esa-sceva/satcom-chunk-collection huggingface dataset:
# Global Power Dynamics in the Contemporary Space System
## 4 System Actors--Space Powers
### Major Space Actors
#### 4.2.2 People's Republic of China
China views space exploration as a means of enhancing its national development and building international prestige and reputation. The Chinese president's vision for space activities is articulated as follows: 'To explore the cosmos, develop the space industry, and establish China as a space power is our eternal dream' [47]. Since then, China has supported and funded one of the world's most ambitious space programs including the following: building its own permanent space station; building its Mars robotic missions, including Mars sample return, by 2030; developing its Lunar robotic mission program and establishing a Lunar station on the Moon by 2036; developing a human landing system for Mars by 2049; constructing space-based solar power satellites in geosynchronous equatorial orbit (GEO) by 2050; constructing asteroid probes (2029-2034); and developing planetary defense [37]. China also aims to assemble, by 2030, a resilient space support structure with a fleet of satellite constellations that includes both civilian and military satellites [27] (pp. 4-5). The People's Republic of China has explicitly delineated its long-term objectives in the domain of space exploration and utilization. The nation's ambition is to emerge as the preeminent space power by the year 2045, a target that aligns with the centenary of the establishment of the People's Republic in 2049. President [PERSON] has repeatedly emphasized the indispensable role of space in the pursuit of China's global leadership. In order to do this, China has become increasingly engaged in utilizing space as a diplomatic instrument on the global stage. For instance, China is the current leader of the Asia-Pacific Space Cooperation Organization (APSCO) and signed a strategic memorandum of understanding (MOU) with Russia in March 2021 for cooperation in constructing the International Lunar Research Station (ILRS). Moreover, China offers satellite development and launch services to developing countries that have a limited presence in space. Since 2010, China has launched at least 41 satellites for 18 countries. Of these countries, all but four were developing nations in Africa, Asia, and Latin America [47]. In the 21 st century, the People's Republic of China has made significant advancements in the field of space, surpassing Russia in this domain for the first time in 2021. The share of Asia in global expenditures has increased substantially since the early 2000s, primarily driven by China's substantial investments in the sector [46].
# Superconducting Phases in Neutron Star Cores
## 2 The Ginzburg-Landau Formalism
### Entrainment and Local Phase Invariance
With the additional constraint of Galilean invariance (2), the most general form of the entrainment term is found to be \[F_{\rm ent}=\tfrac{1}{2}(h_{1}+h_{2})\bigg{|}\Big{(}\tfrac{m_{\rm n }}{m_{\rm p}}\Big{)}^{1/2}\psi_{\rm n}^{*}\mathbf{\
abla}\psi_{\rm p}+\Big{(}\tfrac{ m_{\rm p}}{m_{\rm n}}\Big{)}^{1/2}\psi_{\rm p}\mathbf{\
