esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussions
### Discussion of Different Soil Types  
The study at hand encompasses the simulation of silty clay conditions, a soil type characterized by diminutive clay particles manifesting elevated cohesion and internal friction angles. This attributes to heightened resistance to particle displacement within the context of hydraulic forces. The cohesive nature of clay particles precipitates the establishment of a robust interparticle adhesion framework when subjected to aqueous  
Figure 27: The internal force chains within the subsurface at 400,000 simulation steps.  
flow, thereby attenuating the pace of particle detachment. The intricacies of the clay's pore structure and its permeability orchestrate the dynamics of water infiltration and interstitial fluid flow, ultimately wielding a discernible imprint upon the mode of soil erosion.  
In contrast, sandy soil, distinguished by its coarser particle constitution, is rendered susceptible to pronounced displacement and dislodgement in response to the hydraulic scouring phenomenon. A reflection of its relatively diminished cohesive and internal friction attributes, sand particles demonstrate a proclivity to forfeit resistance in the presence of water flux, thereby rendering their susceptibility to entrainment and consequent amplification of the rate of erosional attrition. Beyond this, the augmented permeability characterizing sandy soil engenders active groundwater percolation and soil particle mobilization, thereby heralding ground subsidence and soil mass migration mechanisms that diverge distinctly from the tenets observed in clayey contexts.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 2 Background and Related Work  
This section introduces the idea of opportunistic networks and outlines their main characteristics, then it illustrates these concepts at work in two real-world scenarios, a festival in Belgium, and one in Zurich.
## 2 Materials and Methods
### Obstacle Cost Map Construction  
#### 2.2.1 Elevation Feature Points Extraction  
In DSM, the pits or fallen rocks on the ground are expressed in the form of elevation difference. [PERSON] et al. [35] proposed a method to extract the elevation feature points to represent the terrain changes. The feature points were extracted by the four directions (vertical, horizontal, 45\({}^{\circ}\) left, 45\({}^{\circ}\) right) scanning method based on the section scanning. The key is the selection of terrain thresholds to distinguish different terrains, such as obstacles or little pit. Moreover, for high-precision DSM, [PERSON] pointed out that the equal-interval sampling method can improve the efficiency of subsequent calculations, and the model error is acceptable. Therefore, the original DSM with 0.032 m accuracy is  
Figure 1: The primary process of map construction and Hybrid A* algorithm planning.  
Figure 2: Positive photograph and corresponding DSM of the cutting zone. (**a**) is the positive photograph; (**b**) is the digital surface model.  
reprocessed to 0.096 m accuracy by the equal-intervals sampling method in this paper. Referring [16], we set \(T=0.3\) m as the terrain threshold of impassable elevation difference, and \(\theta{=15}^{\circ}\) as the slope threshold of the maximum passable slope. The steps of extracting elevation feature points are as follows (take the vertical scanning as an example):  
1. [leftmargin=*]
2. Reforming the elevation matrix \(E\) according to the different scanning directions. The elements of each column in \(E\) is put into \(E_{i}\), as shown in Equation (2). \[E_{i}=\left[\begin{array}{c}e_{1i}\\ \vdots\\ e_{mi}\end{array}\right],E=(E_{1},E_{2}\ldots E_{n}),i=1,2\ldots n\] (2)
3. Scanning each data point sequentially in \(E_{i}\): set a searching group with \(e_{j-1},e_{j},e_{j+1},(j=2,3\ldots m-1)\), the step length is one. If the point is the first or last point of \(E_{i}\), or local extremum of the group, add the point \(p\) to the candidate feature points set \(F_{p}\), where \(p\) contains the elevation value and coordinate information. The extracted feature points are shown in Figure 2(a).
