esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Models for the RT POD of LEO Satellites
### Processing Strategy  
Dynamic models for the RT POD of the TJU-01 satellite are first introduced. The detailed RT POD strategy is then presented in Tables 2 and 3. This is followed by the setting of parameters relating to the processing noise, as given in Table 4. These parameters are set considering the results of multiple previous experiments.  
The GNSS data processing strategy for the TJU-01 satellite RT POD is presented in Table 3.  
On the basis of the strategy mentioned above, the data processing is outlined in Figure 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Force** & **Model Adopted** \\ \hline Earth gravity model & EIGEN-6C (\(70\times 70\)) [22] \\ N-body disturbance & JPL DE405 \\ Solid tide and pole tide & IERS 2010 [23] \\ Ocean tide & FES2014b [24] \\ Relative effect & IERS 2010 [23] \\ Solar radiation pressure & Macro Model [22] \\ Atmosphere drag & Harris–Priester density model, fixed area, estimating the drag \\ Earth rotation parameter & Predicting EOP from Bulletin A in IERS2010 [23] \\  & Six piecewise periodical terms in the three (along, cross, and \\ Empirical forces & radial) directions, Cr and Sr represent radial periodical \\  & parameters, Ct and St represent that in the along direction, \\  & and Cn, Sn for the cross direction, estimated every 90 min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dynamic model for the RT POD of the TJU-01 satellite.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Model Adopted** \\ \hline Observation model & Carrier-phase and pseudo-range undifferenced \\  & dual-frequency ionospheric-free combination \\ Cut-off elevation & \(1^{\circ}\) \\ Processing arc & 9-day arc \\ Sampling interval & 1 s \\ Terrestrial frame & ITRF2020 [25] \\ Observation weight & \(P=\sin\epsilon/\sigma^{2}\). \(\sigma^{2}\) represents the variance in code or \\  & carrier-phase measurements \\ Receiver clock offset & White noise estimation \\ Receiver ISB & Random walk process \\ Filtering method & Extended Kalman filter \\ Ambiguity parameter & Float solution \\ \hline \hline \end{tabular}
\end{table}
Table 3: GNSS data processing strategy for the TJU-01 satellite RT POD.  
Figure 1: Processing flow chart.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **The Value of Initial** & **The Value of** & **Correlation Time** \\  & **Variance** & **Steady-State Variance** & **Correlation Time** \\ \hline LEO satellite position [m] & 1.0 & - & - \\ LEO satellite velocity [m/s] & 1.0 & - & - \\ GPS receiver clock & 500.0 & 50.0 & 1 s \\ offset [m] & 1.2 & 1 & 1 s \\ \
\begin{tabular}{c} Empirical force acceleration in radial direction [nm/s\({}^{2}\)] \\ \end{tabular} & 100.0 & 200.0 & 1 s \\ Empirical force acceleration in along direction [nm/s\({}^{2}\)] & 400.0 & 800.0 & 1 s \\ Empirical force acceleration in cross direction [nm/s\({}^{2}\)] & 200.0 & 400.0 & 1 s \\
\begin{tabular}{c} Ambiguity parameter \\ \end{tabular} & 0 & 0 & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Setting of parameters relating to processing noise.
# Error Analysis and Calibration Improvement of the Imaging Section in a Mueller Matrix Microscope
## 4 Conclusions  
We reviewed the configuration for the Mueller microscope (including the imaging section), modeled it mathematically, and presented an intuitive and quantitative scheme for error analysis and calibration. Using the Taylor expansion together with an optimized combination of controllable error sources, we established a relationship between the source of the errors and the calculated Mueller matrix elements. The resulting error magnification coefficient matrices can not only be used to investigate the influence of individual elements in the Mueller matrix but are also helpful for calibrating the imaging section in the Mueller matrix microscope. A comparison of the experimental results confirms that the imaging section introduces measurement error in the Mueller matrix elements, specifically in the fourth row and column. The parasitic polarization of the objective lens can be characterized by the polarization parameter, which describes the birefringence. The maximum error can be reduced to 0.01 after calibrating both the polarizing and imaging sections. Thus, the proposed method is expected to be more advantageous than the conventional methods when using the Mueller microscope in the fields of biomedicine, material testing, and other applications that require accurate knowledge of the elements of the fourth row or column of the Mueller matrix. This method should also be helpful for calibrating particular error sources with large error magnification coefficients. This method is limited by the assumption that the systematic errors are small and that the calculation results for different error sources are mutually independent.  
