esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### The Adaptive Optimization Mechanism of PDEC  
We use the entity embeddings obtained from the model \(\Theta\) to update the edge vector set \(\
abla\) using Equation (2), and then generalize the polysemous predicates according to Equation (5), thereby achieving iterative optimization.  
To adaptively determine \(\eta\), for each iteration round, we order the Calinski-Harabasz index of the clustering results \(CL_{\theta}\), and determine the clustering quality threshold \(\eta\) according to the following rule:  
\[\frac{\sum_{c,j,(\mathbf{CL_{\theta}})>\eta_{a}\in\mathcal{R}(1)}}{\sum_{c,j, (\mathbf{CL_{\theta}})<-\eta_{a}\in\mathcal{R}(1)}}<0.8 \tag{9}\]  
That is, we perform predicate decomposition on the predicates that have an index size in the top 80%. An empirical optimal hyperparameter of 0.8 is obtained from experiments, and the relevant experimental results are presented in Figures 7-9.  
Subsequently, we use \(\mathcal{K}^{{}^{\prime}}_{train}\) and \(\mathcal{K}^{{}^{\prime}}_{valid}\) to retrain \(\Theta\) and complete an iteration round. After each round, we assess the changes in mean reciprocal rank (MRR). If the increase in MRR is less than the early-stopping threshold, we terminate the iteration.  
The algorithm process is outlined in Algorithm 1.  
```
0: Training set \(\mathcal{K}_{train}\), validation set \(\mathcal{K}_{valid}\), test set \(\mathcal{K}_{test}\), threshold of clustering quality \(\eta\), threshold of early stopping \(\lambda\)
0: The reasoning model \(\Theta\), trained using \(\mathcal{K}^{{}^{\prime}}_{train}\) and \(\mathcal{K}^{{}^{\prime}}_{valid}\) based on polysemous predicate induction. MRR and other indicators obtained from testing on \(\mathcal{K}_{test}\).
1: Train the initial model \(\Psi\) based on the original KG \(\mathcal{K}\).
2: Using \(\Psi\), calculate the entity embedding set \(\vec{E}=\{\vec{e}|e\in\mathcal{E}\}\) and predicate embedding set \(\vec{R}=\{\vec{r}|r\in\mathcal{R}\}\).
3:for\(t\gets 1\) to \(T\)do
4: Based on \(\vec{E}\), update the edge vector set \(\
abla\) using Equation (2);
5:for\(a\gets 0\) to \(|\mathcal{R}|\)do
6: Perform clustering on the edge vector set \(\
abla_{a}\) according to Equation (4);
7:if\(c\_{h}(\mathbf{CL}_{a,\theta})>\eta\)then
8: update \(split_{a}\) using Equation (5);
9:endif
10:endfor
11:endfor
12: Construct/update \(\mathcal{K}^{{}^{\prime}}_{train}\) and \(\mathcal{K}^{{}^{\prime}}_{valid}\) according to \(split_{a},a\in\mathcal{R}\);
13: Train the reasoning model \(\Theta\) based on \(\mathcal{K}^{{}^{\prime}}_{train}\) and \(\mathcal{K}^{{}^{\prime}}_{valid}\);
14: Based on \(\mathcal{K}_{test}\), use Equation (8) to calculate the performance of the model \(\Theta\) and record the results as \(M^{t}=MRR^{t},HIT1^{t},\ldots\)
15: update \(\vec{E}\)
16:if\(t==0||MRR^{t}-MRR^{t-1}>\lambda\)then
17: Continue
18:endif
```  
**Algorithm 1** KG reasoning based on polysemous predicate induction.
## 4 Results and Discussion
### Meso-Analysis of Blocking Behavior
#### 4.1.5 Location and Depth of Blockage  
According to the location of the final blockage, the blockage can be divided into two parts: Shallow clogging on the surface and deep clogging on the inside, and almost no clogging at the bottom of the layout. The main cause of shallow blockage on the surface is coarse-particle blockage. This part of the blockage is within 2 cm of the surface. It forms extremely quickly at the beginning of the blockage and, after it is formed, it is not easily washed away by water, and basically no longer moves. The main cause of deep internal blockage is small particle-sized sludge. This part of the blockage passes through the gap after the blockage of the coarse particle size above and gathers at the lower part, within 5 cm from the road surface. Staying at the corner of the gap and gradually gather, some will be washed away and moved and blocked by the water flow. In the repeated drying-wetting process, they harden into a stubborn blockage. In the actual clogging cleaning process, generally only part of the shallow surface clogging can be cleaned by suction, and it is very difficult to clean the deep internal clogging.
# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 2 BN Model for the Monitoring and the Error Recovery
### Diagnosis with BN  
BNs are probabilistic graphical models used to understand and control the behavior of systems [19]. The nodes of such a network represent random variables, the conditional probabilities of which are given by probability tables (CPTs). The arcs of the network indicate the conditional dependencies. The structure of the Bayesian network is defined by the nodes and the arcs of the corresponding graph. The parameters of the BN are the CPT of the nodes. A simple example of a BN is given in Figure 1. The node UAV Altitude (_U_A_) represents the altitude status describing whether the altitude of the UAV is increasing or decreasing. The nodes Barometer (_S_B_) and Altimeter (_S_A_) represent the sensors observing whether the UAV moves up or down. The node _U_A_ influences the sensor readings, hence there are arcs from _U_A_ to _S_A_ and _S_B_. The CPT tables for the sensor (_S_A_), for example, should be read as follows: if the status _U_A_ is \"increasing\", then the probability that it is reading an increasing value is 0.7.  
Figure 1: (**a**) Definition of a BN with its parameters (CPTs); (**b**) Illustration of the inference mechanism.  
The diagnosis is obtained by using an inference mechanism based on observations on \(S\_A\) or \(S\_B\), and to compute the posterior probabilities over \(A\) using [PERSON] theorem (Equation (1)).  
\[P(A|B)P(B)=P(A\cap B)=P(B|A)P(A) \tag{1}\]  
We use [PERSON] theorem for our example as follows (Equation (2)):  
\[\begin{split} P(U\_A=Inc|S\_A=Inc)=&\frac{P(S\_A= Inc|U\_A=Inc)P(U\_A=Inc)}{P(S\_A=Inc)}\\ =&\frac{0.7*0.5}{0.7*0.5+0.2*0.5}=0.778\end{split} \tag{2}\]  
So with the evidence on \(S\_A\), we can say that the probability of the altitude status to be \"increasing\" equals 0.778 as shown in Figure 1b. If for example, we add observations on the barometer giving an increasing value, the probability \(P(U\_A=Inc|S\_A=Inc,S\_B=Inc)\) increases to 0.969.  
During UAV missions, the evaluation of health status of the components of the system (i.e., sensors, actuators, etc.) is done from sensor data [20]. On the other hand, the Quality of Service (QoS) status of the applications which are executed on board, such as tracking application, can also be monitored to ensure mission success. Figure 2 illustrates the monitoring of the tracking application. For this application, we enumerate the different potential problems that can occur. In this example, one of potential problem is the vibration that can affect the quality of the tracking. The vibration can occur when IMU shows deviation or when strong wind is detected. This BN evaluates the QoS of the application based on evidence on IMU deviation or on wind detection as described in Figure 3.
# A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
## 3 System Model and Problem Description
### Task Queue Model  
The decision vector is defined as \(o(t)=\{o_{1}(t),o_{2}(t),\cdots,o_{M}(t)\}\), in which \(o_{i}(t)=\{o_{i}^{1}(t),o_{i}^{2}(t),\cdots,o_{i}^{N}(t)\}\cdot o_{i}^{k}(t)\) denotes the offload selection for time slot \(t\), which will be defined as  
\[o_{i}^{k}(t)\in\{0,1\},i\in\{1,2,\cdots,M\},k\in\{1,2,\cdots,N\}. \tag{1}\]  
The new task, \(A_{i}(t)\), is assigned to the \(k\)th satellite when \(o_{i}^{k}(t)=1,k\in\{1,2,\cdots,N\}\). Furthermore, the task will remain in the \(i\)th satellite for processing without being offloaded when \(o_{i}^{k}(t)=1,i=k\). At time slot \(t\), the quantity of data offloaded to the \(k\)th satellite is represented by the following equation  
\[D_{k}(t)=\sum_{i=1}^{M}o_{i}^{k}(t)*D_{i}(t),i\in\{1,2,\cdots,M\},k\in\{1,2, \cdots,N\}. \tag{2}\]  
Specifically, to indicate that each task can only be offloaded to one satellite, we define  
\[\sum_{k=1}^{N}o_{i}^{k}(t)=1,i\in\{1,2,\cdots,M\},k\in\{1,2,\cdots,N\}. \tag{3}\]  
Furthermore, each satellite can only receive one task at a time slot. It is expressed as  
\[\sum_{i=1}^{N}o_{i}^{k}(t)=1,i\in\{1,2,\cdots,M\},k\in\{1,2,\cdots,N\}. \tag{4}\]  
The computational capacity of the satellite is denoted as \(C_{k}(t)\)\((\text{bit/s})\). This denotes the processing capacity of the \(k\)th satellite, measured in terms of the amount of data it can handle per unit time.  
