esa-sceva/satcom-chunk-collection huggingface dataset:
# Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels
## 5 Simulation Setup and Results
### PAPR Performance
Figure 9: _Cont._  
The PAPR performances of the 128-subcarrier OFDM systems are shown in Figure 10. It was observed that the PAPR performance of the proposed system was significantly better than the uncoded and BCH encoded OFDM systems for ciphered BCH\((127,8)\) codes--as is shown in Figure 10a,b. However, for other codes, we did not observe an appreciable reduction in PAPR. The reason for this is the relatively large value of \(k\). When the value of \(k\) is 22, 36 and 50, the space of possible key vectors becomes very large as the number of possible key vectors are \(2^{22}\), \(2^{36}\) and \(2^{50}\), respectively. Hence, the problem of finding the best key vector becomes intractable. Figure 9: PAPR performance of the proposed technique for ciphered BCH(63,\(k\)) systems. (**a**) Ciphered BCH(63,7) vs uncoded OFDM, (**b**) Ciphered BCH(63,7) vs original BCH(63,7), (**c**) Ciphered BCH(63,10) vs uncoded OFDM, (**d**) Ciphered BCH(63,10) vs original BCH(63,10), (**e**) Ciphered BCH(63,16) vs uncoded, (**f**) Ciphered BCH(63,16) vs original BCH(63,16), (**g**) Ciphered BCH(63,24) vs uncodedand (**h**) Ciphered BCH(63,24) vs original BCH(63,24). Figure 10: _Cont._
## 4 Case Study  
### Experimentation and Testing Process  
#### 4.1.1 Preparation of Datasets  
The data for the experiments were from the MMI Facial Expression Database. In this experiment, we selected 168 facial expression videos expressing four kinds of cognitive emotion (\"pleasure\", \"doubt\", \"boredom\", and \"frustration\") and 22 contributors (6 females and 16 males) were involved. Figure 6 shows some examples of the facial expression videos selected in this experiment that express the four cognitive emotions. These 168 videos were further divided into 900 video clips (208 \"pleasure\" clips, 232 \"doubt\" clips, 240 \"boredom\" clips, and 220 \"frustration\" clips), as the duration of cognitive emotions was approximately 1-4 s. The video parameters were as follows: frame rate of 25 FPS and 768 \(\times\) 576 pixels.  
For the real-time performance test of the method, we developed a computer-based application based on this method. Then, we invited a volunteer from East Asia to participate in the test. Because the samples used in the previous training model were all from European participants (which may cause inaccurate localization of emotional description points, leading to inaccurate recognition of cognitive emotions), we also collected some video data on the expression of cognitive emotions of East Asians and added them to the previously built training set.
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 3 Results
### Identification of Degradation Zones  
We comprehensively analyzed the trend index of each county in five studied years, and classified the study area into five categories according to the degradation situation. We identified the counties degraded at all the 5 years as type A degradation zones, 4 year, 3 year, 2 year, and 1 year degradation counties as type B, C, D, E, respectively (see Figure 16 and Supplementary Table S1).
# Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
## 2 Study Site and Datasets  
### Study Site  
Raw multi-spectral images obtained from the SDGSAT-1 satellite covering Nanjing, Linyi, Yinchuan and Lanzhou cities in China were used for the verification of multiple CCD image stitching, as shown in Figure 2a. The coverage varies a great deal, ranging from urban  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Methods** & **Key Issues** & **Detailed Contribution** \\ \hline \multirow{3}{*}{Object-Space} & \multirow{3}{*}{Pseudo-Rigorous Model} & 1. Developing a reference plane \\  & & 2. Local correction for large error area \\  & & 3. Segmenting AOI into regular grids \\ \hline \multirow{3}{*}{Image-Space} & \multirow{3}{*}{Line-time Normalization} & 1. Determination of dynamic offset \\  & & 2. Piecewise linear stitching and \\ \cline{1-1}  & & logically adaptive image enhancement \\ \cline{1-1}  & & 3. Integration time adjustment \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main characteristics of the traditional methods.  
areas with residential and commercial buildings to desert and mountainous areas with poor conditions. The elevation of the experimental area ranges from several metres to thousands of metres. The great difference in coverage and terrain offers a varied landscape in terms of geometric complexity.  