4. Filtering the points of \(F_{p}\) by terrain threshold \(T\) which is defined by the characteristics of the cutting zone. Then determine whether \(p_{k}\) and \(p_{k+1}\) are adjacent in \(F_{p}\). If they are adjacent, judge the absolute value of two-point difference, whether it's over the threshold \(T\). If the value greater than \(T\), \(p_{k+1}\) will be reserved in \(F_{p}\). Otherwise, \(p_{k+1}\) will be removed from \(F_{p}\). The original section scanning line and the final feature point set \(F_{p}\) are shown in Figure 2(b).
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Zinc Iron Oxide Films  
The ZnFe\({}_{2}\)O\({}_{4}\) thin films were produced in the absence of a surfactant [187]. A porous surface with nanoplate particles could be observed. The obtained film exhibited a specific capacitance of 471 F/g when the scan rate was 5 mV/s, in a 1 M NaOH solution. The researcher also highlighted the voltage window (1 V), power density (277 W/kg), energy density (4.47 Wh/kg), and long cycle life in solid state symmetric supercapacitor devices.
## 3 Results
### Association between Anthropometric Parameters and Physical Fitness
Sci._**2024**, _14_, 9217  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \(\beta\) (**95\% CI**) & \(\beta\) _p-Value_ & \(\beta\) **st** & **R\({}^{2}\) Adjusted** & **RMSE** & **F** \\ \hline Fat mass (\%) & & & & & & \\ Sum of four skinfolds (mm) a  & 0.36 (0.31; 0.42) & **<0.001** & 0.83 & 0.69 & 4.47 & 188.9 \\ Subscapular skinfold (mm) & 1.31 (1.1; 1.6) & **<0.001** & 0.75 & 0.56 & 5.31 & 110.1 \\ Waist circumference (cm) & 0.60 (0.40; 0.80) & **<0.001** & 0.55 & 0.29 & 6.76 & 36.1 \\ Waist-to-height ratio & 114.7 (79.6; 149.7) & **<0.001** & 0.58 & 0.33 & 6.59 & 42.3 \\ Fat-free mass (kg) & & & & & & \\ Humeral diameter (cm) & 14.0 (9.6; 18.4) & **<0.001** & 0.77 & 0.68 & 4.74 & 44.1 \\ Femoral diameter (cm) & 9.76 (6.5; 13.0) & **<0.001** & 0.76 & 0.66 & 4.89 & 40.1 \\ Sum of diameters (cm) b  & 5.95 (4.1; 7.8) & **<0.001** & 0.78 & 0.70 & 4.64 & 46.8 \\ Bone mineral content (g) & & & & & & \\ Humeral diameter (cm) & 499.4 (284.5; 714.3) & **<0.001** & 0.59 & 0.53 & 231.1 & 23.7 \\ Femoral diameter (cm) & 354.3 (202.4; 506.3) & **<0.001** & 0.59 & 0.53 & 230.7 & 23.8 \\ Sum of diameters (cm) b  & 214.6 (126.1; 303.1) & **<0.001** & 0.60 & 0.55 & 225.7 & 25.7 \\ Bone mineral density (g/cm\({}^{2}\)) & & & & & & \\ Humeral diameter (cm) & 0.18 (0.08; 0.28) & **0.001** & 0.70 & 0.41 & 0.11 & 15.1 \\ Femoral diameter (cm) & 0.13 (0.06; 0.20) & **0.001** & 0.71 & 0.42 & 0.11 & 15.4 \\ Sum of diameters (cm) b  & 0.08 (0.04; 0.12) & **0.001** & 0.72 & 0.43 & 0.10 & 16.3 \\ VO\({}_{2 peak}\) (mL-kg\({}^{-1}\)min\({}^{-1}\)) & & & & & & \\ Sum of four skinfolds (mm) a  & \(-0.23\) (\(-\)0.30; \(-\)0.15) & **<0.001** & \(-\)0.55 & 0.30 & 6.35 & 36.5 \\ Subscapular skinfold (mm) & \(-\)0.90 (\(-\)1.20; \(-\)0.60) & **<0.001** & \(-\)0.55 & 0.30 & 6.36 & 35.9 \\ Waist circumference (cm) & \(-\)0.34 (\(-\)0.56; \(-\)0.13) & **0.002** & \(-\)0.33 & 0.10 & 7.21 & 10.1 \\ Waist-to-height ratio & \(-\)51.7 (\(-\)91.2; \(-\)12;2) & **0.011** & \(-\)0.28 & 0.07 & 7.34 & 6.77 \\ Handgrip strength (kg) & & & & & & \\ Humeral diameter (cm) & 18.4 (8.1; 28.6) & **0.001** & 0.50 & 0.39 & 11.0 & 14.0 \\ Femoral diameter (cm) & 13.4 (6.4; 20.5) & **0.001** & 0.52 & 0.42 & 10.8 & 15.7 \\ Sum of diameters (cm) b  & 8.0 (3.8; 12.3) & **0.001** & 0.52 & 0.43 & 10.7 & 16.0 \\ Abdominal endurance (reps/min) & & & & & & \\ Waist-to-hip ratio & \(-\)24.2 (\(-\)66.2; 17.8) & 0.256 & \(<\)0.01 & \(<\)0.01 & 14.3 & 1.3 \\ Waist-to-height ratio & \(-\)37.0 (\(-\)112.9; 38.9) & 0.335 & \(<\)0.01 & \(<\)\(-\)0.01 & 14.4 & 0.94 \\ Subscapular skinfold (mm) & \(-\)0.13 (\(-\)0.80; 0.55) & 0.705 & \(<\)0.01 & \(-\)0.01 & 14.4 & 0.14 \\ Stand Broad Jump (cm) & & & & & & \\ Calf skinfold (mm) & \(-\)2.62 (\(-\)5.1; \(-\)0.14) & **0.040** & \(-\)0.35 & 0.16 & 28.4 & 4.88 \\ Subscapular skinfold (mm) & \(-\)1.32 (\(-\)3.5; 0.81) & 0.210 & \(-\)0.20 & 0.03 & 30.6 & 1.69 \\ Sum of four skinfolds (mm) a [FOOTNOTE:a]Footnote a: \({}^
# Driver Clustering Based on Individual Curve Path Selection Preference
## 2 Methods and Materials
### Online Parameter Identification
\begin{table}
\begin{tabular}{l} \hline Step 0: Initialization \\ \(\Sigma_{0}\) and \(x_{0}\) \\ \hline Step 1: Prediction (prior estimates) \\ \(x_{k|k-1}=f(x_{k-1|k-1},u_{k})\) \\ \(\Sigma_{k|k-1}=F_{k}\Sigma_{k-1|k-1}F_{k}^{T}+Q\) \\ \hline Step 2: [PERSON] gain calculation \\ \(K_{k}=\Sigma_{k|k-1}H_{k}^{T}(H_{k}\Sigma_{k|k-1}H_{k}^{T}+R)\) \\ \hline Step 3: Correction (posterior estimates) \\ \(x_{k|k}=x_{k|k-1}+K_{k}(z_{k}-h(x_{k|k}))\) \\ \(\Sigma_{k|k}=(I-K_{k}H_{k})\Sigma_{k|k-1}\) \\ \hline \end{tabular}
\end{table} 
Table 1: EKF Algorithm steps. \[Q= \begin{bmatrix}I^{3\times 3}\sigma_{y}^{2}&O^{3\times 18}\\ O^{18\times 3}&I^{18\times 18}\sigma_{p}^{2}\end{bmatrix} \quad R= \begin{bmatrix}I^{3 \times 3}\sigma_{y}^{2}\end{bmatrix}  \tag{36}\]  
In most applications, selecting the proper covariance values is critical. Often, covariance values are calculated to maximize the logarithmic likelihood of the predicted outputs, which is usually referred to as Maximum Likelihood Estimates (MLE). \[\ell=\sum_{j=1}^{k}\log(p(x_{j}|x_{1}^{\prime},\cdots,x_{j-1})) \tag{37}\]  
The likelihood of state \(x_{k}^{\prime}\) is calculated as the conditional probability on previous states, as given in (37), which can be formulated for Table 1 filter equations, as given in (38). \[\ell=\ell_{0}+\sum_{j=1}^{k}\log(|\Sigma_{y}|)+y_{err}\Sigma_{y}^{-1}y_{err_{ j}} \tag{38}\]  
with \(\Sigma_{y}=H\Sigma_{k|k-1}H^{\top}+R\) and \(y_{err_{k}}=z_{k}-h(x_{k|k})\). \(\ell_{0}\) is the initial likelihood estimate. The optimization problem of the covariance \(\sigma_{y}\), \(\sigma_{p}\) and \(r_{y}\) is formulated in (39). Optimization is performed using the _fmincon()_ function of MATLAB 2024b. \[ \begin{array}{rl}\min_{\sigma_{y}\sigma_{p},r_{y}}&-\ell(\sigma_{y},\sigma _{p},r_{y})\\ \text{s.t.}&\sigma_{y},\sigma_{p},r_{y}\geq 0\end{array}  \tag{39}\]
