esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measurement Principles and Methods
### Sub-Pixel Edge Detection and Fitting
#### 2.1.4 Simulation Experiment  
This comparative analysis evaluates the three methods discussed above through simulation. Firstly, a slanted-edge image, measuring 30 pixels in both length and width, is selected. The results of applying these three sub-pixel edge detection methods are illustrated in Figure 4.  
Without considering random noise, Table 1 shows that all three sub-pixel edge detection accuracies are high, with the interpolation method having an error of less than 0.06 pixels, while the GLCM and fitted methods have significantly higher accuracies than the interpolation method, with an error of less than 0.03 pixels.  
Gaussian noise, with a noise standard deviation ranging from 0.001 to 0.010 in increments of 0.001, was added to the simulated image for blurring purposes. Subsequently, the root-mean-square error (RMSE) between the measured and theoretical values of the GLCM, BLI, and HTF detection methods was calculated under varying noise conditions. The results of these calculations are depicted in Figure 5.  
The introduction of varying levels of Gaussian noise reduces the accuracy of the three methods under consideration. Specifically, the accuracy of the BLI method is significantly compromised by random noise, with edge detection errors escalating rapidly from 0.06 pixels to 0.7 pixels post-noise addition. Conversely, the GLCM and HTF methods exhibit more resilience to random noise, with errors incrementally rising to only 0.1 pixels. Notably, the GLCM method demonstrates commendable accuracy, comparable to the fitting method. However, its shorter processing time makes it particularly suited to real-time detection requirements. Consequently, this paper opts for the GLCM method, balancing accuracy with operational efficiency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Method** & **GLCM** & **BLI** & **HTF** \\ \hline Angle deviation MEAN & 0.063718 & 0.044824 & 0.038874 \\ Angle deviation SD & 0.043189 & 0.02798 & 0.028086 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean and standard deviation of sub-pixel edge deviations calculated by the three methods.  
Figure 4: The calculated results for three sub-pixel edge detection methods are as follows: (**a**) the slanted-edge image; (**b**) the deviation between the calculated and theoretical edge positions.
## 4 Challenges and Future Research Areas
### Airframe Shadowing and UAV Machinery
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Refs.** & **Methodology** & **Frequency** & **Signaling** & **Environment** & **Delivered Research** \\ \hline
[47] & Simulations & 2.4 GHz & \
\begin{tabular}{c} BW = 100 MHz \\ P\({}_{\text{Tx}}\) = 15 dBm \\ \end{tabular} & Urban &
\begin{tabular}{c} Machine-learning path-loss prediction \\ (RF, kNN algorithms) \\ \end{tabular} \\ \hline
[48] & Simulations & \begin{tabular}{c} 800 MHz \\ 2.4 GHz \\ \end{tabular} & \
\begin{tabular}{c} BW = 100 MHz \\ \(\text{P}_{\text{Tx}}\) = 0 dBm \\ \end{tabular} & Urban/dense &
\begin{tabular}{c} Path-loss model (CI) \\ \end{tabular} \\ \hline
[50] & Theoretical & \
\begin{tabular}{c} 2.4 GHz \\ 5.8 GHz \\ \end{tabular} & - & Urban &
\begin{tabular}{c} Path-loss model (SUI extension) \\ Excess loss due to Doppler \\ \end{tabular} \\ \hline
[53] & Theoretical & \
\begin{tabular}{c} 5.8 GHz \\ Simulations \\ \end{tabular} & 5.8 GHz & - & Dense urban &
\begin{tabular}{c} Path-loss model \\ Two-ray + knife-edge diffraction \\ \end{tabular} \\ \hline
[55,56] & Stochastic (GBSM) & 3 GHz & - & Cylinder model &
\begin{tabular}{c} Time-frequency correlation \\ functions, Doppler spectrum, \\ channel stationarity \\ \end{tabular} \\ \hline
[57] & Stochastic (GBSM) & \
\begin{tabular}{c} 2, 2.5, 5.8 GHz \\ \end{tabular} & - & Ellipsoid &
\begin{tabular}{c} Space-time correlation function, \\ Doppler spectrum, \\ channel stationarity \\ \end{tabular} \\ \hline \hline \end{tabular}  
\(\text{P}_{\text{Tx}}\): transmitted power.  