Figure 2: Geometric distribution and overviews of experimented datasets. (**a**) Geometric distribution of the experimental dataset (Nos. 1\(\sim\)4 accompanied by red stars indicate the locations of four multispectral images, referred to as Image data 1\(\sim\)Image data 4): (**b**) Image data 1, (**c**) Image data 2, (**d**) Image data 3, (**e**) Image data 4.  
The first image was obtained on 18 November 2021, two weeks after the launch of the SDGSAT-1 satellite. The coverage area in Figure 2b is around Nanjing City, consisting of dense buildings and coastlines. The second image in Figure 2c was obtained on 23 November 2021. The coverage is mainly surround by plain and small hills. The last two images, in Figure 2d,e were obtained in 25 November 2021, and consist of large-scale desert and mountainous areas.
# 3D In Situ Stress Estimation by Inverse Analysis of Tectonic Strains
###### Abstract  
The determination of a 3D engineering-scale in situ stress field is essential in underground rock mechanics and engineering. The inverse analysis method is a useful technique to determine the in situ stress around the zone of interest. This paper presents a new approach with tectonic strains based on traditional stress-based or displacement-based inverse analysis. In this approach, there are only six tectonic strain variables at the boundary to be optimized, which does not need to select the stress or displacement boundary conditions as the traditional inverse analysis. Therefore, the proposed approach has a better clarity. The proposed approach is applied to the determination of the engineering-scale in situ stress of the underground powerhouses of the Three Gorges Project, and the results are compared with those obtained by traditional approaches. The comparison further shows that the proposed method has better accuracy than traditional methods.  
in situ stress; inverse analysis; tectonic strain; underground powerhouses +
Footnote †: journal: Applied Sciences
## 1 Introduction
The investigation presented in this paper aims at medium power IM drives controlled by PTC with machines designed to operate in the delta-winding connection nominally. Our paper shows that if such a drive is completed with a controllable (either mechanical or solid-state) delta-star switch, then in some working points (i.e., below the rated speed and torque), it is beneficial to operate the machine in the star configuration, because both the current ripple and consumed electrical power can be reduced. The presented PTC algorithm with switchable delta-star winding topology is supported by theoretical analysis, simulations in MATLAB/Simulink, and experiments conducted on a 5.5-kW dSpace-controlled IM laboratory drive. The simulations verify the presented space-vector equations of the delta-connected machine and analyze the effects of not respecting the actual winding topology. The experimental results confirm the current ripple reduction and efficiency increase by a comprehensive analysis of the current THD and consumed electrical power in multiple operating points of a star-operated but nominally delta-designed machine. The paper is organized as follows: The first part of Section 2 derives the necessary modifications in the transformation of the measured inverter output currents in the case of a delta-connected machine. The second part of the section emphasizes the differences between the fundamental motor phase voltage vectors created by the inverter for both topologies. Section 3 analyzes the constraints imposed on the flux, torque, and speed control of a machine with a switchable delta-star winding connection and provides information about the PTC algorithm's design. Section 4 is dedicated to simulation and experimental results. The paper is concluded with Section 5 that contains a discussion about the proposed method and achieved results. The symbols used throughout the paper are defined in a list before the Appendix A.
## 4 Inverse Problems
### Iterative Inverse Methods
#### 4.2.1 Target Function  
Most inverse studies implement a single target function for optimization, although optimization of multiple targets is feasible. The target function is often defined in terms of an error between the forward problem output and experimental measurements. Since the target function is defined as an error to be minimized but not exactly reduced to zero in a point-wise fashion, the iterative inverse method can converge to reasonable solutions even if the measured data are scattered and affected by random error.  
The selection of an adequate target function must comply with at least two conditions. First, the target function must be compatible with the solution space of the forward model; otherwise, convergence may never be achieved, e.g., pulse wave velocity is incompatible as a target with CFD models assuming rigid walls. Second, the target function must be representative of all the aspects of the modeled phenomena, e.g., a target function based solely on output flow estimation is not adequate for FSI models since the contribution of  
Figure 6: Data processing pipeline for patient-specific iterative inverse methods. Symbols, \(B\): forward problem inputs, \(E\): forward problem outputs, \(e\): experimental data.  
the elastic behavior of the wall can be miscalculated. In FSI studies, pulse wave velocity or multiple target functions dealing with pressure and flow velocity are more reasonable options [106].