abla}\psi_{\rm n}^{*}\Big{|}^{2}\] \[+\tfrac{1}{2}(h_{1}-h_{2})\bigg{|}\Big{(}\tfrac{m_{\rm n}}{m_{\rm p }}\Big{)}^{1/2}\psi_{\rm n}\mathbf{\
abla}\psi_{\rm p}-\Big{(}\tfrac{m_{\rm p}}{m_{ \rm n}}\Big{)}^{1/2}\psi_{\rm p}\mathbf{\
abla}\psi_{\rm n}\Big{|}^{2}\] \[+\tfrac{1}{4}h_{3}\big{|}\mathbf{\
abla}(\psi_{\rm p}\psi_{\rm p}^{*} )\big{|}^{2}+\tfrac{1}{4}h_{4}\big{|}\mathbf{\
abla}(\psi_{\rm n}\psi_{\rm n}^{*}) \big{|}^{2}\,, \tag{4}\]  
which includes four real, independent parameters \(h_{1}\),..., \(h_{4}\). In terms of the superfluid densities and velocities, we can write this as  
\[F_{\rm ent}=h_{1}\bigg{[}\frac{\rho_{\rm n}}{4m_{\rm p}^{2}} \big{|}\mathbf{\
abla}\rho_{\rm p}^{1/2}\Big{|}^{2}+\frac{\rho_{\rm P}}{4m_{\rm n }^{2}}\big{|}\mathbf{\
abla}\rho_{\rm n}^{1/2}\Big{|}^{2}+\frac{\rho_{\rm P}\rho_{ \rm n}}{\hbar^{2}}\big{|}\mathbf{\
abla}\rho_{\rm p}-\mathbf{\
abla}_{\rm n}\big{|}^{2} \bigg{]}\] \[+\frac{h_{2}}{8m_{\rm p}m_{\rm n}}\mathbf{\
abla}\rho_{\rm p}\cdot \mathbf{\
abla}\rho_{\rm n}+\frac{h_{3}}{16m_{\rm p}^{2}}\big{|}\mathbf{\
abla}\rho_{ \rm p}\big{|}^{2}+\frac{h_{4}}{16m_{\rm n}^{2}}\big{|}\mathbf{\
abla}\rho_{\rm n} \big{|}^{2}\,. \tag{5}\]  
So by comparison with Equation (2) the entrainment coefficient is  
\[\rho^{\rm pn}=-\frac{2}{\hbar^{2}}h_{1}\rho_{\rm P}\rho_{\rm n}. \tag{6}\]  
The parameters \(h_{2},h_{3},h_{4}\) only provide density-gradient coupling, and the simplest model of entrainment would therefore set these parameters to zero. On scales much larger than the fluxtube cores the superfluid densities are approximately constant, and so these terms will have negligible effect. However, we will show later that the density-gradient terms play a significant role in the transition between type-I and type-II superconductivity, and therefore must be included in the construction of phase diagrams.
## 2 The Whole Stage Stability of the ANC System with Online SPM
### Mean Weight Behavior  
#### 2.1.1. SPM Filter  
The residual error \(f(n)\) contains three parts: error \(e_{x}(n)\) caused by the ANC filter, error \(e_{v}(n)\) caused by the SPM filter, and background noise \(u(n)\).  
\[f(n)=e_{x}(n)+e_{v}(n)+u(n), \tag{3}\]  
where:  
\[e_{x}(n)=d(n)-y^{\prime}(n), \tag{4}\]  
\[e_{v}(n)=\mathbf{s}^{\mathsf{T}}(n)\mathbf{v}_{L}(n)-\mathbf{\hat{s}}^{ \mathsf{T}}(n)\mathbf{v}_{M}(n). \tag{5}\]  
Combining with assumptions 1-3, the variance of \(f(n)\) can be expressed as:  
\[E(f^{2}(n))=E(e_{x}^{2}(n))+E(e_{v}^{2}(n))+E(u^{2}(n)), \tag{6}\]  
where \(E[\cdot]\) denotes the expectation operator. The Wiener solution \(\mathbf{s}^{*}\) of the secondary path can be obtained by substituting (5) into (6):  
\[\mathbf{s}^{*}=\big{(}E(\mathbf{v}_{M}(n)\mathbf{v}_{M}^{\mathsf{T}}(n)) \big{)}^{-1}E(\mathbf{v}_{M}(n)\mathbf{v}_{L}^{\mathsf{T}}(n))\mathbf{s}(n). \tag{7}\]  
It can be seen \(M\geq L\), \(\mathbf{s}^{*}=\mathbf{s}(n)\). If \(M<L\), a modeling error exists. In practical applications, \(\mathbf{s}(n)\) is an FIR filter. Therefore, reducing or eliminating the modeling error is always possible by making \(M\) a larger value. In order to simplify the analysis and not lose generality, we set \(M=L\) in this paper. For the auxiliary noise vector \(\mathbf{v}^{\mathsf{T}}(n)\), the subscript is no longer used to indicate the length.  