\end{table} 
Table 3: Relevant theoretical studies and simulations for the A2A communication channel. investigated, because the existing results are related to low frequencies, thus emphasizing Ka and Ku bands, where Doppler effects are expected to be more intense. Engines and ambient phenomena can also induce jittering, which directly affects the delay spread of the channel (the channel becomes more frequency-selective). Therefore, it is necessary to deepen knowledge on this topic by taking into consideration the physical structure of the air vehicle, its stability during flight (flight dynamics), the impact of the engine (single- or multi-rotor, jet), the position and type of the airborne antenna, and additional electronic payloads. Extensive measurements and thorough theoretical studies are then pivotal in order to evaluate those challenging effects, especially for unmanned UAVs at low and higher altitudes.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 4 Results and Discussion  
To validate the effectiveness of the cluster-based mitigation algorithm in detecting and counteracting jamming attacks, three different test scenarios are proposed. These scenarios analyze three key mitigation metrics: retransmission rate, energy consumption, and routing resilience. The experiments are conducted in a university campus environment, with sensor nodes deployed in various locations such as classrooms, offices, laboratories, and outdoor areas. Each test scenario introduces a jammer node to simulate a targeted attack while evaluating how the network adapts through clustering mechanisms.
## 4 RADC: Resource Allocation Based Data Classification  
### System Model  
The system model is much more focused on limited network environments, such as academic and industrial networks, etc. In these types of networks, each area consists of a network administrator, and a set of nodes and server(s). For instance, Figure 2 illustrates a network with four areas denoted \(Area=\{Area_{i}|i\in[1.4]\}\). Routers in the same network area sent periodically the sum of each consumed class to the administrator side. Similarly, the network administrator will calculate the allocated partitions for each class according to the utility function given in Section 4.3. The communication between administrator/routers of the same area can be performed using the IP layer in the NDN hourglass [3]. To classify the received contents, each router in the network will use the proposed classification model (Section 4.2) to assign the contents to the corresponding classes. The nodes outside those networks use the default caching mechanism used in NDN.
## 3 Datasets
### Data Preparation  
After the data collection is the data preparation process, which includes collecting, cleaning, filtering, and consolidating data into one file or data table. The cleaning and filtering of the raw data are primarily completed to prepare for the follow-up works--namely the annotation of the posts and comments in the dataset and the training model for Odia hate speech detection. The tasks performed in the data preparation process are listed below:  
1. Remove all non-Odia, non-English, and non-textual posts and comments;
2. Remove all null, blank value, and whitespace;
3. Filter data using keywords that are an indicator of hate and offensive language;
4. Join data of each page into one dataset;
5. Remove duplication to ensure the uniqueness of each text in the dataset.  
All of the above preparation tasks consider the nature and behaviors of the Odia language. The context of each text in a dataset is kept for annotation processes. The keywords and offensive words are gathered for filtering purposes, from university students, and also from different social media user pages known for using highly offensives words.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**No** & **Categories Name** & **Description** \\ \hline
1 & News media and broadcasting pages & Delivering news to the general public or a target public. \\ \hline
2 & Bloggers’ and journalists’ pages & A person who regularly writes for blogs, newspapers, magazines. \\ \hline
3 & Religious media and religious group pages & Delivering religious news, religious teachings to the followers of that religion or a target group. \\ \hline
4 & Political party, politician and government & A group of people who come together to contest elections. A group of politicians who have held the power of government. \\ \hline
5 & Public figure: artists’ and authors’ pages & A person or public figure who creates song, painting or drawing, and a writer of a book, article, or document. \\ \hline
6 & Activists’, general or interest community pages & A person who campaigns to bring about political or social change, and pages promoting different issues on political, religious, ethnic and other social issues. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected Social Media Page Categories.  
The authors collected 27,422 posts. The total number of unique post and comment filtered were 27,162.