# Ultrafast and Wideband Microwave Photonic Frequency-Hopping Systems: A Review
## 2 Microwave Photonic Frequency-Hopping Systems
### Microwave Photonic Opto-Electronic Oscillator Enabled Frequency-Hopping System
The modulated light is then sent to PM-PSFBG via an optical coupler (OC) such that the light will go through both the principle polarization axes of the PM-PSFBG. The reflected signal is detected by a photodetector and an ultra-narrow (~40 MHz) passbands are observed at the microwave photonic filter. The resultant hopping frequency is determined by the power distribution between the two PM-PSFBG axes (i.e., governed by the control signal, i.e., the hopping code sequence applied to the PolM) and the frequency spacing between the laser wavelength and notches at the MWP filter. Therefore, by changing the laser wavelength, modifying the DC voltage applied to the PolM, and adjusting the light polarization launching to the PM-PSFBG, frequency-hopping RF signal can be generated. Figure 4a,b show two resultant frequency-hopping signals that successfully hop between 17.1 GHz and 15.75 GHz as well as 9.58 GHz and 8.65 GHz, respectively, with 100 ns hopping speed. Although frequency-hopping signal can be generated using MWP filter-based OEO scheme, there are still room for improvements in the following aspects: (1) reduction of the long establishment time of oscillating mode to increase frequency-hopping speed; (2) improvement of signal phase noise that was currently deteriorated due to uncorrelated phase relationship between adjacent oscillation modes; (3) achieving multi-level frequency-hopping signal generation that is currently hindered by mode competition in the OEO. Figure 3: (a) Schematic diagram of an OEO-based frequency-hopping signal-generation scheme. PolM: polarization modulator; OC: optical coupler; PD: photodetector; PA: electrical power amplifier. (b) Operation principle of the OEO (with the courtesy of Dr [PERSON]).
# The Recent Development of the Surgical Treatment for Hepatocellular Carcinoma
## 6 The Impact of HCV Eradication on Surgical Outcomes for HCC  
HCV infection is a major risk factor for the development of HCC in Europe, the United States, and Japan, as well as a risk factor for the progression of liver dysfunction (liver fibrosis and cirrhosis) [75]. It is now clear that elimination of HCV and achievement of sustained virological response (SVR) prevents or delays the progression of fibrosis/cirrhosis, decompensation, and the incidence of primary HCC, leading to improved survival [76]. However, the true impact of SVR on surgical outcomes of HCV related HCC remained controversial. Today HCV can be eliminated in almost all cases, and it is necessary to clarify that at least antiviral treatment has not worsened the surgical outcomes of HCC. Therefore, in a multicenter study, we investigated the impact of SVR before and after hepatectomy on the long-term surgical outcomes of HCV-related HCC after statistically minimizing bias arising from patients' background characteristics [77]. Two hundred ninety-six patients underwent radical hepatectomy for primary HCV related HCC, of which 58 achieved SVR before hepatectomy (Pre-SVR group), 54 achieved SVR after hepatectomy, and 186 without achieving SVR (Non-SVR group). All patients in the pre-SVR group received interferon (IFN) treatment. OS and RFS were significantly better in Pre-SVR group than the Non-SVR group in a propensity-matched cohort. In terms of recurrence, both early intrahepatic metastases and multicentric carcinogenesis were suppressed in the SVR group. The post-SVR group also showed significantly better OS and RFS than the non-SVR group. Furthermore, histopathological evaluation using surgically resected specimens revealed that only the patients with SVR (about 30%) had regression of liver fibrosis. There have been several similar reports, and achievement of SVR before or after hepatectomy by IFN therapy is essential for improving long-term surgical outcomes in patients with HCV-related HCC [78; 79; 80; 81].  
Currently, the majority of treatments for HCV have replaced IFN with direct-acting antivirals (DAA). As data accumulation progresses, it has become clear that DAA treatment as well as IFN treatment associated reduced risk of mortality and HCC [82]. Concern was transiently raised that DAA treatment might increase the risk of HCC recurrence after hepatectomy; however, it is now clear that HCV elimination with DAAs does not have a significant impact on the course of established HCC [83; 84]. On the other hand, whether HCV exclusion by DAA treatment can improve the surgical outcomes of HCC still needs further investigation due to the small number of cases and short follow-up period [85].
