinding direction and HMM model in the non-difficult grinding direction as training samples, and the models are trained until all models converge.  
Then, the last 19 groups of samples of each direction type are used as test samples, and the time domain characteristic parameters, namely peak value and skewness, are calculated, respectively, for each group of vibration signals to form a 2 \(\times\) 19 feature vector sequence, the time domain characteristic parameters, namely peak value, are calculated for each group of sound signals to form a 1 \(\times\) 19 feature vector sequence. The above 19 groups of 3 \(\times\) 19 feature vector sequences are used as test samples to input the above-trained HMM model in the difficult grinding direction and the non-difficult grinding direction. The maximum likelihood probability corresponding to the output of the two HMM models is calculated. The orientation type of the model with a high output probability value is regarded as the orientation type of the current grinding tool. The identification of a single acoustic emission sensor and a single vibration sensor are compared with the method in this paper.  
Figure 5: Experimental platform.
# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
## 4 Comparison between Transformations  
Time series with different characteristics will behave differently after transformation. For instance, forecasting accuracy in time series, with positive skewness, non-stationarity, and non-normality, may improve with logarithmic transformation. Furthermore, in time series with large observations or large variance, standardisation can improve the forecasting accuracy. Sampling frequency is another potential factor affecting forecasting accuracy. Time series with high sampling frequency (e.g., hourly or daily) usually have an oscillation frequency close to its noise frequency and consequently show instable and noisy behaviour. On the other hand, time series with larger sampling frequency are smoother. These characteristics of time series may affect forecasting and accuracy as well. As such, to investigate the practical effect of data transformation in SSA forecasting, we should consider \"Sampling Frequency,\" \"Skewness,\" \"Normality,\" and \"Stationarity\" as control factors.  
To observe the effectiveness of data transformation prior to the application of SSA, we may compare the forecasting performance of SSA under different transformations and control factors: firstly, by comparing the Root Mean Squared Forecast Error (RMSFE), and secondly, by employing a nonparametric test to examine the treatment effect (data transformation).
# Strength Prediction Model for Cohesive Soil-Rock Mixture with Rock Content
## 4 Numerical Simulation on Mechanical Properties of Cohesive SRM
### Prediction Method for Shear Strength of Cohesive SRM  
Figure 11 illustrates the shear strength versus axial strain curves for cohesive SRM samples with different P\({}_{\text{BV}}\) values at a confining pressure of 400 kPa. Figure 12 presents a scatter plot illustrating the relationship between P\({}_{\text{BV}}\) and shear strength.  
As shown in Figure 13, based on the scatter plot trend fitting curve, its growth trend follows a logistic function.  
According to Figure 13, the relationship between P\({}_{\text{BV}}\) and shear strength can be expressed by the following formula (Equation (3)):  
\[\tau=A+\frac{B-A}{1+\left(\frac{P_{\text{BV}}}{C}\right)^{D}} \tag{3}\]  
where \(B\) denotes the shear strength when P\({}_{\text{BV}}\) = 0, defined as \(\tau_{0}\), where three linearly independent parameters \(A\), \(C\), and \(D\) exist.  
For the logistic function, the rapidly increasing portion is governed by two parameters, \(C\) and \(D\), where \(D\) plays a dominant role and \(B\) plays a secondary role. Previous studies on SRMs have identified key P\({}_{\text{BV}}\) thresholds for shear strength at 0.2-0.3 and 0.7-0.8 [10]. Therefore, the expected thresholds were approached by controlling parameters \(C\) and \(D\) based on the test results. Thus, parameters \(C\) and \(D\) were determined to be 25 and 2.4,  
Figure 12: Scatter plot of P\({}_{\text{BV}}\) vs. shear strength.  
Figure 13: Fitted graph.  
respectively. The goodness of fit of the fitted curve, R\({}^{2}\) = 0.978, indicates that the fit of the curve is applicable.  
\[\tau=A+\frac{\tau_{0y}-A}{1+\left(\frac{P_{\rm BV}}{2\gamma}\right)^{24}} \tag{4}\]  
where \(A\) is the single unknown variable. Therefore, for a given shear strength of a cohesive SRM at any \(P_{BV}\), the specific relationship between shear strength and P\({}_{\rm BV}\) can be determined from Equation (4).
## 2 Case Report
### Computer-Assisted Surgical Planning  
In order to determine the safe location of the miniplates, cone-beam computed tomography (CBCT; Alphard 3030, Asahi, Inc., Kyoto, Japan) was taken. Diagnostic models and bite registration were taken two weeks before the surgery and were scanned through a desktop model scanner (Freedom UHD(r), Dof Inc., Seoul, Korea). All the saved files were merged into a three-dimensional image for the virtual surgery via a specific program (FaceGide(r), MegaGen Co., Daegu, Korea) [11].  
Afterward, the authors discussed the desired location of the miniplates considering both the orthodontic and the surgical aspects through a virtual simulation, which could be visualized on the monitor. After the final location of the miniplate reached an agreement, the plan was saved for manufacturing the patient-customized guides and patient-customized miniplates (Figure 2) [11].  