Let the error coefficient vector of the SPM filter be \(\Delta\mathbf{s}(n)=\mathbf{\hat{s}}(n)-\mathbf{s}(n)=\big{[}\Delta s_{L-1}( n),\Delta s_{L-2}(n),\cdots,\Delta s_{0}(n)\big{]}\). Subtract \(\mathbf{s}(n)\) from both sides of Equation (1) and take expectation:  
\[\begin{array}{ll}E(\Delta\mathbf{s}(n+1))&=E(\Delta\mathbf{s}(n))-\mu_{s}E (\mathbf{v}(n)(e_{x}(n)+e_{v}(n)+u(n)))\\ &=(\mathbf{I}-\mu_{s}E(\mathbf{v}(n)\mathbf{v}^{\mathsf{T}}(n)))E(\Delta \mathbf{s}(n))\end{array}, \tag{8}\]  
where \(\mathbf{I}\) denotes an identity matrix with compatible dimensions. It can be seen from (8) that, to ensure convergence to the optimal solution, the condition \(\mu_{s}\leq 2/\lambda_{g,\max}\) must be met. \(\lambda_{g,\max}\) is the largest eigenvalue of the covariance matrix \(E(\mathbf{v}(n)\mathbf{v}^{\mathsf{T}}(n))\). Because \(\mathbf{v}(n)\) is Gaussian white noise, all eigenvalues of the correlation matrix are \(\sigma_{v}^{2}\). Therefore \(\mu_{s}\) must be less than \(2/\sigma_{v}^{2}\). \(\sigma_{v}^{2}\) is also the power of the auxiliary noise. In addition, it can be observed that a larger auxiliary noise power can increase the convergence speed of the SPM filter, but it will reduce the steady-state performance of the ANC system. To balance the two conflicting requirements of fast convergence and low residual noise under steady-state conditions, several auxiliary noise power scheduling strategies have been proposed [15; 18]. A larger auxiliary noise was used in the early stage of the system, and a smaller auxiliary noise was used when the system tended to converge.
## 3 Results and Discussion
### Calorific Value Analysis
#### 3.2.3 Root Mean Square Error (RSME) Average  
To quantitatively compare the RMSE of the sulfur results, the RMSE averages were calculated by dividing the RMSEC and RMSECV by the average of reference calorific  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Measured Value** & **Predicted Value** & **Absolute Error** & **Relative Error** \\
**Number** & **(kcal/kg)** & **(kcal/kg)** & **(kcal/kg)** & **(\%)** \\ \hline UO1 & 6350.000 & 6284.033 & 65.967 & 1.0389 \\ UO2 & 6350.000 & 6279.933 & 70.067 & 1.1034 \\ UB1 & 6376.667 & 6248.880 & 127.787 & 2.0040 \\ UB2 & 6403.333 & 6315.346 & 87.987 & 1.3741 \\ UB3 & 6430.000 & 6434.265 & 4.265 & 0.0663 \\ UB4 & 6456.667 & 6450.932 & 5.735 & 0.0888 \\ UB5 & 6483.333 & 6449.053 & 34.280 & 0.5287 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction results of calorific values for the unknown samples.  
Figure 11: Relative error of calorific values between measured and predicted values for the PLSR of SG smoothing-processed data.  
Figure 12: Prediction plot of calorific value analysis for the unknown samples.  
values. The calculated RMSEC and RMSECV averages for both the original data and the SG smoothing-processed data are indicated in Table 5. These values allow comparisons between properties of unequal size. Therefore, the calculated values for calorific values can be compared with the sulfur concentration measurement results. The RMSEC average for the original data and the RMSECV average value for the original data were calculated as 0.7826 and 0.9941%, respectively. This is slightly higher than the RMSEC average value for the original data. In the case of SG smoothing-processed data, more reduced values were calculated. The RMSEC and RMSECV averages were 0.7109 and 0.8997%, respectively. In the case of sulfur analysis, the RMSEC and RMSECV average values of SG smoothing-processed data were calculated as 7.5097 and 9.0301%, respectively. These values are almost ten times higher than the calorific value results. The sulfur content in coal is so small; therefore, the error value tends to increase even with small changes in sulfur concentration. In the case of the calorific value, it can be seen that it is mainly influenced by carbon, referring to [PERSON]'s formula, since the carbon concentration of the coal samples used in this experiment is from 60 to 75%. As a result of the large proportion of carbon in the coal, the prediction errors of calorific value are lower than in the sulfur concentration analysis.