Then, at time slot \(t\), the amount of data in the \(k\)th satellite's task queue can be expressed as \(Q_{k}(t+1)\) and is given by the following expression  
\[Q_{k}(t+1)=\max[Q_{k}(t)-C_{k}(t)*\tau+D_{k}(t),0],k\in\{1,2,\cdots,N\}. \tag{5}\]  
Here, \(Q_{k}(0)\) is initialized as zero for all \(k\in\{1,2,\cdots,N\}\). Finally, we define the task queue stability requirement of the system [18] as follows:  
\[\lim_{T\rightarrow\infty}\frac{1}{T}\sum_{t=0}^{T-1}\mathbb{E}[Q_{k}(t)]\leq 0,k\in\{1,2,\cdots,N\}. \tag{6}\]
## 4 Simulation Results
### Comparison of the Network Energy Consumption
#### 4.6.1 Comparison of Network Energy Consumption  
In this paper, the BS is centered, and the area is divided into 4 cells. Then, we select the top 25% of nodes as the CHs in each cell and perform the establishment of an ICRM through the optimized Dijkstra algorithm to reduce network energy consumption and balance the energy consumption of  
Figure 18: Comparison of the number of rounds leading to decline in the network for each protocol.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **First Node Death** & **Coverage Degradation** & **Integrity Degradation** \\ \hline LEACH & 1061 & 1093 & 1072 \\ DEEC & 1135 & 1175 & 1149 \\ GSEN & 1163 & 1227 & 1172 \\ EECRP-HQSND & 1331 & 1333 & 1332 \\ EECRP-HQSND-ICRM & 1364 & 1367 & 1365 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the number of rounds leading to decline in the network for each protocol.  
the network nodes. As shown in Figure 19, compared with LEACH and DEEC, the GSEN has an advantage in reducing energy consumption. Compared to these three, the EECRP-HQSND effectively reduces the energy consumption of the network, which proves the effectiveness of the CH selection mechanism in this paper. In addition, EECRP-HQSND-ICRM further reduces the network energy consumption compared to EECRP-HQSND, reflecting the feasibility of the ICRM.
## 5 Conclusions and Future Research Work  
In DVC, a suitable and accurate correlation noise model plays a crucial role in improving the RD performance and coding efficiency. As in DVC, neither WZF is available at the decoder, nor is the estimated SI of the corresponding WZF available at the encoder. Therefore, online estimation of the CNM and its parameters is quite tricky, especially when the motion vector is not estimated at the decoder such as in [15]. The proposed novel online CNM approach is suitable for such a scenario and accurately calculates the error distribution and makes the coding of the codec highly efficient. Higher coding efficiency and PSNR gain lead to better RD performance. The DIVCOM codec achieves coding efficiency up to 8.05 kbps, and PSNR ranges from 0.0245 dB to 0.18 dB compared to the DIS codec. The PDIVCOM achieves a coding efficiency of up to 10.9 kbps, and PSNR ranges from 0.019 to 0.17 dB compared to DIS.  
Even so, there is still room for improvement. During the frame-by-frame analysis, a higher coding rate is observed at some frames of every video sequence. A closer analysis determined that for some frames, the variance was around 0.1, and the channel-coding rate achieved under such conditions was too high. Along with the high channel-coding rate, all errors in bands of such frames were not completely corrected. By further investigating those frames at band level, a higher bit rate is also noticed for most bands of those frames. The CNM parameter (\(\alpha_{f}\)) estimation process can further be enhanced by improving the mathematical formulation by considering the variance. This could potentially improve the coding efficiency. Therefore, rectifying the CNM parameter at the band level enhances the coding rate and the overall RD performance. For band-level CNM, we may send more than one sample of coefficient values of a band instead of the mean of coefficients of a band.  