Conceptualization, X.C. and M.L.; Data curation, J.Y.; Funding acquisition, X.C.; Methodology, J.Y. and M.L.; Project administration, X.C.; Software, J.Y.; Supervision, X.C.; Writing--original draft, J.Y.; Writing--review and editing, X.C. All authors have read and agree to the published version of the manuscript.  
This research was supported by grants from the National Natural Science Foundation of China (No. 61527826, No. 51735002), the Shenzhen Science and Technology Innovation Program (No. JCYJ20170412171011187), and the National Key Research and Development Program of China (No. 2017 YFC1403602).  
The authors declare no conflict of interest.
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 1 Introduction
For instance, neural networks and reinforcement learning [15; 16; 17] were used to enhance the learning performance of the force term in the DMP model. The devel  
Figure 1: LfD-based learning for a pick-and-place application. opment of the incremental learning mechanisms for the DMP model received attention as well [18; 19; 20]. However, the previous research has problems of high complexity and computational difficulty. A more detailed analysis is provided in the following Section 2. Based on the above considerations, in this study, an improved DMP model with extended demonstration and incremental learning capabilities is designed. The capabilities are implemented based on a 1D-based topology trajectory segmentation algorithm (1D-SEG), a Gaussian probabilistic roadmap (G-PRM), and a broad learning system (BLS). By constructing an appropriate sampling strategy and loss function, G-PRM can generate an extended demonstration based on the features extracted from the segmented original demonstration via 1D-SEG. This extended demonstration and the force item of the DMP model are fed into the BLS to update the DMP model to store previously learned skills (i.e., incremental learning). Three innovative characteristics of this study are represented below. * Segmentation and extended demonstration: 1D-SEG is combined with G-PRM to generate an extended demonstration by incorporating the features of the original demonstration, so that fewer demonstrations are required to minimise the cost of data collection;
* DMP modelling and incremental learning update: The BLS learns the difference between the extended demonstration and the original demonstration by incrementally increasing the number of network nodes (hereafter referred to as additional enhancement nodes). The force item of the previously constructed DMP model is updated with the results generated by the BLS. * Electric vehicle (EV) battery disassembly cases and an experimental platform were used to verify the effectiveness of the developed approach. Based on the approach, the successful disassembly of nuts and battery cells were achieved. The remainder of this paper is structured as follows. Section 2 reviews the related work from the perspective of the trajectory generation and DMP-based optimisation. The key research gaps of the related work are summarised in Section 2. According to these gaps, segmentation and extended demonstration-based methods (namely 1D-SEG and G-PRM) and BLS-based incremental learning for DMP (namely Bi-DMP) are developed in Section 3. In Section 4, a disassembly experimentation platform based on EV batteries and collaborative robots is presented. The effectiveness of the proposed algorithms was verified by three cases, i.e., pick-and-place, unscrewing nuts, and battery cell removal. A comprehensive discussion is given in Section 5. Finally, Section 6 concludes the research.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Resistance Against Differential Attacks
#### 3.6.2 Number of Pixels Change Rate (NPCR)  
The NPCR metric measures the percentage of the differences between two cipher images that are produced by encrypting two plaintext images that differ in only one pixel, with all other pixels being the same in the two plaintext images [58]. A higher NPCR value indicates greater responsiveness from the algorithm to changes in the plaintext image or key and, thus, better resistance to differential attacks. In other words, a good encryption algorithm should be extremely sensitive to minor changes in the key or the plaintext image to resist such attacks. [PERSON] et al. [59] calculated that the ideal NPCR value is \(\geq\)99.6094%. The NPCR value can be calculated as follows:  
\[NPCR=\sum_{i=1}^{H}\sum_{j=1}^{W}\frac{D(i,j)}{H\times W}\times 100\%, \tag{29}\]where  
\[D(i,j)=\begin{cases}0&\text{if }C_{1}(i,j)=C_{2}(i,j)\\ 1&\text{if }C_{1}(i,j)\
eq C_{2}(i,j)\end{cases}\]  
where \(D(i,j)\) is the sum of the number of different pixels between \(C_{1}\) and \(C_{2}\) at position \((i,j)\). \(D(i,j)\) has a value of 1 when the pixel values of \(C_{1}\) and \(C_{2}\) at position \((i,j)\) are different; otherwise, it has a value of zero.