# Statistical Analysis of Cutting Force and Vibration in Turning X5 CrNi18-10 Steel
###### Abstract
The study [12] calculated the component's RMS values and found that the vibration RMS values are much smaller in  
Figure 1: Direction of forces in the turning operation. the axial direction because the tool is moving in the axial direction; so, the vibration values can be neglected in the axial [13]. \[\mathrm{V_{e}=\sqrt{V_{t}^{2}+V_{r}^{2}}} \tag{2}\]  
Several researchers [14; 15; 16; 17; 18; 19] have studied vibration and its effect on the quality parameters of the machined surface, mainly the surface roughness. Cutting forces and vibration are the result of the machining parameters; proper selection of these parameters can lower the value of cutting forces and vibration during the machining, which affects the quality parameters of the machined surface, mainly the surface roughness. Optimal cutting parameters, including the cutting speed, feed rate, and depth of cut, are crucial for improving the machinability and extending the tool life. The feed rate significantly affects the signal characteristics of cutting forces and vibrations, with a specific frequency band most sensitive to these changes [20]. Increasing the feed rate increases the cutting forces, while the cutting force decreases with increasing speed [21]. Monitoring the cutting phenomenon improves productivity, increases the tool life, and avoids disruption in machining; several scholars [17; 22; 23; 24; 25; 26] have used various methodologies to investigate the correlations between the machining parameters, cutting forces, and vibration. However, no significant conclusion has been drawn that highlights the relationship between the feed rate, cutting forces, and vibration. As a result, understanding the effects of cutting phenomena like cutting forces and vibration in X5 CrNi18-10 steel grade is important to avoid failures in manufacturing processes, particularly those involving machining. This research aims to study the correlation between the feed, cutting forces, and vibration in the turning of X5 CrNi18-10 steel grade using a piezoelectric dynamometer and MPU6050 sensor, and to check the validity of the regression model, analysis of variance (ANOVA) is used. Additionally, this research highlights the effect of machining parameters--the feed, cutting speed, and depth of cut on the cutting forces and vibration using estimated marginal means plots.
## 2 Preliminaries  
### Advanced Encryption Standard (AES)  
AES was first introduced in 1998 [15; 16] and officially adopted as the standard for symmetric encryption algorithms in 2001, replacing the legacy Data Encryption Standard (DES), which supported only 56-bit keys. Due to its high computational efficiency and strong security features, AES has become one of the most widely used encryption algorithms, particularly within the IoT domain. AES encryption process consists of four main stages. First, the plaintext is arranged into a \(4\times 4\) matrix, where each element represents 1 byte. Then, the key matrix is XORed with the plaintext matrix during AddRoundKey(). Next, SubBytes() replace each byte using the S-Box lookup table to introduce nonlinear transformation to enhance security. Next, the ShiftRows() operation shifts the rows of the state matrix to increase data diffusion. Except in the last round, the MixColumns() operation is performed, which combines the four bytes in each column to obscure the relationship between plaintext and ciphertext through matrix multiplication in the Galois Field (GF(2\({}^{8}\))).  
The main difference between AES-128, AES-192, and AES-256 is the length of the encryption key, which impacts the number of encryption rounds; N of 10, 12 or 14 offers a flexible balance between security and performance. Given the resource limitations in IoT devices, AES-128 is widely adopted due to its low computational overhead and power consumption while still offering strong security. In AES-128, the 128-bit key is divided into 16 individual bytes, each ranging from hexadecimal 00 to FF. The encryption process, as illustrated in Figure 1, ensures that even with the shortest key length, AES provides a high level of security and performance, making it particularly well-suited for lightweight IoT systems.  
