esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### The SMS Decoder  
Consider the Tanner graph [29] representation of the PCM \(\mathbf{H}\) for a QC-LDPC code. For any positive integer \(\omega\), we define the set \([\omega]=\{0,1,\ldots,\omega-1\}\). For \(n\in[N]\) and \(m\in[M]\), we denote the \(n\)th variable node (VN) and the \(m\)th check node (CN) in the Tanner graph by \(v_{n}\) and \(c_{m}\), respectively. Note that \(v_{n}\) and \(c_{m}\) are known as neighbors and connected to each other if there is a one-entry in the \(n\)th row and \(n\)th column of \(\mathbf{H}\). Define the index sets of neighboring nodes of \(v_{n}\) and \(c_{m}\) by \(\mathcal{N}(v_{n})\) and \(\mathcal{N}(c_{m})\), respectively. For a set \(\mathcal{A}\) and an element \(a\in\mathcal{A}\), we denote the set with index \(a\) excluded by \(\mathcal{A}\setminus a\).  
The shuffled decoding schedule was first proposed in [23] for the belief propagation (BP) algorithm to reduce the loading latency of the exchanged messages. For practical concerns, we focus on the low-complexity SMS decoder [27] and briefly introduce the details as follows. Denote the check-to-variable (C2V) message sent from \(c_{m}\) to \(v_{n}\) at the \(t\)th iteration by \(S_{mn}^{(t)}\), and denote the variable-to-check (V2C) message sent from \(v_{n}\) to \(c_{m}\) by \(R_{nm}^{(t)}\). Here, \(t=1,2,\ldots,T_{\max}\), where \(T_{\max}\) is a maximum preset number of iterations. Define \(L(v_{n})\) as the channel output of the node \(v_{n}\). At each iteration, the SMS decoder updates the exchanged (C2V/V2C) messages by  
\[S_{mn}^{(t)}=\alpha\cdot\prod_{n^{\prime}\in\mathcal{N}(c_{m}) \setminus n}\text{sgn}(R_{n^{\prime}m}^{(t-1(n^{\prime}>n))})\cdot\min_{n^{ \prime}\in\mathcal{N}(c_{m})\setminus n}\Big{|}R_{n^{\prime}m}^{(t-1(n^{ \prime}>n))}\Big{|}\,, \tag{1}\] \[R_{nm}^{(t)}=L(v_{n})+\sum_{m^{\prime}\in\mathcal{N}(v_{n}) \setminus m}S_{m^{\prime}n}^{(t)}\,, \tag{2}\]  
where \(\alpha\) is the normalized factor and \(\mathbb{I}\) is the indicator function operating as  
\[\mathbb{I}(statement)=\begin{cases}1,&\text{$statement$ is true,}\\ 0,&\text{otherwise.}\end{cases} \tag{3}\]  
Note that for \(t=0\), we have \(R_{mn}^{(0)}=L(v_{n})\). Denote a posterior message of the node \(v_{n}\) by \(Q_{n}\), which can be computed at the end of each iteration by  
\[Q_{n}=R_{mn}^{(t)}+S_{mn}^{(t)}. \tag{4}\]In practice, the shuffled decoding schedule is generally conducted by partitioning the PCM \(\mathbf{H}\) into \(N_{b}\) groups, so-called the layers, where each layer consists of \(Z\) VNs and corresponds to a QC column block of \(\mathbf{H}\). Note that we consider that any two columns in each layer have at most a one-entry in the same row. Therefore, the layer \(j\) of \(\mathbf{H}\) is equivalent to the \(j\)th column of the base matrix \(\mathbf{H}_{b}\). Within a layer, the SMS decoder updates the exchanged messages in parallel and the decoding process proceeds in serial among consecutive layers.