Conceptualization, S.K., N.B. and V.J.; methodology, S.K., N.B. and V.J.; validation, N.B., V.J. and M.A.H.; writing--original draft preparation, S.K.; writing--review and editing, N.B., V.J. and M.A.H.; supervision, N.B., V.J. and M.A.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Institute of Health and Analytics, Universiti Teknologi PETRONAS.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Video**} & \multicolumn{2}{c}{**BD-Rate**} & \multicolumn{2}{c}{**BD-PSNR**} \\ \cline{2-5}  & **DIVCOM vs. DIS** & **PDIVCOM vs. DIS** & **DIVCOM vs. DIS** & **PDIVCOM vs. DIS** \\ \hline Coastguard & \(-6.13\) & \(-6.27\) & 0.14 & 0.14 \\ \hline Akiyo & \(-3.46\) & \(-11.37\) & 0.113 & 0.35 \\ \hline Foreman & \(-1.49\) & 1.33 & 0.05 & \(-0.026\) \\ \hline Hall & \(-2.46\) & \(-3.47\) & 0.18 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of BD-Rate and BD-PSNR between DIVCOM and PDIVCOM with DIS.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 6 State-of-the-Art of CR-Based UAVs
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline
**Publication** & **Q1** & **Q2** & **Q3** & **Q4** & **Q5** \\ \hline [PERSON] et al. [95] & & & & & \\ \hline [PERSON] and [PERSON] [27] & & & & & \\ \hline [PERSON] and [PERSON] [96] & & & & & \\ \hline [PERSON] et al. [97] & & & & & \\ \hline [PERSON] et al. [98] & & & & & \\ \hline [PERSON] et al. [10] & & & & & \\ \hline [PERSON] et al. [99] & & & & & \\ \hline [PERSON] et al. [100] & & & & \\ \hline [PERSON] and [PERSON] [101] & & & & \\ \hline [PERSON] et al. [102] & & & & \\ \hline [PERSON] et al. [9] & & & & \\ \hline [PERSON] et al. [103] & & & & \\ \hline [PERSON] et al. [15] & & & & \\ \hline [PERSON] et al. [104] & & & & \\ \hline [PERSON] et al. [105] & & & & \\ \hline [PERSON] et al. [106] & & & & \\ \hline [PERSON] et al. [107] & & & & \\ \hline [PERSON] and [PERSON] [108] & & & & \\ \hline [PERSON] et al. [109] & & & & \\ \hline [PERSON] et al. [110] & & & & \\ \hline [PERSON] et al. [111] & & & & \\ \hline [PERSON] et al. [112] & & & & \\ \hline [PERSON] et al. [113] & & & & \\ \hline [PERSON] et al. [114] & & & & \\ \hline [PERSON] et al. [115] & & & & \\ \hline [PERSON] et al. [116] & & & & \\ \hline [PERSON] et al. [117] & & & & \\ \hline [PERSON] et al. [118] & & & & \\ \hline [PERSON] et al. [119] & & & & \\ \hline [PERSON] et al. [120] & & & & \\ \hline Dunne and Keenlance [121] & & & & \\ \hline [PERSON] et al. [122] & & & & \\ \hline [PERSON] et al. [51] & & & & \\ \hline [PERSON] et al. [123] & & & & \\ \hline [PERSON] et al. [124] & & & & \\ \hline [PERSON] et al. [125] & & & & \\ \hline [PERSON] et al. [126] & & & & \\ \hline [PERSON] et al. [127] & & & & \\ \hline [PERSON] et al. [128] & & & & \\ \hline [PERSON] [129] & & & & \\ \hline [PERSON] and [PERSON] [130] & & & & \\ \hline [PERSON] et al. [131] & & & & \\ \hline [PERSON] et al. [132] & & & & \\ \hline [PERSON] et al. [133] & & & & \\ \hline [PERSON] et al. [134] & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Reviewed publications and their explored topics. On the columns Q1 to Q5, cells filled in gray color represent “yes” for the respective column, and “no” otherwise. In general, these works can be separated in two groups: one using SDR-based UAVs for different applications, and another studying the impact of integrating CR technology with UAVs, mainly with theoretical analysis and simulation results. A notable exception to this is [PERSON] et al. [119]. The authors designed, developed, and validated through real indoor and outdoor experiments a CR platform for UAVs. Their experiment involves on-ground CR nodes interacting with a CR-based UAV, and it shows that their proposed platform optimizes wireless networks subject to interference, thus maximizing their throughput. To the best of our knowledge, their work presents the first CR-based UAV real experiment in the literature.