###### Abstract  
Sentiment analysis has played a primary role in text classification. It is an undoubtedly fact that some years ago, textual information was spreading in manageable rates; however, nowadays, such information has overcome even the most ambiguous expectations and constantly grows within seconds. It is therefore quite complex to cope with the vast amount of textual data particularly if we also take the incremental production speed into account. Social media, e-commerce, news articles, comments and opinions are broadcasted on a daily basis. A rational solution, in order to handle the abundance of data, would be to build automated information processing systems, for analyzing and extracting meaningful patterns from text. The present paper focuses on sentiment analysis applied in Greek texts. Thus far, there is no wide availability of natural language processing tools for Modern Greek. Hence, a thorough analysis of Greek, from the lexical to the syntactical level, is difficult to perform. This paper attempts a different approach, based on the proven capabilities of gradient boosting, a well-known technique for dealing with high-dimensional data. The main rationale is that since English has dominated the area of preprocessing tools and there are also quite reliable translation services, we could exploit them to transform Greek tokens into English, thus assuring the precision of the translation, since the translation of large texts is not always reliable and meaningful. The new feature set of English tokens is augmented with the original set of Greek, consequently producing a high dimensional dataset that poses certain difficulties for any traditional classifier. Accordingly, we apply gradient boosting machines, an ensemble algorithm that can learn with different loss functions providing the ability to work efficiently with high dimensional data. Moreover, for the task at hand, we deal with a class imbalance issues since the distribution of sentiments in real-world applications often displays issues of inequality. For example, in political forums or electronic discussions about immigration or religion, negative comments overwhelm the positive ones. The class imbalance problem was confronted using a hybrid technique that performs a variation of under-sampling the majority class and over-sampling the minority class, respectively. Experimental results, considering different settings, such as translation of tokens against translation of sentences, consideration of limited Greek text preprocessing and omission of the translation phase, demonstrated that the proposed gradient boosting framework can effectively cope with both high-dimensional and imbalanced datasets and performs significantly better than a plethora of traditional machine learning classification approaches in terms of precision and recall measures.  
gradient boosting machines; sentiment analysis; high-dimensional data; Modern Greek
# Vibration Response Analysis of Overhead System Regarding Train-Track-Bridge Dynamic Interaction
## 2 System Dynamics Theory of Train-Track Overhead System
### Vibration Analysis Model of Accelerated Train  
Any rigid body in space has six degrees of freedom, according to rigid body dynamics. Without taking into account the rigid body's axial expansion and contraction, each rigid  
Figure 12: Train-track-overhead system model.  