###### Abstract
In this paper, Deep Reinforcement Learning (DRL) algorithms are considered for the application of AI in the automation of network operations to ensure the provision of QoE. Specifically, this paper shows the work that has been carried out in which the algorithm applies a series of actions that consist of establishing the target bitrate, maximizing the QoE based on the state of the network. It is used to prevent bothersome situations and bottlenecks from spreading throughout the network and to localize its sources to enable fast and precise corrective actions. Reinforcement Learning (RL) addresses the problem by training agents on which actions should be taken, evaluating the states and actions based on defined reward functions that reflect the idea of quality and efficiency that we want to translate into our automated system. These algorithms include two Neural Networks (NN) in the form of agents to evaluate the correct functioning of training and validation. That is, thanks to Deep Learning (DL) techniques, we can value and emphasize the actions that the RL technique is performing. As training progresses, these corrections will become more accurate. NNs are based on Convolutional Neural Networks (CNN) which have shown high performance in training processes that require a large volume of data. These NNs map the environment observations (inputs) to the bitrate decision (output). This work investigates the potential application of an algorithm called Advantage Actor-Critic (A2C), as it is one of the leading algorithms in DRL, allowing a multimedia service application through multiple agents. These parallel agents perform the training synchronously, that is, the update of the results of the central node is carried out when all the agents have finished their episode. However, this research focused on the use of a single agent, to enable training in standard resource environments. The work presented in this paper contains the initial design and test of a self-optimized cloud streaming vertical, and finally its validation in the laboratory prior to deployment in a real environment. The work is presented as follows. Section 2 introduces the multimedia content used in this work, the components of the service, an explanation of the workflow, and the optimizer theoretical background. The previous sections are accomplished in Section 3, exposing the AI algorithms applied and the results obtained. Finally, Section 4 ends with the conclusions of the work and future work expansion.
# Dimensional Reduction-Based Moment Model for Probabilistic Slope Stability Analysis
## 5 Applications
### Example 2: Soil Slope  
This example compares the proposed method to the traditional polynomial response surface method. The problem set is a homogeneous slope. The strength parameters of soil (cohesion \(c\) and friction angle \(\varphi\)) are the random variables with a normal distribution. The statistical parameters of random variables are listed in Table 8. Other parameters of the slope are the determinate and the following value are specified, i.e., height of slope \(H=10\) m, slope angle \(\beta=45^{\circ}\), unit weight of soil \(\gamma=20\) kN/m\({}^{3}\), and a gravitational acceleration \(g=9.81\) m/s\({}^{2}\). The numerical model was shown in Figure 8. Mohr-Coulomb yield criterion was adopted in this numerical model. There are 1130 nodes and 1024 elements. The safety factor was computed using the strength reduction method.  
The proposed method was used to estimate the reliability of the slope. The mean value and standard deviation of the safety factor are 1.1304 and 0.1768, respectively. The mean value is very close to the safety factor (1.13) by numerical solution using the mean point of random variables. Figure 9 shows the failure mode at the mean point, and failure is also indicated by shear strain rate contours. It shows it is feasible and accurate to estimate the statistical moment using MDRM. The probability distribution of the safety factor was shown in Figure 10 using MDRM based reliability analysis. For sake of comparison, the traditional RSM was adopted to compute the reliability index and failure probability. The comparisons of reliability index and failure probability are shown in Figure 11 based on a different method. The results are in excellent agreement with the traditional response surface method. The maximum relative error of reliability index and failure probability is less than 1.5% and 1.6%, respectively. It shows MDRM can represent well the implicit limit state function, which is a complex, and nonlinear relationship between safety factors. It is feasible to approximate the limit state function using MDRM in slope engineering. In practical slopes, a numerical model was commonly adopted to determine the safety factor. Once the slope stability model is built, it is convenient to determine the MDRM and moment through a repeating calling slope stability analysis model. Therefore, the proposed method provides a practical approach to deal with the uncertainty.  
Figure 7: Probability distribution of safety factor and its comparison with MCS for frictional soil with pore pressure.  
Figure 8: The numerical model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Random Variables** & **Distribution** & **Mean** & **Standard Deviation** \\ \hline Cohesion \(c\) & Normal & 13,000 & 2600 \\ Friction angle \(\varphi\) & Normal & 25 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The statistical parameter of soil strength for soil slope.  
Figure 9: Stability analysis and failure mode by numerical solution at the mean point (FOS = 1.13).
## 3 Methods
### Multi-Scale Feature Extraction Using Swin Transformer
#### 3.3.2 Patch Merging  
Patch merging is performed to reduce the sequence length and computation cost. After processing through several Transformer encoder layers with patch embeddings of size \(P\times P\), patch merging aggregates neighboring patches into a larger patch to downsample the spatial dimensions.  