In 2023, NIST chose ASCON [17] as the next-generation lightweight cryptographic standard for IoT encryption. ASCON was not the fastest, but it was chosen due to its security, good operation on low-power platforms, and inability to be attacked in other ways. ASCON is well-suited for resource-constrained environments. It is based on AEAD and SHA-256, providing efficient protection for short-lived, time-sensitive data commonly found in IoT ecosystems by low-power overheads. However, ASCON is not necessarily more secure than AES-128 in all cases. ASCON is best for short-lived data in low-power environments. AES-128 is still the best choice for securing persistent and sensitive data in many IoT applications. Most microcontrollers have already used well-known cryptographic algorithms like AES, RSA, ECDSA, and SHA-256. This makes AES-128 a better choice than ASCON. This study improves the AES-128 encryption algorithm for use in real-world IoT applications. Because it is compatible with many devices and provides security, improving AES-128 can benefit IoT devices that need to be secure and perform well. By refining the algorithm, we aimed to provide a solution that ensure robust protection without using too many resources.
# All-in-Focus Three-Dimensional Reconstruction Based on Edge Matching for Artificial Compound Eye
## 4 Edge Matching and Propagation Method
### Edge Depth Estimation  
#### 4.2.1 Sub-Image Matching Feature Selection  
The resolution of the rectified sub-image is \(101\times 101\) pixels, as directly matching the adjacent sub-image may cause a mismatch. To obtain accurate depth, the local feature matching-based method inspired by [12] is proposed. However, the resolution of the sub-image is low and the FOV is small, which leads to few feature points by using many robust feature extraction methods. Figure 7 shows six different features (SURF [45], FAST [46], Harris [47], BRISK [48], MSER [49], and Canny [50]) extracted for the rectified sub-image, and Table 1 gives their counts. The sub-image has fewer SURF, FAST, Harris, BRISK, and MSER feature points, whereas Canny feature points reach 554, much more than the other features. Considering the total pixels of the sub-image is \(101\times 101\), the Canny feature is also much less than the pixels, which can largely speed up the computation. Therefore, the edge feature is adopted to obtain the sparse depth.  
In addition to the large number of edge features, another reason for choosing edge features is that all-in-focus image reconstruction is mainly meant for obtaining the object depth information, and the depth estimation algorithms are essential to finding the matching points of the reference image points from the corresponding image. In the original information of the image, the edge points are the most informative features and can be used to describe  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature** & **SURF** & **FAST** & **Harris** & **BRISK** & **MSER** & **Canny** \\ \hline Count & 6 & 1 & 18 & 3 & 32 & 554 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature count in one sub-image  
Figure 6: The image correction experiment setup. (**a**) The original captured image. Left, the image array; right, the enlarged sub-images in the corresponding color boxes (red, yellow, and cyan). (**b**) The experiment setup.  
the magnitude and direction of the image gradient. The multiple consecutive edge points can also be concatenated to carve the pattern and describe the shape of the pattern, etc.  
Then the sparse edge depth of one sub-image can be estimated by matching it with its four neighboring sub-images. The depth estimation diagram is shown in Figure 8.  
Figure 8: The edge depth estimation diagram.  
Figure 7: The extracted features in one sub-image with different methods, the green and blue cross in the figure show these features.