Figure 13: The process of joint simulation.  
body has five degrees of freedom: Horizontal, vertical, rolling, nodding, shaking. The train dynamics model has 35 degrees of freedom per vehicle, based on the number of rigid bodies. The differential equations of motion of the train body and the two bogies can be expressed by Equation (5).  
\[\begin{bmatrix}M_{cc}&0&0\\ 0&M_{t_{1}t_{1}}&0\\ 0&0&M_{t_{2}t_{2}}\end{bmatrix}\left\{\begin{array}{c}\ddot{v}_{c}\\ \ddot{v}_{t_{1}}\\ \ddot{v}_{t_{2}}\end{array}\right\}+\begin{bmatrix}C_{cc}&C_{t_{1}c}&C_{t_{2}c }\\ C_{ct_{1}}&C_{t_{1}t_{1}}&0\\ C_{ct_{2}}&0&C_{t_{2}t_{2}}\end{bmatrix}\left\{\begin{array}{c}\dot{v}_{c} \\ \dot{v}_{t_{1}}\\ \dot{v}_{t_{2}}\end{array}\right\}+\\ \left\{\begin{array}{c}K_{cc}&K_{t_{1}c}&K_{t_{2}c}\\ K_{ct_{1}}&K_{t_{1}t_{1}}&0\\ K_{ct_{2}}&0&K_{t_{2}t_{2}}\end{array}\right\}\left\{\begin{array}{c}v_{c} \\ v_{t_{1}}\\ v_{t_{2}}\end{array}\right\}=\left\{\begin{array}{c}F_{c}\\ F_{t_{1}}\\ F_{t_{2}}\end{array}\right\} \tag{5}\]  
where: \(c\) = train  
\(t_{1}\), \(t_{2}\) = the front and rear bogies  
\(v\) = Displacement of train body  
\(\dot{v}\) = Moving speed of train body  
\(\ddot{v}\) = Moving acceleration of train body  
\(M\) = Mass submatrix  
C = Damped submatrix  
\(K\)--Stiffness submatrix.  
The load vector consists of two parts, which are expressed as shown in Equation (6).  
\[\left\{\begin{array}{c}F_{c}\\ F_{t_{1}}\\ F_{t_{2}}\end{array}\right\}=\left\{\begin{array}{c}F_{ce}\\ F_{t_{1}v}\\ F_{t_{2}v}\end{array}\right\}+\left\{\begin{array}{c}0\\ F_{t_{1}v}\\ F_{t_{2}v}\end{array}\right\} \tag{6}\]  
where: \(F_{ce}\), \(F_{t_{1}e}\), \(F_{t_{2}e}\) = External force acting on train body and bogie; \(F_{t_{1}w}\), \(F_{t_{2}w}\) = The force of the wheelset transmitted to the bogie through primary suspension.
## 4 Design and Reconstruction of Sliding Mode Fault Observer (SMFRO)  
### Design of Robust Adaptive Sliding Mode Fault Observer  
For the system Equation (10), the sliding mode state observer is designed as follows:  
\[\left\{\begin{array}{l}\dot{\hat{x}}(t)=A\hat{x}(t)+Bu(t)-L_{l}(C\hat{x}(t)- y(t))+L_{n}v_{\rho}\\ \dot{y}(t)=\mathrm{C}\hat{x}(t)\end{array}\right. \tag{12}\]  
where \(L_{l}\) is the undetermined gain matrix, \(L_{n}\) is the nonlinear feedback matrix, and \(v_{\rho}\) is the sliding mode strategy to be designed. The state estimation error of the observer is defined as \(e(t)=\hat{x}(t)-x(t)\), the output estimation error is \(e_{y}(t)=\hat{y}(t)-y(t)\), and the deviation system equation \(\dot{e}(t)=(A-L_{l}\mathrm{C})e(t)+L_{n}v_{\rho}-Ff_{a,\delta}(t)\) is obtained by Equations (10) and (12).  