Specifically, the input sequence of patch tokens \(\mathbf{z}\in\mathbb{R}^{N\times(D+1)}\) is first reshaped to \(\mathbf{z}\in\mathbb{R}^{H/P\times W/P\times(D+1)}\). For each non-overlapping \(2\times 2\) patch block \(\mathbf{z}_{b}\in\mathbb{R}^{2\times 2\times(D+1)}\), patch merging is applied by first averaging the positional encodings \(\mathbf{e}_{b}\) of the 4 patches, then concatenating and linearly projecting the patch embeddings to a lower dimension:  
\[\mathbf{\hat{e}}_{b} =\text{Average}(\mathbf{e}_{b}) \tag{22}\] \[\mathbf{\hat{z}}_{b} =\text{Linear}(\text{Concat}(\mathbf{z}_{b}^{0})) \tag{23}\]  
The merged patch token is then constructed by concatenating \(\mathbf{\hat{z}}_{b}\) and \(\mathbf{\hat{e}}_{b}\). This reduces the number of patches by a factor of 4. The new sequence of downsampled patch tokens is then input to subsequent Transformer encoder layers, which adopt larger patch sizes.  
Through such hierarchical patch merging, SwinT can efficiently process the input time-frequency representations of radar jamming signals. This architecture also allows the network to integrate fine-grained and coarse-grained features to learn representations.
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 4 Simulation  
We used the Waxman model to generate four topological structures, each containing 200 nodes (Waxman200), 2k nodes (Waxman2000), 200k nodes (Waxman200k), and 2 million nodes (Waxman2000k). When choosing a baseline, our primary consideration is to select a method that has an impact; secondly, the baseline should be distributed and have the potential to be deployed in large-scale networks. Based on the above considerations, we have chosen the following three methods as baselines.  
1. WAVE [13]: Each time DR receives a join request, it needs to probe all nodes in the multicast tree and select the MJN with the lowest path load to join. The WAVE algorithm is suitable for asymmetric routing and has an impact on many algorithms [14; 15; 16; 17];Near: DR selects the nearest MJN to join. Many ICN multicast protocols use this method to select MJN [26; 27; 28; 29]. This method cannot detect the path load status in the case of asymmetric routing;
3. SPT: DR adds the root node. Many IP multicast protocols use this method to select MJN [13; 18; 19; 20]. This method cannot detect the path load status in the case of asymmetric routing.  
Our experiment assumes that each multicast group occupies a bandwidth of 6 Mbps (2K video bandwidth), the bandwidth of each link is 10 Gbps (1500 multicast streams), and the switch can accommodate 1K forwarding status at most. Our experiments are conducted on a Dell R740 xd PowerEdge server with two Xeon(R) Gold 5218R CPUs and 300 GB RAM.  
In our experiments, the positions of DRs are distributed in nodes with relatively high degrees. This is because DR, as a link device between the internal and external networks, is a core device in a small area and therefore needs to be deployed in nodes with relatively high centrality. The number of DRs accounts for 1% to 0.1% of the total network nodes, which is a common simulation setting for sparse mode multicast [17; 35]. Since sparse mode is the main mode for multimedia applications [36; 37; 38], this paper simulates under sparse mode.  
The value of \(\zeta\) in Formula (2) varies with changes in topology. For the waxman200 and waxman2000 topologies, since each DR can almost completely explore the topology, the value of \(\zeta\) has little effect on the results, so we set \(\zeta=1\). In the waxman200k topology, we set \(\zeta\) to 1.5, and in the waxman2000k topology, we set \(\zeta\) to 2.  
The traffic generation model in the experiment is as follows: for each multicast group, the source DR is randomly selected, and each multicast group has only one source DR. The number of receivers (group size) ranges from \(1/20\) to \(1/3\) of the total number of DRs, and their locations are randomly selected from all DRs [17; 35]. The specific settings are shown in the Table 2.  
Additionally, in this experiment, the network routing is asymmetric, meaning that the route from point A to point B is often different from the route from point B to point A.
# Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations
## 2 Methods
### Proposed Method
#### 2.2.1 Adaptive Dictionary Learning in Salt and Pepper Noise Removal
To further derive the mathematical property of FDLCP, let \(R_{j}\) denote the operator that extract a patch owned with \(j\)th geometric direction from an image \(x\), and then the FDLCP transform is simplified as for the total \(J\) geometric directions. The forward and inverse transform satisfies:  
\[\mathbf{\Psi}^{T}\mathbf{\Psi}=\Big{[}\mathbf{R}_{1}^{T}\mathbf{D}_{\omega_{1}},\mathbf{R}_{2}^{T} \mathbf{D}_{\omega_{2}},\cdots,\mathbf{R}_{J}^{T}\mathbf{D}_{\omega_{J}}\Big{]}^{T}[\mathbf{R} _{1}^{T}\mathbf{D}_{\omega_{1}},\mathbf{R}_{2}^{T}\mathbf{D}_{\omega_{2}},\cdots,\mathbf{R}_{ J}^{T}\mathbf{D}_{\omega_{J}}]=\ c\mathbf{I}, \tag{8}\]  
where \(\mathbf{I}\) is an identity matrix and the variable \(c\) is the overlapping factor for image patches [15]. By further setting \(\mathbf{\Phi}^{T}=\frac{1}{\sqrt{c}}\Psi^{T}\), the transform \(\mathbf{\Phi}\) obeys:  
\[\mathbf{\Phi}\mathbf{\Phi}^{T}=\mathbf{I}, \tag{9}\]  
meaning that the modified FDLCP transform \(\mathbf{\Phi}\) is a tight frame for image reconstruction [36]. Then, how to train the representations with FDLCP in the salt and pepper noise removal? We suggest learning the FDLCP from a reasonable pre-reconstructed image, e.g., which is obtained with AMF. This strategy has been found to achieve sparse representations under fast computing not only in impulse noise removal [10; 11], but also other image reconstruction problems [15; 16; 37].
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
## 5 Conclusions  
As a key strategic field in the information age, the LEO satellite constellation is facing increasingly serious network security problems. In this paper, the space-time diagram model is proposed to identify the key nodes in the LEO satellite constellation, and a DDoS attack is adopted as the main means to attack the key nodes. The attack effects of two-satellite key nodes and multi-satellite key nodes are verified, and the security performance of the whole satellite network against a DDoS attack is analyzed, which is of great significance to effectively deal with LSCN attacks.  
Writing-original draft preparation, Y.Z. ([PERSON]), Y.W. and Y.Z. ([PERSON]); writing-review and editing, Z.L., K.W. and L.K.; supervision, Y.H.; funding acquisition, Y.W., Q.Z. and L.W. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (62201592, 61901490, and 61671454), the Research Plan Project of NUDT (ZK21-33), the Young Elite Scientist Sponsorship Program of CAST (2021-JCJQ-QT-048), and the Macau Young Scholars Program (AM2022011).  
Not applicable.  
The authors declare no conflict of interest.