## 7 Feature Extraction Approaches
### Subspace Learning-Based Approaches
Their method first constructs a fuzzy membership matrix using the fuzzy k-nearest neighbors algorithm (FKNN), computed separately for within-class and between-class weight matrices to encode intra-personal and inter-personal variations. Two theorems are subsequently derived to efficiently obtain the generalized eigenfunctions for optimized class separation. Finally, elastic net regularization is utilized to determine the optimal sparse projection matrix. [PERSON] et al. (2022) [57] proposed an innovative approach known as double-cohesion learning-based multi-view and discriminant palmprint recognition (DC_MDPR). This approach effectively exploits the multi-view features of palmprint images along with the inherent data structure. To achieve this goal, they introduced a method called double cohesion, which combines inter-view cohesion and intra-class cohesion. This technique aims to enhance the distinctiveness of multiple features, reduce feature dimensions, and enhance the representation of these features within the same subspace. [PERSON] et al. (2023) [58] proposed an approach known as low-rank two-dimensional local discriminant graph embedding (LR-2 DLDGE) for the purpose of feature extraction and dimensionality reduction. Initially, the technique uses a graph embedding (GE) framework to capture and preserve essential discriminative information within local neighborhoods of the data. Next, LR-2 DLDGE is designed to ensure that data points within the feature space are maximally independent across different classes, thereby enhancing discriminative capabilities. To bolster the method's resilience against noise and corruption, the approach incorporates an L1 norm constraint and employs low-rank learning techniques. Table 4 provides a summary of the studies discussed in this subsection, elucidating the employed feature extraction methods, datasets, experimental protocols, and principal findings.
## 5 Simulation and Discussion
### Day-Ahead DRO Model Simulation Analysis
#### 5.2.2 Determining Optimal Fixed Hydrogen Blending Ratio  
To determine the optimal value of the fixed hydrogen blending ratio, a sensitivity analysis of the blending ratio \(r\) is performed.The blending ratio \(r\) is optimized within the range of 0 to 0.2 with a step size of 0.005. Figure 5 shows the relationship between the blending ratio and the total cost. From the magnified dip in the graph, it is clear that when \(r\) is set to 0.16, the total cost reaches its minimum value. This indicates that a hydrogen blending ratio of \(r=0.16\) is the optimal choice for scheduling. Therefore, this paper uses 0.16 as the fixed hydrogen blending ratio in Case 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\alpha_{\text{h}}\) & 0.234 t/MWh & \(\lambda\) & 250 CNY/t \\ \(\alpha_{\text{c}}\) & 269 kWh/t & \(\alpha\) & 0.25 \\ \(\alpha_{\text{t}}\) & 0.389 t/MWh & \(l\) & 2 t \\ \(\beta_{\text{e}}\) & 0.680 t/MWh & \(a_{1},b_{1},c_{1}\) & 36, 0.38, 0.0034 \\ \(\beta_{\text{h}}\) & 0.367 t/MWh & \(a_{2},b_{2},c_{2}\) & 3, 0.004, 0.001 \\ \hline \end{tabular}
\end{table}
Table 3: Relevant parameters of the stepped carbon trading mechanism.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Number** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Probability & 0.0745 & 0.0727 & 0.0878 & 0.0857 & 0.1577 & 0.1539 & 0.1858 & 0.1814 \\ Total Cost (CNY) & 15,320.11 & 15,241.20 & 15,246.96 & 15,214.02 & 15,236.39 & 15,207.23 & 15,237.09 & 15,207.92 \\ \hline \end{tabular}
\end{table}
Table 5: Scenario probabilities for 8 typical scenarios and the cost distribution for Case 4.
## 4 Proposed CLCQ Protocol  
### Q-Learning-Based Routing Protocol  
Q-learning is a model-free reinforcement learning algorithm which learns the optimal actions by interacting with the environment and estimating the long-term value of actions in specific states [30]. The objective of Q-learning is to identify the optimal policy that maximizes the expected future rewards. The complexity of the underwater acoustic channel and the uncertainty of the environment make it challenging to obtain a large number of real environmental data. Q-learning is capable of modifying the node behavior strategy in real time based on the current network status and data characteristics. This makes it a  
Figure 4: OFDM underwater acoustic communication system implementation process.  