According to the system Equation (11), a sliding mode fault reconstruction observer is proposed:  
\[\left\{\begin{array}{l}\dot{\hat{x}}_{1}(t)=A_{1}\hat{x}_{1}(t)+A_{2}\hat{x}_ {2}(t)+B_{1}u(t)-A_{2}e_{y}(t)\\ \dot{y}_{2}(t)=A_{3}\hat{x}_{1}(t)+A_{4}\hat{x}_{2}(t)+B_{2}u(t)-(A_{4}-A_{4}^{ \delta})e_{y}(t)+v_{\rho}\end{array}\right. \tag{13}\]where \(A_{4}^{s}\in R^{p}\) is the arbitrary stable matrix, the robust SMO is undetermined gain matrix \(L_{l}\) and the nonlinear feedback matrix \(L_{n}\) is, respectively, \(L_{l}=T^{-1}\left[\begin{array}{c}A_{2}\\ A_{4}-A_{4}^{s}\end{array}\right]\), \(L_{n}=T^{-1}\left[\begin{array}{c}0\\ I_{p}\end{array}\right]\).  
It is assumed that the deviation between the system (10) and the SMO (12) is always bounded: \(\|e\|\leq\varepsilon\), \(\varepsilon\) is the normal number, and the following sliding mode strategy is proposed for the observer Equation (12):  
\[\
u_{\rho}=\left\{\begin{array}{ll}-\rho\|P_{2}\|\frac{P_{2}C_{L}\
u_{\rho}}{ \|P_{2}C_{L}\
u_{\rho}\|}&e_{y}\
eq 0\\ 0&e_{y}=0\end{array}\right. \tag{14}\]  
where \(P_{2}\in R^{p}\) is the Lyapunov matrix of \(A_{4}^{s}\), \(\rho\) is the correction coefficient and \(0<\rho\leq 1\).  
The state estimation error is \(e_{1}(t)=\hat{x}_{1}(t)-x_{1}(t)\), and the output deviation is \(e_{y}(t)=\hat{x}_{2}(t)-x_{2}(t)\). The state and output equations of the system are Equations (15) and (16):  
\[\hat{e}_{1}=A_{1}e_{1}(t) \tag{15}\]  
\[\hat{e}_{y}=A_{3}e_{1}(t)+A_{4}^{s}e_{y}(t)+\
u_{\rho}-F_{2}f_{a,s}(t) \tag{16}\]  
According to [32], the system Equations (15) and (16) are asymptotically stable. When \((e_{1},e_{y})\to 0\), the SMO is asymptotically stable.
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
## 3 TSN End-Side Equipment Design Scheme
### Hardware Coordination  
In order to improve the accuracy of message transmission timing, this scheme uses an Intel I210 as the network card. This NIC has four transmit queues, with queue numbers 0 and 1 having the ability to send hardware-timed messages, as shown in Table 2. The LaunchTime field in the transmit descriptor is used to set the time for sending packets in queues 0 and 1. LaunchTime is a 25-bit field defined in units of 32 ns, which can ensure that the packets leave the NIC device at a precise and controllable time. Therefore, NIC queues 0 and 1 can be used for the transmission of time-sensitive streams, and queues 2 and 3 can be used for the transmission of best-effort streams.  
The hardware-timed sending capability of the I210 NIC works in conjunction with the Linux ETF Qdisc software scheduling function. ETF Qdisc sends packets to the NIC driver's queue at the T-delta moment. The NIC driver writes the packet send time T to the LaunchTime field of the send descriptor and notifies the NIC driver DMA to carry the packet to queue 0 or queue 1 within the NIC. The NIC extracts the LaunchTime field of the descriptor to obtain the packet send time. Until the current time is greater than the send time T, the packet will leave the NIC hardware port. Queues 2 and 3 are used to cache the best-effort streams and transmit the messages in the queue out of the network port in FIFO order.  