## 4 Discussion
### Limitations and Future Studies  
The present study presented several limitations. First of all, participants were healthy subjects. Future studies should replicate this study in patients with different conditions due to the fact that running on soft surfaces may reduce the risk of injuries [30], including musculoskeletal conditions such as meniscal injuries, tibial stress and fractures and spine alterations [31] as well as forefoot injuries such as metataralsalgia and metataralsal stress fractures [32]. Second, participants were only men; female participants should be analyzed due to the fact that sex has been associated with running plantar pressure differences [33]. Third, participants presented moderate physical activity; sedentary participants should be studied in order to determine the best adaptation to surface hardnesses to establish or standardize a training protocol, which is especially important for patients with plantar skin disorders such as diabetes [34]. Finally, the thickness of all mats should be the same in control experiments. In this study, the thickness of the air chamber mat was 20 cm while both the soft and hard mats presented a thickness of 50 cm. Nevertheless, this thickness difference did not influence the hardness of the air chamber mats as their hardness was established by the air pressure at which it swelled, providing a fixed hardness because they were made of foam rubber [4]. Future studies should analyze spatiotemporal parameters in addition to plantar pressure distribution in superficies with different hardness, including older adults and people with neurological conditions [35; 36].
# QPSK MMW Wireless Communication System Based On p-i-n InGaAs Photomixer
###### Abstract  
Millimeter-wave (MMW) frequencies (30-300 GHz), located between the microwave and infrared (IR), are promising solutions for the increasing demand of high data rate applications, UHD multimedia, HD gaming, security, surveillance, and the emergence of 5G Internet of Things (IoT). In this article, we experimentally demonstrated MMW wireless communication link using InGaAs p-i-n photomixer and commercially available telecom components at W-band (75-110 GHz). The photomixer was excited by two 1.5 \(\upmu\)m lasers via standard telecom fiber optics, to generate frequency difference at W-band. QPSK modulated signal transmitted by the photomixer and received horn antenna integrated MMW mixer and analyzed using a spectrum analyzer and Vector Signal Analyzer (VSA) software.  
coherent detection; millimeter-wave; radio-over-fiber; sub-mm wave; photomixer; photonic THz generation; wireless communication; 5G +
Footnote †: journal: Electronics
# Sensor-Guided Assembly of Segmented Structures with Industrial Robots
## 3 Problem Statement and Solution Approach  
Our example demonstration process consists of three main steps as shown in Figure 2:  
1. Panel Localization and Pick-up: A panel is placed in an arbitrary configuration in a pick-up area. The system detects the panel and identifies its location. The robot then securely and gently picks up the panel.
2. Panel Transport: The robot transports the panel quickly, without excessive vibration, to the assembly area.
3. Panel Placement: The robot accurately and gently places the panel and returns to Step 1.  
The process repeats indefinitely as long as there are panels available for pick-up and there is space in the assembly nest. The user can step through the stages or run the process autonomously. The process can be interrupted by the user or under exception conditions (e.g., excessive force in inadvertent contacts). The user can manually operate the system and then continue the automatic operation by moving to the subsequent or previous steps.  
Our solution implementation involves these steps:  
1. Construct a state machine describing the transition between the steps in the assembly process and the interaction with the operator and the occurrence of exception condition.  
Figure 2: The assembly process consists of three major steps: panel localization and pick-up, panel transport, and panel placement. Vision feedback is used for localization and alignment. Force feedback is used for gentle contact and placement. Motion planning is required for collision-free panel transport.  
For panel pick-up localization, use the overhead camera and determine the grasp points based on the panel location and panel CAD geometry.
3. For panel placement, use the robot wrist mounted cameras for vision-guided alignment 4. For both pick-up and placement, the robot wrist-mounted force/torque sensors are used to avoid excessive contact force and alignment accuracy.
5. Identify the frequency of the fundamental vibration mode of the panel using a high speed motion capture system. Specify the robot motion bandwidth to avoid exciting the dominant panel vibrational mode.  
The overall system integration and coordination is implemented in ROS. Robot motion is implemented through the external command motion mode, which allows command of the desired joint position and reading of the actual joint position at regular intervals (in our case, the External Guided Motion, or EGM, mode for the ABB robot at the 4 ms rate). This feature is used to implement sensor-based motion. The rest of this section will describe the key algorithms used in our implementation.