# Mdp1 _sciences_
## 2 Methods and Data
### Site Description and Wind Data  
On-site tall tower wind resource data from the development phase of a commercial wind farm in Mexico were used for model construction and validation. All towers at the site were equipped with three pairs of redundant cup anemometers (class I for primary sensors, standard for redundant) placed at 80, 60, and 40 m above ground level. The wind direction was measured at two levels. Data were recorded in 10-min intervals; for each variable the mean, maximum, minimum, and standard deviation were recorded. A subset of three towers was selected for this study. One full year of concurrent information with only minor data gaps was selected to avoid seasonal biases. There were few gaps in general, and those only extend to a few hours in most cases. Apart from missing data, a few stretches of data were eliminated because of poor data quality. Initial quality assurance was conducted in a semi-automatic way using Windographer. Overall data recovery after quality assurance was 99.9 %. Only 80-m wind speed data were used for the purposes of this study.  
The relative location of the three towers used in this study (T2, the target tower, T9 the \"upstream tower\", and the additional tower T3) are shown in Figure 3a; the wind roses are exhibited in Figure 3b.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{3}{c}{**Model Details**} & **RMS [m/s]** \\ \hline KF1 & \(\mathbf{x}_{1}(k+1)=\mathbf{A}_{1}\mathbf{x}_{1}(k)+\mathbf{B}_{1}\begin{bmatrix} \begin{bmatrix}\text{T9}(k)\\ \text{T3}(k)\end{bmatrix}+\mathbf{K}_{1}[\text{T2}(k)-\mathbf{C}\mathbf{x}_{1 }(k)]\end{bmatrix}\) & 0.5534 \\ KF2 & \(\mathbf{x}_{2}(k+1)=\mathbf{A}\mathbf{x}_{2}(k)+\mathbf{B}_{2}\text{T9}(k)+ \mathbf{K}_{2}[\text{T2}(k)-\mathbf{C}\mathbf{x}_{2}(k)]\) & 0.5699 \\ KF3 & \(\mathbf{x}_{3}(k+1)=\mathbf{A}\mathbf{x}_{3}(k)+\mathbf{B}_{3}\text{T3}(k)+ \mathbf{K}_{3}[\text{T2}(k)-\mathbf{C}\mathbf{x}_{3}(k)]\) & 0.5426 \\ KF4 & \(\mathbf{x}_{4}(k+1)=\mathbf{A}\mathbf{x}_{4}(k)+\mathbf{K}_{4}[\text{T2}(k)- \mathbf{C}\mathbf{x}_{4}(k)]\) & 0.7081 \\ KF5 & \(\mathbf{x}_{5}(k+1)=\mathbf{A}_{1}\mathbf{x}_{5}(k)+\mathbf{B}_{51}\begin{bmatrix} \text{T9}(k)\\ \text{T3}(k)\end{bmatrix}+\mathbf{B}_{51}\text{T9}(k-1)+\mathbf{K}_{5}[\text{ T2}(k)-\mathbf{C}\mathbf{x}_{5}(k)]\) & 0.6733 \\ KF6 & \(\mathbf{x}_{6}(k+1)=\mathbf{A}_{1}\mathbf{x}_{6}(k)+\mathbf{B}_{61}\text{T9} (k)+\mathbf{B}_{62}\text{T9}(k-1)+\mathbf{K}_{1}[\text{T2}(k)-\mathbf{C} \mathbf{x}_{6}(k)]\) & 0.7145 \\ \hline \hline \end{tabular}
\end{table}
Table 2: KF model details and performance summary \(\left(N_{p}=2\right)\). T2 is the target tower, and T3 and T9 are additional towers providing exogenous wind speed data.
## 2 Beidou-Binocular Cooperative Navigation Model
### Beidou Navigation Positioning Module
#### 2.1.2 Challenges of Beidou Positioning in Complex Environments  
In complex industrial environments such as substations, the Beidou positioning system faces the following major challenges:  
* Electromagnetic Interference: The high-voltage equipment, transformers, and switch cabinets inside the substation generate strong electromagnetic radiation, which may interfere with the reception of Beidou signals, causing fluctuations in pseudorange measurement values and reducing positioning accuracy.
* Signal Obstruction: Buildings, metal structures, and high-voltage line towers in the substation may block satellite signals, preventing the receiver from obtaining enough satellite signals simultaneously, thus affecting the reliability of positioning computation.