## 1 Introduction
Consequently, mission management architectures should take into  
Figure 1: Mission life cycle stages. account how to manage unexpected and unforeseen events. Typically, this has been done by delegating the responsibility to adapt to the new situation to the AUV, or to abort the mission [19]. That is because the classical transference of the mission plan was performed by loading the full mission into the vehicle before being launched [20]. However, the advances in underwater communications [21] may enable other possibilities. In particular, it is of interest to know how self-adaptation is considered from the global mission management point of view. Especially in relation to the use of decentralized control patterns [22] that include both the autonomous vehicles and a global management component. The aforementioned improvement in underwater communications also enables other mechanisms for dispatching the mission plan from the global mission management to the AUVs participating in a mission, thus constituting another topic of interest not covered in previous surveys. Finally, the availability of a greater variety of vehicles with different capabilities is increasing. Consequently, the possibility of using different vehicles with different mission management capabilities in the same mission is also increasing. This situation presents an opportunity for the use of virtualization mechanisms in the mission management architectures that can enable the integration of legacy systems with novel ones. In summary, the main contributions for this survey can be described as:  
* An identification of a set of four features of interest that a mission manager for cooperative robotics should take into consideration, specially, but not restricted to underwater operations, namely: \[ \begin{array}{ll}\odot&\text{Mission plan specification.}\\ \odot&\text{Use of self-adaptive models.}\\ \odot&\text{Use of virtualization.}\\ \odot&\text{Mission plan dispatching and execution.}\end{array} \]
* A categorization of the four features identified for allowing the assessment of the selected architectures. * A survey of a selected number of architecture proposals for cooperative robotics, highlighting their most relevant characteristics. * A discussion of the open issues found. * A discussion of possible future works that can be carried out so that the quality of future mission management architectures will be improved. The rest of this paper is organized as follows. Section 2 provides a comprehensive description of the selected features to be compared among the selected mission management architectures. Section 3 provides a description of the selected architectures, paying special attention to the features introduced in Section 2. Section 4 provides a summary comparison of the reviewed architectures, and a discussion of their strengths and weaknesses. Finally, Section 5 covers the conclusions from the survey and introduces some possible future works worth for exploration.
## 2 Precision Seeding Technology
### The Seed-Spacing Consistency-Regulation Technology
Much research has been carried out on the seed metering device, which is reflected in the design of the seed metering device and the improvement in working performance. At the same time, related research has also been carried out on the link between seed guidance and seed dropping. Forming an indispensable link to precision seeding, the research into the regulation technology for a consistent sowing depth is particularly important.
###### Abstract  
Physical activity induces modifications in the concentrations of trace mineral elements. However, studies exploring sex-related differences in manganese (Mn) and molybdenum (Mo) levels among athletes are scarce. Mn and Mo are essentials metals required for a variety of metabolic functions, including those involved in normal human development, the activation of certain metal-loenzymes, energy metabolism, and immune system function. They are important cofactors for a variety of enzymes, including those involved in neurotransmitter synthesis and metabolism. The presence of molybdenum (Mo) is essential for several enzymes, including xanthine oxidase (XO), aldehyde oxidase, sulfite oxidase (SO), and the mitochondrial amidoxine reductase component (mARC). This study aimed to: (a) analyse changes in plasma, urine, erythrocyte, and platelet Mn and Mo concentrations throughout a competitive season in men's and women's football players, and (b) investigate sex-based discrepancies. A total of 46 football players (22 men: age; 20.62 \(\pm\) 2.66 years; height; 1.76 \(\pm\) 0.061 m; weight; 71.50 \(\pm\) 5.93 kg, and 24 women: age; 23.21 \(\pm\) 4.11 years; height; 1.65 \(\pm\) 0.06 m; weight; 59.58 \(\pm\) 7.17 kg) participated in this study. Three assessments were conducted throughout the competitive season. Data were collected on anthropometry, body composition, nutritional intake, physical fitness, female hormones, haematology, and the determination of Mn and Mo in different biological compartments. Regarding Mn, significant sex differences were observed in plasma, urine, and erythrocyte concentrations (_p_ < 0.05). Moreover, significant variations were observed throughout the season in all analysed biological compartments (_p_ < 0.05). Regarding Mo, significant sex differences were reported in plasma concentrations (_p_ < 0.05). Similarly, there were variations throughout the season in all analysed biological compartments (_p_ < 0.05). Plasma, urine, erythrocyte, and platelet Mn and Mo concentrations could change during a competitive season in football players. On the other hand, sex differences could exist in plasma, urine, and erythrocyte Mn concentrations in football players.  