After exporting the images as Standard Tessellation Language (STL) files, the actual materials were produced with a rapid prototyping machine (S30 3D printer, Rapid Shape GmbH, Heimsheim, Germany) and a computer numerical control machine (ARDEN, TPS Korea Ltd., Gwangju, Korea). The customized miniplates were made of titanium (Medical Grade 4, S-Tech corp. Tainan, Taiwan). The fabricated guides and miniplates were then sent to the operation room in advance for the sterilization.  
Figure 1: Panoramic radiograph before using the skeletal anchorage. Exfoliation of the upper primary canines and first primary molars on both sides is imminent, resulting in increased mobility.  
Figure 2: The surgical guides and the customized miniplates in the virtual surgery. (**A**) The surgical guides. (**B**) The customized miniplates.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 5 Evaluation
### Improved MUCS-BSAE Algorithm Based on the Path Planned by the \"Square Wave Signal\" Curve Path  
Below, we demonstrate the performance improvement of the improved MUCS-BSAE Algorithm under the \"square wave signal\" curve path. We set the working area as a square region of \(800m\times 800m\) (\(16D\times 16D\)), with three UAVs and a flight speed of \(V=15\) m/s set for each UAV. The UAVs work along the \"square wave signal\" curve path, with the starting coordinate set as \(S=(x_{s}=\frac{5L}{2}=400,y_{s}=-30)\). Figure 16 shows the working routes of the UAVs in this scenario. We represent each area center with a point and use different colored lines to represent the routes of the three UAVs. The final working times of the IMUCS-BSAE Algorithm and the MUCS-BSAE Algorithm were 415.5 s and 423.2 s, respectively, resulting in a 2% increase in work efficiency. We can clearly see that the improved IMUCS-BSAE Algorithm avoids some of the ineffective paths and that the approach is demonstrated to be superior.  
Figure 16: Comparison of improved MUCS-BSAE algorithm enhancement under the “square wave signal” curve path.  
Figure 15: Comparison based on rectangular area.  
We further demonstrate the performance comparison between the IMUCS-BSAE algorithm and the MUCS-BSAE algorithm under different area size working areas. We set the working area of the UAV as a square area with a side length \(SL=(800m=16D,900m=18D,1000m=20D,1100m=22D)\). Figure 17 shows the comparison results. It can be observed that the IMUCS-BSAE algorithm shows further performance improvement over the MUCS-BSAE algorithm.
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 4 Dataset
### Dataset A  
Dataset A comprises data on signal samples from 10 navigation satellites. These signals are observed using an Agilent spectrum analyzer, received using a 13-m antenna and collected using a signal-acquisition device with a 250 MHz sampling rate at the Xi'an Aerospace Base Park of National Time Service Center, Chinese Academy of Sciences. Among the 10 navigation satellites, five are from Japan's Quasi-Zenith Satellite System (QZSS) and the other five are from the Indian Regional Navigation Satellite System (NAVIC). We collected three time navigation signals with a frequency of 1176.45 MHz broadcasted by 10 navigation satellites. The collection interval is 15 min. Figure 6a,b show the spectrums of navigation signals of a QZSS satellite and a NAVIC satellite after denoising and smoothing filtering, respectively. Both spectrums were calculated and plotted using 10\({}^{6}\) data points. The navigation signals broadcasted by five QZSS satellites at a 1227.60 MHz frequency were also collected because we use a broadband signal-acquisition system. However, this did not affect our SEI work, as both signals indicate the same satellite. We constructed three subsets of data using navigation signals collected three times and labeled them as A1, A2, and A3.
## 3 Results  
### Spatial and Temporal Variations in GPP, ET, and WUE  
Regarding the interannual variations of GPP, ET, and WUE in Inner Mongolia from 2001 to 2020 (Figure 2), regional GPP showed a significant increasing trend (6.28 gC m\({}^{-2}\) yr\({}^{-1}\) a\({}^{-1}\), \(p<0.01\)), with a mean of 481.51 gC m\({}^{-2}\) yr\({}^{-1}\). The lowest and highest GPP were observed in 2001 (412.26 gC m\({}^{-2}\) yr\({}^{-1}\)) and 2018 (561.38 gC m\({}^{-2}\) yr\({}^{-1}\)), respectively. ET also showed a significant increasing trend (2.70 mm yr\({}^{-1}\) a\({}^{-1}\), \(p<0.01\)), with a mean of 327.77 mm yr\({}^{-1}\), but its increasing trend was significantly smaller than that of GPP. The lowest and highest ET were observed in 2008 (290.87 mm yr\({}^{-1}\)) and 2015 (371.18 mm yr\({}^{-1}\)), respectively. Under the combined action of GPP and ET, regional WUE also showed a significant increasing trend (0.006 gC m\({}^{-2}\) yr\({}^{-1}\) a\({}^{-1}\), \(p<0.01\)), and annual WUE ranged between 1.16 and 1.60 gC m\({}^{-2}\) mm\({}^{-1}\), with a mean of 1.35 gC m\({}^{-2}\) mm\({}^{-1}\). Its interannual values fluctuated widely from 2001 to 2020, with the lowest and highest values in 2001 (1.16 gC m\({}^{-2}\) mm\({}^{-1}\)) and 2018 (1.60 gC m\({}^{-2}\) mm\({}^{-1}\)), respectively.  