* Multipath Effects: Satellite signals reflected on metal surfaces or walls cause multipath propagation, adding error terms to the pseudorange measurements, especially in environments with complex signal propagation paths.
* Reference Station Layout and Data Transmission Delays: The distribution of reference stations around the substation and the potential delays or packet loss in data transmission between the reference stations and the robot's receiver may affect the timeliness of RTK correction information.
## 6 Conclusions  
In response to the security and stability risks of traditional cryptographic-based certificate identity authentication methods in IoT scenarios, such as low efficiency and large latency, we analyzed the collected CSI data in real environments, which indicated that CSI data can support identity authentication for devices in the scenario. A physical layer identity authentication method based on deep reinforcement learning was explored and proposed; this method was based on the channel state information of the wireless channel to distinguish the identity of the terminal. By designing the deep neural network with reasonable states, actions, rewards, and methods, the proposed scheme had higher authentication accuracy compared to the comparison methods and significantly lower computational requirements compared to traditional authentication methods.  
**Author Contributions:** Conceptualization, Y.L. and P.Z.; Investigation, Y.W. and X.L.; Methodology, P.Z. and H.L.; Project administration, P.Z. and H.J.; Validation, Y.L., Y.W. and H.L.; Writing--original draft, Y.L. and P.Z.; Writing--review and editing, Y.W. and H.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Fundamental Research Funds for the Central Universities (no. 328202206 and 3282023034), the Beijing Natural Science Foundation (no. L192002), and in part by the \"Advanced and sophisticated\" discipline construction project of the universities in Beijing (no. 20210013Z0401), the China National Key R&D Program (no. 2020 YF-B1808000).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design and Implementation of Inverse Kinematics and Motion Monitoring System for 6 DoF Platform
## 2 Inverse Kinematics Design
### Traditional Inverse Kinematics  
Traditional inverse kinematics changes the endpoint coordinate of the crank arm through the rotation angle of the arm [18], which requires adding together the components of the crank mapped to the platform, components of the connecting rod mapped to the platform, and the coordinates of the rotation axis of the drive shaft and then subtracting the sum from the expected upper platform coordinate to calculate its distance from the rod. Because the length of the support arm is fixed, the endpoint coordinate of the crank arm was adjusted by rotating the crank arm until the distance between the lower and upper platform coordinates aligned with the actual length of the support arm (Figure 1).  
As Figure 2 illustrates, when the upper platform coordinate (xp1, yp1, zp1) of the first axis and inverse kinematics were used to calculate the expected rotating position vector (xp1_1, yp1_1, zp1_1), the coordinate corresponding to the lower platform (xb1_1, yb1_1, zb1_1) was moved from (xb1, yb1, zb1) according to the rotation angle of the crank arm. The virtual support arm (\(L1\)) was calculated using (xp1_1, yp1_1, zp1_1) and (xb1, yb1, zb1). If \(L1\) did not equal the actual support arm (\(L\)), the two had different lengths. The M1 motor rotation command is obtained according to the change in the error value as  
\[\theta_{1}(k+1)=\theta_{1}(k)+\gamma(L1(k)-L) \tag{1}\]  
where \(k\) is the number of sampling points, \(\theta_{1}\) is the angle of the M1 motor, and \(\gamma\) is the weighting factor of the iteration. Then, the angle of the M1 motor had to be adjusted until \(L1\) and \(L\) were of the same length. Therefore, the upper platform coordinate (xp1, yp1, zp1)  
Figure 1: Photograph of the motion platform.  
was slowly moved from the virtual support arm (\(L1\)) to \(L^{\prime}\) by controlling the M1 motor. The length of \(L^{\prime}\) was calculated using Equation (1).  
\[L^{\prime}=\sqrt{\left(\text{x}_{\text{p1\_1}}\text{-x}_{\text{b1}}\right)^{2}+ \left(\text{y}_{\text{p1\_1}}\text{-y}_{\text{b1}}\right)^{2}+\left(\text{z}_{ \text{p1\_1}}\text{-z}_{\text{b1}}\right)^{2}} \tag{2}\]  
The software ran Equation (2) until \(L=L^{\prime}\), which indicated that the coordinate of the lower platform (xb1_1, yb1_1, zb1_1) was correct.