trace elements; minerals; exercise; soccer +
Footnote †: journal: Applied Sciences
# Mixed-Degree Cubature \(H_{\infty}\) Information Filter-Based Visual-Inertial Odometry
## 2 Model of Visual-Inertial Odometry  
In this section, we present the model of a keyframe-based VIO system consisting of the motion model, the keyframe management, and the measurement model. The motion model was formulated in form of classical rigid-body kinematics with the modified Rodrigues parameter representing the attitude. The keyframe management was mainly inspired by [PERSON]'s DSO system with some simplifications. The measurement model was formulated in the form of the simple frame-to-frame alignment with the intensity as the measurement.
## 3 The Particle Swarm Optimization Theory  
The particle swarm optimization (PSO) algorithm is an evolutionary computation technique developed by [PERSON] and [PERSON] in 1995 [34]. In this algorithm, a swarm of particles explores a multi-dimensional search space to find optimal solutions. Given an optimization function \(f(X)\), where \(X\) is an \(n\)-dimensional random vector, these particles serve as candidate solutions for the optimization problem. Each particle \(i\) is characterized by its velocity \(V_{i}=(v_{i1},v_{i2},...,v_{ij})\) and position \(P_{i}=(p_{i1},p_{i2},...,p_{ij})\), \(i=1,2,...,q\), \(j=1,2,...,n\), and \(n\) and \(q\) represent the dimensions and swarm size, respectively. Each particle represents a candidate solution and searches for the global optimum in the problem space. To find the optimal solution, each particle moves towards the previous best position (_pbest_) and the global best position (_gbest_) in the cluster can be mathematically expressed as [35]\[\begin{split} pbest(i,t)=\arg\min_{k=1,\cdots,t}[f(P_{i}(k))],i\in 1,2, \cdots,N_{p},\\ gbest(t)=\arg\min_{\begin{subarray}{c}i=1,\cdots,N_{p}\\ k=1,\cdots,t\end{subarray}}\big{[}f(P_{i}(k))\big{]},\end{split} \tag{1}\]  
where \(N_{p}\) denotes the total number of particles, \(t\) denotes the current iteration number, and \(f\) denotes the fitness function. In each generation, particles \(i\) adjusts its velocity \(V_{i}\) and position \(X_{i}\) according to the following formula [36]:  
\[\begin{split} v_{i}(t+1)=wv_{i}(t)+c_{1}r_{1}(t)(pbest(i,t)-P_{ i}(t))+c_{2}r_{2}(t)(gbest(t)-P_{i}(t)),\\ P_{i}(t+1)=P_{i}(t)+v_{i}(t+1),\end{split} \tag{2}\]  
where \(w\) is the inertia weight used to balance the global exploration and local exploitation, \(r_{1}\) and \(r_{2}\) are uniformly distributed random variables within range \([0,1]\), and \(c_{1}\) and \(c_{2}\) are positive constant parameters called acceleration coefficients. As the algorithm progresses, particles share their best-known positions with some or all of the swarm. This collaborative sharing helps the guide of the group toward optimal solutions. The extent to which this division affects individual particles depends on the specific environmental topology used.
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
###### Abstract  
Generalized Hilbert transform (GHT) has been widely and successfully used in signal processing and interpretation for many years. However, the order parameter in GHT can only be chosen in a certain range, and the GHT result is not stable with respect to the chosen order parameter, which usually leads to poor-stability and low-resolution outputs. To improve the stability of GHT and broaden its order parameter chosen range, and finally achieve high-efficiency and high-resolution results, we extend GHT by introducing the sign function, the two frequency integration parameters, and propose the LP-order time-frequency spectra (LTFS). In LTFS, the sign function aims to broaden the chosen range of the order parameter into any positive numbers, and then leads to high-stability and high-resolution LTFS results; the two frequency integration parameters aim to optimize the computation efficiency and output resolution of LTFS. Synthetic one-dimensional (1D) channel data testing, synthetic two-dimensional (2D) seismic data, and actual three-dimensional (3D) seismic data examples demonstrate favorable capability of LTFS for edge detection.  