It is worth noting that the sending time of the message is set in the user application, the time base used is the Linux system clock, and the software scheduling performed in the ETF also uses the Linux system clock as the time base, whereas the current time reference is the hardware clock of the NIC when performing the timed sending in the hardware of the NIC, so the system clock and hardware clock of the TSN-side device must be kept synchronized. Here, linuxptp is used to synchronize the I210 NIC with the Linux system hardware and software clocks.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**63–57** & **56–32** & **31–16** & **15–9** & **8–0** \\ \hline \hline Reserved & LaunchTime & VLAN & MACLEN & IPLEN \\ \hline \end{tabular}
\end{table}
Table 2: I210 NIC transmit descriptors.  
Figure 4: ETF soft scheduling in Linux.
# Optimized Design for NB-LDPC-Coded High-Order CPM: Power and Iterative Efficiencies
## 4 Code Design and Advantages  
### EXIT Technique  
The EXIT chart [35] is a powerful technique for predicting the iterative detection convergence using SISO modules. Similar to the density evolution, this technique assumes extrinsic information in SISO decoders as independent Gaussian random variables. Figure 4 depicts the extrinsic information interaction between two SISO decoders of NB-LDPC-coded high-order CPM. CPM-SISO has two inputs of the prior information from the interleaver and the inner codeword information from matched filters. As the inner codeword information is connected to \(\mathrm{E_{b}/N_{0}}\), the average mutual information of the CPM-SISO output can be treated as a function of the average mutual information of the input \(I_{A}^{\mathrm{CPM}}\) and \(\mathrm{E_{b}/N_{0}}\), given by:  
\[I_{E}^{CPM}=T_{CPM-SISO}(I_{A}^{CPM},\mathrm{E_{b}/N_{0}}) \tag{14}\]  
The average mutual information of the LDPC-SISO output \(I_{E}^{LDPC}\) is only related to the average mutual information of the input \(I_{A}^{LDPC}\), resulting in:  
\[I_{E}^{LDPC}=T_{LDPC-SISO}(I_{A}^{LDPC}) \tag{15}\]  
where functions \(T_{CPM-SISO}(\cdot)\) and \(T_{LDPC-SISO}(\cdot)\) are defined as the EXIT characteristics of CPM-SISO and LDPC-SISO, respectively.  
Figure 4: Serially iterative decoder of NB-LDPC-coded high-order CPM.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 3 The Basic CBBA
### Drawbacks of CBBA in Imaging Satellite Mission Planning  
From the introduction of the CBBA algorithm above, it can be seen that for task \(t_{j}\), which currently has the highest global bid, satellite \(s_{i}\) will prioritize adding to its own task bundle, and all satellites will obtain and update this information during the consensus check phase. Therefore, CBBA will prioritize determining the bidding result for the task with the highest global bid, and achieve consensus on this information globally. However, the selection of tasks affects each other. For example, the execution of one task may cause the O-VTW of other tasks to be unavailable (which can be regarded as a bid of 0), i.e., some tasks will be masked. Throughout the entire CBBA process, hidden tasks may be re-qualified for election due to conflict resolution during the consensus check phase.  
Let us take Figure 5 as an example to explain the masking phenomenon. The figure shows the O-VTW information and bidding information of the two satellites for task A and task B, respectively. Both missions are visible to satellite \(s_{1}\), but only one of them can be observed due to time window overlap conflicts. Table 4 shows the scheduling process information of the two stages of CBBA. In the first iteration, \(s_{1}\), prioritizes task A with a higher bid, and due to time window conflicts, the bid for task B is 0. In the second iteration, task B regains the bidding qualification due to task A not being added to the bundle. With the increase in satellite scale and task scale, the frequency of such phenomena increases greatly, and even there are conflicting relationships among multiple tasks. At this point, the change in one task can affect the bidding of multiple tasks. This masking phenomenon leads to capricious bids for the same task. In the worst case, after a round of auction, only one task's scheduling result may be determined, that is, the task with the highest global bid. At this point, when the number of tasks is \(|T|\), \(|T|\) rounds of auctions are needed to determine the final scheduling result, which to some extent limits the convergence speed of the algorithm and indirectly increases the communication cost of the algorithm.