## 4 IoT for Geospatial Research
### Scope of Analysis
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**IoT-Based Method** & **Examples of Generalizations** \\ \hline \multirow{4}{*}{Participatory sensing} & Detecting emergency events at city scale [23], promoting neighborhood identity and local services [24], creating a noise map of a city [25], detecting outbreaks of dengue fever [48], developing heat maps from cyclists used for better city planning [45], producing a global spatial distribution of malaria risk [65]. \\ \cline{2-3}  & Proactively performing urban traffic monitoring [54], travel planning based on real-time traffic information [12]. \\ \hline \multirow{4}{*}{Fixed IoT sensors} & Urban decision-making assistance [13], wildlife monitoring and understanding of herd behavior [60], monitoring the area levels of air pollution [46], creating air temperature and precipitation maps [70], understanding fish-school characteristics around artificial reefs [67], estimating the level variations of the sand layer of sandy beaches or dunes [66]. \\ \hline \multirow{4}{*}{Satellite imagery} & Understanding how invasive species respond to landscape configuration relative to native species [49], assessing how the livestock agriculture affects the physical environment [35; 50], modeling forest fire risk zones [33], earthquake risk assessment [34], planning of tsunami evacuation [36], creating digital maps with information about bacteria habitats [47], delineating groundwater potential zones in hard rock terrain [39]. \\ \hline \multirow{4}{*}{Ground sensor sampling} & Estimating the Grand Canyon height map [63], generating high-risk floodplain maps [63], creating soil fertility maps [72], assessing the spatial variation of groundwater quality and producing salinity hazard maps [60], assessing the heavy metal pollution in soils [71], estimating the zinc contamination concentrations around a lake [68]. \\ \hline \multirow{4}{*}{Web-based IoT datasets} & Estimating traffic from historical traffic flows [42], optimizing routes of public transportation based on taxi rides [43], exploring and analyzing attractive areas [38], associating assault rates to measures of population and place characteristics [41]. \\ \cline{1-1} \cline{2-3}  & Assessing damage in Haiti by earthquake and facilitating emergency response [61], infrastructure asset management [58]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: IoT-based methods for generalization of analysis. environment [50]. Historical time series-based observations were critical for the forecasting of disasters such as flooding [32] and fire [33]. Finally, the time dimension in combination with spatial sensing allowed the acquisition of knowledge of mobile phone users' profiles, history needs and preferences [19], to provide better recommendations by remembering the user's actions in previous days, and proposing tourist attractions to visit [20]. Time and space allowed the P-DBSCAN platform to derive attractive places automatically based on photo density by tourists [38], and to analyze places and events by using collections of geo-tagged photos [37].
## 4 Results
### Evaluation  
The dewarping evaluation measure (DW) is a central part of the evaluation methodology proposed in the paper. It quantitatively assesses the performance of document image dewarping techniques by examining the degree to which text lines in a dewarped image are straightened, as it was described in [26]. The DW measure for the j-th text line is calculated using the ratio of the integrals of the curves from the warped and dewarped images:  
\[DW_{j}=\begin{cases}1-\frac{Ar_{j}}{Ar_{j}^{\prime}}&\text{if }\frac{Ar_{j}}{Ar_{j}^{ \prime}}<1,\\ 0&\text{otherwise}\end{cases} \tag{10}\]  
where \(Ar_{j}^{\prime}\) and \(Ar_{j}\) are the areas under the curves for the warped and dewarped images, respectively. To provide a holistic quantitative assessment, we define the DW as the mean value of all individual \(DW_{j}\) measurements:  
\[\mathbf{DW}=\frac{\sum_{j=1}^{N}DW_{j}}{N}\times 100\% \tag{11}\]  
where \(0\%\leq\mathbf{DW}\leq 100\%\). A higher DW value indicates better performance of the dewarping technique. The theoretical DW of our proposed method is calculated to be 100%. This is because, during the final alignment step, the function \(g_{2}(x)\) is completely flattened, resulting in a slope of zero. Consequently, the integral \(A_{2}\) equals zero. Therefore, since the slope is nullified in this final step, the resulting distortion is minimized, leading to a theoretical DW of 100%.  
\[DW_{j}=1-\frac{A_{2}}{A_{1}}=1-\frac{\int_{x_{1}}^{x_{2}}g_{2}(x)dx}{\int_{x_ {1}}^{x_{2}}g_{0}(x)dx}=1 \tag{12}\]  
From a more practical perspective, we conducted a third curve estimation on the final text line image. This involved fitting a curve to the processed text line and calculating the integral under this curve. The results based on our dataset are summarized in Table 2. As anticipated, all the dewarping evaluation measurements have yielded results exceeding 99.5%, aligning closely with the theoretical expectations outlined for our method. Additionally, we assessed the OCR accuracy both before and after the implementation of our proposed rectification algorithm. Prior to rectification, the OCR accuracy ranged between 16.61% and 49.37%. Following the application of the rectification technique, the OCR accuracy significantly improved, achieving results between 93.8% and 97.63%. It is important to note that the results presented are specific to this particular set of splines used  
\begin{table}
\begin{tabular}{c c c} \hline
**Input Image** & **Output Image** & **PaddleOCR** \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._in our examples. For different scenarios of text distortion, such as those involving a sine wave warping function, a different configuration of splines might be necessary to achieve comparable levels of rectification and OCR accuracy.