generalized Hilbert transform; order parameter; frequency integration parameters; sign function; LP-order; high stability; high resolution; high efficiency Article  
Edge Detection
## 2 Materials and Methods  
### Study Design  
A pilot study was performed to establish the sample size calculation. The minimum sample size was fixed at 15 instruments for each group (\(n=15\)) to ensure a test power of 0.80 (G*Power 3.1.9.2 software, Heinrich-Heine-Universitat Dusseldorf, Dusseldorf, Germany) with \(\alpha=0.05\) and \(\beta=0.95\). Therefore, 180 new 25/0.06 F6 ST and OC files of 25-mm length were selected. All instruments were previously examined through an optical stereomicroscope with 20 magnification (SZR-10; Optika, Ponteranica, Bergamo, Italy) for any evidence of visible deformation. No instrument was discarded. Instruments, all from the same production lot, were randomly allocated into 6 groups (\(n=15\)) for each brand. Groups 1 and 4 (the control groups) included new instruments that were tested without NaOCI contact at room (20 \({}^{\circ}\)C) or body (37 \({}^{\circ}\)C) temperature, respectively. Groups 2 and 5 were composed of instruments tested with liquid NaOCI (Chloraxid 5.25%, PPH CERKAMED, Stalowa, Poland) at 20 \({}^{\circ}\)C or 37 \({}^{\circ}\)C, respectively. The remaining groups (groups 3 and 6) received instruments that were tested with NaOCI gel (Chloraxid 5.25% GEL, PPH CERKAMED, Stalowa, Poland) at 20\({}^{\circ}\)C or 37 \({}^{\circ}\)C, respectively. According to the manufacturer, the two formulations present the same composition (i.e., sodium hypochlorite (5.25% of active chlorine), purified water European Pharmacopoeia) except for the gel base (ethoxylated behenyl alcohol, glycol solvent) present in the Chloraxid 5.25% GEL [18; 19].
# Model for Quantifying the Quality of Secure Service
## 5 Case Studies of Multi-Channel QoS
### Three-Channel Network  
The second example applies PSMT to the communication architecture, and demonstrates the initial application of multiple channels. In this example, the communication network uses three discrete, heterogeneous channels to communicate between the transmitter and the receiver. For this example, \(n=3\), \(\sigma=1\) listener, and \(\rho=1\) disruptor. One difference between the single channel case and the three-channel case is the AL. The original message is fragmented into three equal portions, \(f_{1}\), \(f_{2}\), and \(f_{3}\), which are each transmitted twice as follows: \(\{f_{1},f_{2}\}\) on Channel 1, \(\{f_{2},f_{3}\}\) on Channel 2, and \(\{f_{3},f_{1}\}\) on Channel 3. For this case \(AL=0.66\), and \(DF=2\) (because each fragment is sent twice).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Channel** & \(\mathbf{P(int)}\) & \(\mathbf{P(der)}\) & \(\mathbf{P(dec)}\) & \(\mathbf{P(I)}\) & \(\mathbf{P(C)}\) & \(\mathbf{P(n)}\) & \(\mathbf{P(s)}\) & \(\mathbf{P(inj)}\) & \(\mathbf{P(c)}\) & \(\mathbf{P(I)}\) \\ \hline \(1\) & 0.5 & 1 & 1 & 0.5 & 0.5 & 0.25 & 1 & 0.33 & 0.9381 & 0.0619 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Input and output values for a single-channel network.  
Table 4 shows the theorized characteristics for a network with various probability of interception and fixed values for probability of decryption and decoding. Additionally, Table 4 shows that the network has various probabilities of injection with fixed values for probability of noise and suppression.  
Channel 1 has identical input factors to the single-channel network as demonstrated in Table 3; however, the message is fragmented across multiple channels, which causes the probability of confidentiality and probability of integrity to increase, not only for Channel 1, but for each channel in the network [12]. The average probability of confidentiality is 0.83 even without encryption, indicating that fracturing data across the multiple channels improves the probability of confidentiality and over-all QoSS, partially mitigating the lack of encryption.