Figure 5: BER at different receiving depths and distances (transmitting depth 800 m).  
highly versatile solution for UACNs that frequently change. The fundamental principle underlying Q-learning is the utilization of the Bellman equation to iteratively update the Q-function until it converges to the optimal Q-value. The updated formula for the Q-function can be expressed as follows:  
\[Q^{\pi}(s_{i},a_{i}) = r_{i}+\gamma\sum_{s_{j}\in\mathrm{S}}P_{s_{i}s_{j}}^{a_{j}}Q^{\pi }(s_{j},a) \tag{8}\]  
where \(s_{i}\) and \(s_{j}\) represent the current state and the next state, respectively. \(a_{i}\) represents the action taken, and \(r_{i}\) represents the immediate reward for the current action. \(\gamma\in(0,1)\) denotes the discount factor, and \(P_{s_{i}s_{j}}^{a_{i}}\) is the transition probability from state \(s_{i}\) to state \(s_{j}\) through action \(a_{i}\). Ref. [31] has proven that there exists at least one optimal policy \(\pi^{*}\) that can achieve the optimal value, which can be described as follows:  
\[V^{*}(s) = \max_{a}(Q^{*}(s,a)) \tag{9}\] \[Q^{*}(s_{i},a_{i}) = r_{i}+\gamma\sum_{s_{j}\in\mathrm{S}}P_{s_{i}s_{j}}^{a_{i}}V^{*} (s_{j}) \tag{10}\]  
Among these, \(Q^{*}(s_{i},a_{i})\) represents the expected return of taking action \(a_{i}\) under the optimal policy in the state \(s_{i}\). The protocol framework that is the subject of this study is depicted in Figure 6. In order to calculate the value of the reward function, nodes must obtain information from the environment, such as the remaining energy of neighboring nodes. The agent then evaluates the actions for candidate nodes and sends the data packet to the next hop probabilistically. This method fully exploits the information transfer between the network layer, data link layer, and physical layer within the network protocol stack to achieve efficient routing in practical applications.
# Dynamic Changes in the Export Competitiveness of the South Korean Oil Refining Industry
## 5 Discussion of the Results  
This study analyzed export competitiveness using import and export data for the South Korean oil refinery industry from 2000 to 2019, and the main results are as follows. First, the TSI analysis confirms that diesel and kerosene were close to complete export specialization (TSI = 1) during this period, and lubricants were quickly established as export-specialized items. On the other hand, naphtha was an import-specialized item for the entire period, and its degree of import-specialization is on the rise. Bunker C oil was converted from an export-specialized item to an import-specialized item after 2012, while aviation gasoline was converted from an import-specialized item to an export-specialized item after 2014.  
Second, the RCA index analysis shows that diesel, automobile gasoline, jet oil, lubricating oil, and naphtha have comparative advantages, whereas kerosene, bunker A oil, bunker B oil, bunker C oil, and aviation gasoline have comparative disadvantages. Among the comparative advantage groups, changes in diesel and automobile gasoline are very dramatic. The RCA index of diesel increased 2.7 times from 4.665 in 2002 to 12.763 in 2019. The RCA index of automobiles gasoline also significantly improved the RCA index from 1.552 to 10.204 during the same period. Both diesel and automobile gasoline have been supported by strong domestic demand, which enables oil refineries to secure economies of scale, and compete successfully with global oil refineries.  
Third, a comprehensive analysis of the competitiveness of each petroleum product using a \(2\times 2\) matrix of the TSI and the RCA index shows that diesel, automobile gasoline, jet oil, and lubricant oil belonged to the 'high competitiveness group' for the entire period, whereas naphtha belonged to the 'import-specialized group with comparative advantage' for the entire period. 'High competitiveness group' could maintain their export competitiveness by actively introducing advanced facilities that extract high value-added light oil. On the other hand, naphtha has a comparative advantage, but production capacity could meet domestic demand because South Korea is specialized in the petrochemical industry and naphtha is a major ingredient of petrochemical products.  