As shown in Figure 3, WUE and GPP exhibited similar spatial patterns, the higher values in the northeast and lower values in the southwest. In contrast, ET exhibited a spatial pattern of decreasing from east to west and south to north. From the spatial distribution of their trends (Figure 3), GPP, ET, and WUE all exhibited an increasing trend. Regarding GPP, approximately 96.39% of the pixels showed an increasing trend, of which, areas with significant increase accounted for 48.89% of all pixels, mainly distributed in the eastern regions and Mu Us sandy land in the southwest. Regarding ET, approximately 87.97% of the pixels showed an increasing trend, and areas with significant increases accounted for 39.91% of the total pixels and appeared in the central and eastern parts of the study area. Nevertheless, 12.03% of the area showed a slight decrease, scattered in the northeast and western parts. The WUE statistics show that the increase and decrease trends accounted for 70% and 30%, of which, areas with significant increase and decrease accounted for approximately 22.35% and 2.63% of all pixels. Areas with significant increases were mainly located in the Horqin sandy land in the southeast and the Mu Us sandy land in the southwest, whereas areas with significant and slight decreases were concentrated in the central part of Xilin Gol league and the Da Hinggan Ling Mountains in the northeast.
## 3 Description of the Discrete-Modulated Underwater CV-QKD
According to the above analysis, the transmittance of seawater channel to quantum signal is \(T_{sea}\) in the case of short distance. On the premise of assuming that the additional noise introduced by the seawater channel is \(\varepsilon\), which mainly contributes to the variation of surrounding environmental factors, the noise added from the seawater channel referred to the channel input can be expressed as \(\chi_{ch}=1/T_{sea}+\varepsilon-1\). **Step 3**: At [PERSON]'s side, the imperfect detector with efficiency \(\eta\) and electric noise \(v_{el}\) is used to measure the received coherent states. The noise introduced by the detector referred to [PERSON]'s input can be denoted as \(\chi_{h}\) in shot-noise units and it satisfies  
\[\chi_{hom}=\left[(1-\eta)+v_{el}\right]/\eta \tag{2}\]  
for homodyne detection, and  
\[\chi_{het}=\left[(2-\eta)+2v_{el}\right]/\eta \tag{3}\]  
for heterodyne detection. The total noise referred to the channel input can be expressed as \(\chi_{tot}=\chi_{ch}+\chi_{h}/T_{sea}\). The EB version of the protocol is shown in Figure 4 and it can be described as follows:  
**Step 1**: [PERSON] prepares two-mode entangled states \(|\Phi_{N}\rangle\) with variance of \(V=1+V_{A}\), which can be defined as  
\[|\Phi_{N}\rangle=\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}|\psi_{k}\rangle|\alpha_{k }\rangle, \tag{4}\]  
where the states  
\[|\psi_{k}\rangle=\frac{1}{\sqrt{N}}\sum_{m=0}^{N-1}e^{i(2k+1)m\pi/N}|\phi  
where  
\[\lambda_{k}=e^{-a^{2}}\sum_{n=0}^{\infty}\frac{(\alpha^{2})^{Nn+k}}{(Nn+k)!}, \tag{7}\]  
and \(|Nn+k\rangle\) denotes the Fock state with \(Nn+k\) photons. Subsequently, she performs the projective measurements \(|\psi_{k}\rangle\langle\psi_{k}|\ (k=0,1,\ldots,N-1)\) on the half of the states (mode \(A\)) to get the variables \(X_{A}\) and \(P_{A}\), thus preparing the coherent state \(\alpha_{k}\) when her measurement gives the result \(k\). The other half of the states (mode \(B_{0}\)) is then sent to [PERSON] through the seawater channel. The quantum state attenuated by the seawater channel can be represented by the mode \(B_{1}\). **Step 2**: At [PERSON]'s side, the electronic noise introduced by the imperfect detector causes the received quantum state to be transformed before measurement, and the transformed quantum state can be represented by the mode \(B\). [PERSON] decides to measure one quadrature with homodyne detector or two quadratures with heterodyne detector to get the variable \(X_{B}\) (\(P_{B}\)) or both \(X_{B}\) and \(P_{B}\) of the mode \(B\), and then decodes the information by the sign of his measurement results. The electronic noise \(v_{el}\) can be modeled by an EPR state of variance \(V_{d}\). For homodyne detection, we have \(V_{dhom}=\eta\ \chi_{hom}/(1-\eta)\), and for heterodyne detection, \(V_{dhet}=(\eta\ \chi_{het}-1)/(1-\eta)\). **Step 3**: [PERSON] sends the absolute value results to [PERSON] through a classical channel, and they perform the post-processing procedures, including reverse reconciliation, privacy amplification and so on to share the final secret key.