## 4 Materials and Methods
### RNA Purification and qRT-PCR Analysis  
Exponentially grown cultures of the wild-type strain BW25113, its \(\Delta\)_srrA_ derivative SR19957, SR19761 (BW25113+vector pCA24N) and SR14857 (BW25113+psrrA\({}^{+}\) in pCA24N) were used to extract total RNA. For comparison of wild type and its \(\Delta\)_srrA_ derivative, cultures were grown at 37 \({}^{\circ}\)C in LB and adjusted to an optical density OD\({}_{600}\) of 0.05. Cultures were allowed to grow up to an OD\({}_{595}\) of 0.2 and were then harvested by centrifugation. To quantify changes in the mRNA abundance when the expression of the _srrA_ gene is induced, the bacterial culture of SR19761 and SR14857 were grown at 30 \({}^{\circ}\)C in LB-rich medium to an OD\({}_{595}\) of 0.1. Prior to heat shock, IPTG at the final concentration of 75 \(\upmu\)M was added. Aliquots were immediately shifted to a prewarmed medium held at 43 \({}^{\circ}\)C and incubated for another 15 min. Equivalent amounts of culture samples were collected from cultures grown at 30 \({}^{\circ}\)C and after the heat shock of 43 \({}^{\circ}\)C, they were harvested by centrifugation. Total RNA was extracted using hot phenol extraction as described previously [75]. Purified total RNA was treated RQ1 DNase (Promega, Madison, WI, USA) to remove any genomic DNA and ethanol precipitated. Pellets were resuspended in DEPC-treated water. RNA was subjected to another round of purification using the GeneElute universal total RNA isolation kit from Sigma Aldrich, St. Louis, MO, USA. RNA amounts were quantified and their integrity verified using agarose gel electrophoresis. q-RT-PCR was used to quantify changes in the _clsA_ gene expression, using gene-specific primers. Two \(\upmu\)g of purified mRNA was converted to cDNA using Maxima H-Minus Reverse Transcriptase (Thermo Scientific). Reactions were conducted for 40 cycles using PowerUp SYBR(r) Green PCR Master Mix (Thermo Scientific), as described previously [6]. Q-RT-PCR was performed using the CFX Connect Real-Time PCR Detection System (Bio-Rad, Warsaw, Poland). Data were analyzed by software Bio-Rad CFX Maestro 2.3.
# Production Planning Problem of a Two-Level Supply Chain with Production-Time-Dependent Products
## 4 Computational Experiments  
In this paper, we evaluated the suggested algorithm through computational experiments on a number of problem instances. These problem instances were generated randomly based on real information from a poultry company in Korea. Three levels of the number of supplies (\(I\) = 10, 12, and 14) and three levels of the length of planning horizon (\(T\) = 6, 8, and 10) were considered. For the nine combinations of (\(I\) and \(T\)), 10 instances were generated (totally 90 instances). Based on the information from the poultry company, we considered three types of semi-finished products with different processing time (\(L\) = 3, 4, and 5), and the numbers of candidate production line are three for all suppliers (\(J\) = 3). Other data were generated as follows. Here, \(U(a,b)\) represents the discrete uniform distribution with range \([a,b]\). 1. Demand was generated from \(U(500,800)\) for all types of product at each period;
2. The capacities of the three production lines at a supplier were generated from \(U(100,200)\), \(U(200,300)\), and \(U(300,400)\), and setup costs for these production lines were generated from \(U(500,1000)\), \(U(1000,1500)\), and \(U(1500,2000)\), respectively;
3. The production cost of each type of semi-finished products of a supplier were generated from \(U(10,15)\), \(U(20,25)\), and \(U(30,35)\). The transportation costs between a supplier and the manufacturing plants were generated from \(U(5,10)\), \(U(10,15)\), and \(U(15,20)\) for semi-finished product types 1, 2, and 3, respectively. The heuristic algorithm was coded in Java programming language, and a commercial solver CPLEX was used for the linear and integer programs. We set the computation time limit to 15,000 s for an instance to prevent prolonged execution time. All tests were operated on a personal computer with AMD Phenom II (3.0-GHz) and 3 GB RAM. Results of the test are given in Tables 1-3, which show solution values of the heuristic and CPLEX, CPU times, and percentage errors of the heuristic solutions from the solutions (or lower bounds) obtained by CPLEX. As can be seen from Table 1, for only 4 instances (with ten suppliers) out of 30 instances, the percentage errors were larger than 4%. Tables 2 and 3 show similar results for larger instances. These results demonstrate the performance of the suggested heuristic algorithm that can give reasonably good solutions regardless of the length of the planning horizon. Additionally, the heuristic shows almost the same performance regardless of the number of the suppliers. The overall average percentage error (PE, %) of the 90 instances was 2.43%. Figure 1 shows the summary of the results of percentage errors according to different number of periods and suppliers. As shown in the figure, we could not find significant correlation between problem size and the percentage error.