The portfolio classification analysis presented in this study is useful in deriving major implications for fostering the oil refining industry. For example, in the case of 'the Import-specialized group with comparative advantage' to which naphtha belongs, it has a comparative advantage in exports, but since production does not meet domestic consumption, government support for investment in expanding production facilities is needed. On the other hand, since the low competitiveness group is in a situation where the existence of the industry is threatened, it is urgent to achieve economies of scale through market restructuring and introduce advanced facilities.
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 3 Network Model and Problem Statements
### Network Model  
For our network model, we consider a \(100\times 100\) m network space in which multimedia sensor nodes are randomly and uniformly scattered. In the clustered sensor networks, there are three types of nodes: CH, CM, and sink nodes:  
CH nodes: These nodes are responsible for communication with the sink nodes. They receive the collected data from other member nodes in the cluster and do not participate in data acquisition when they act as CHs. The role of being a CH rotates among the nodes and is selected optimally using game theoretic computations. There are limited number of CHs in a network adhering to other member nodes in the cluster that are within the range.  
CM nodes: These nodes are responsible for collecting data from the network. Periodically, they report to the CHs and do not transmit data to the sink nodes directly. For our network, CMs compete for the FoV of the object and depending on their energy level, one of the two competing nodes wins and transmits the data to the CH.  
Sink nodes: These nodes receive data from the CHs and have higher power level and computation capacity. Sink nodes also act as a gateway to connect to the outer world, and users may directly request data fetched from them remotely. There are three sink nodes in the network. CHs transmit data to the sink nodes based on the distance.  
Multi-sink network architecture: A sensor node is usually assumed to be covered by a sink node. However, a sensor node is uncovered by a sink node if the received signal strength is below a certain threshold level. In this paper, the process of deciding the number of sink nodes is carried out iteratively. Initially, it is assumed that none of the sensor nodes is covered by a sink node and the set of the sink nodes is empty. Then, the number of the sink nodes is iteratively increased one by one so that every sensor node has dual paths to two sink nodes for providing redundant paths against sink failure. To ensure all the sensor nodes in the network to have dual paths to two different sink nodes, we found out that three sink nodes are necessary in our simulation study. It will be described in Section 5.1.  
Figure 1a-c shows the network scenarios wherein multiple sink nodes have been deployed. In Figure 1a, sink nodes broadcast to the CH nodes, and based on the RSSI value, the CH will decide which sink it should choose to transmit the collected data to. In Figure 1b, CHs in different clusters transmit  
data to their nearest sink node for faster and reliable data delivery. The dashed circles represent a cluster, black circular nodes represent CMs, blue circular nodes represent CHs, and quadrilaterals represent sink nodes. The role of a sink node is of utmost importance in a WMSN because the entire network is useless without it. Failure of a sink node is not desired and use of multiple sinks can increase reliability greatly. Figure 1c shows that even if a sink node fails/runs out of energy, the WMSN can complete its task.
# A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
###### Abstract  
As multiprocessor (or multicore) real-time systems become popular, there has been much research on multiprocessor real-time scheduling algorithms. This work evaluates EDZL (Earliest Deadline until Zero Laxity), a scheduling algorithm for real-time multiprocessor systems. First, we compare the performance of EDZL schedulability tests. We measure and compare the ratio of task sets admitted by each test. We also investigate the dominance between EDZL schedulability tests and discover that the union of the demand-based test and the utilization-based test is an effective combination. Second, we compare the schedulability of EDZL and EDF\({}^{(k)}\). We prove that the union of the EDZL schedulability tests dominates the EDF\({}^{(k)}\) schedulability test, i.e., the union of the EDZL schedulability tests can admit all task sets admitted by the EDF\({}^{(k)}\) schedulability test. We also compare the schedulability of EDZL and EDF\({}^{(k)}\) through scheduling simulation by measuring the ratio of successfully scheduled task sets. EDZL can successfully schedule 7.0% more task sets than EDF\({}^{(k)}\).  
multiprocessor; real-time; schedulability; EDZL; EDF\({}^{(k)}\); dominance +
Footnote †: journal: Applied  
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