esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network-Based Bilingual Lexicon Induction for Indonesian Ethnic Languages
## 3 Materials and Methods  
### A Neural Network Approach  
We introduce a neural network approach to extract transformation rules or patterns from the Indonesian to Minangkabau language [15]. The first approach uses character-level one-hot embedding, where words will be separated as characters, and each vector has the same length size adjusted by total characters. Then, the sequence-to-sequence (seq2 seq) model, which has two RNN encoders and decoders, is utilized. Bi-LSTM as the encoder and LSTM as the decoder were used in this research. The Bi-LSTM encoder processes the word in the source language (Indonesian), character by character, and produces a representation of the inputted words. The LSTM decoder takes the output of the encoder as the input and produces a representation, character by character, in the target language (Minangkabau). Similar to the first method, the second method employs a sequence-to-sequence model. The distinction is in the inputted words, which are tokenized using SentencePiece with byte pair encodings for the input to the encoder-decoder in the sequence-to-sequence model. The tokenization involves splitting the words into chunks of characters.  
The secondary data were obtained from [PERSON] et al. [2] and [PERSON] and Koto [16] with a total of 13,761 translation pairs. Pre-processing the data was completed by deleting duplicate word pairs and constructing an array of word pairs in the form of a data-type dictionary given by Python. In this case, various Indonesian to Minangkabau word pairings have several meanings. A dictionary is made up of a set of key-value pairs. Each key-value pair corresponds to a certain value [PERSON] et al. [17]. The data were validated by Minangkabau native speakers after the duplicate data were removed. As a result, there were 10,278 translation pairs in the complete set of data. The model's performance was evaluated using five-fold cross-validation with precision as the evaluation metric. Precision measures the extent to which the positive predictions from a classification model are correct. Precision calculates the percentage of true positive (TP) predictions compared to the total positive, i.e., true positive (TP) + false positive (FP) predictions made by the model, as shown in Equation (1).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]
# Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics
## 4 Experiments
### Robot Experiments  
The iCub model inverse kinematics solution has been tested on the real robot in order to verify the feasibility of the computed configuration. The experimental setup involves an iCub robot fixed on a pole, as shown in Figure 5, controlled in position through low-level PID running at 1 kHz. The reference joint position is computed real-time using the dynamical inverse kinematics on the robot model, and the data are sent to the robot at the frequency of 50 Hz.
## 4 Simulation Results
### Case 5: Active power loss  
To reduce transmission lines active power loss, we use the objective function given in Equation (19). Simulation results, illustrated in Table 4, show that the power loss using HIC-GWA is 2.61 (MW). Compared with existing optimization methods in Table 9, HIC-GWA has significantly reduced the power loss.  
In cases 3, 4, and 5, the proposed metaheuristic showed a better exploration than recently published optimization methods that appear to be stuck at a local minimum.
## 4 Results
The X-axis represents weighted degree centrality, indicating the strength of connections for each node. The Y-axis represents closeness centrality, showing the accessibility of each node within the network. The size of the points reflects betweenness centrality and the colors represent industries (1 st: yellow, 2 nd: orange, 3 rd: red). Each cluster in the visualization corresponds to a distinct industrial group. For example, Primary industries include logistics  
Figure 3: Industry definition using cluster result. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Industry Definition**} & \multirow{2}{*}{**Node**} & **Weighted Degree Centrality** & **Closeness Centrality** & **Betweenness Centrality** \\ \hline
1 & Other transportation equipment & \(9\times 10^{6}\) & \(3\times 10^{5}\) & \(4\times 10^{-2}\) \\ \hline
3 & Etc & \(1\times 10^{7}\) & \(4\times 10^{5}\) & \(1\times 10^{-1}\) \\ \hline
3 & Cultural and travel-related services & \(1\times 10^{7}\) & \(3\times 10^{5}\) & \(2\times 10^{-2}\) \\ \hline  & Production and distribution of video & & & \\
1 & and audio products & & & \\ \hline
1 & Computers and peripherals & \(2\times 10^{7}\) & \(4\times 10^{5}\) & \(9\times 10^{-2}\) \\ \hline
3 & Social groups & \(2\times 10^{7}\) & \(4\times 10^{5}\) & \(0\times 10^{0}\) \\ \hline
1 & Other electronic components & \(3\times 10^{7}\) & \(4\times 10^{5}\) & \(3\times 10^{-2}\) \\ \hline
3 & Public administration, national & & & \\ defense and social security & \(5\times 10^{7}\) & \(5\times 10^{5}\) & \(0\times 10^{0}\) \\ \hline
1 & Coal, crude oil, and natural gas & \(1\times 10^{8}\) & \(4\times 10^{5}\) & \(0\times 10^{0}\) \\ \hline
1 & Electrical equipment & \(1\times 10^{8}\) & \(4\times 10^{4}\) & \(6\times 10^{-4}\) \\ \hline
2 & Cigarettes & \(2\times 10^{6}\) & \(2\times 10^{5}\) & \(0\times 10^{0}\) \\ \hline
2 & Electric power and renewable & & & \\ energy & \(2\times 10^{7}\) & \(2\times 10^{5}\) & \(0\times 10^{0}\) \\ \hline
3 & Semiconductors & \(8\times 10^{7}\) & \(5\times 10^{5}\) & \(2\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Centrality measures of potential nodes based on link analysis. and transportation, Secondary industries involve manufacturing and steel, and Tertiary industries cover areas such as IT services and finance. The first industry exhibits a high weighted degree, indicating its role as a core resource-based node with strong network connections. In contrast, the second industry displays low values across all centrality metrics, indicating its peripheral role or weak involvement in the network. The third industry shows a higher betweenness value, positioning it as a key intermediary despite having a lower weighted degree compared with the first industry. In particular, some nodes in this industry exhibit high connectivity, highlighting their structural importance.
# Performance Analysis of Multihop Underground Magnetic Induction Communication
## 4 Numerical Results
### Multihop MI-WUSN Performance for INID of Hops
to the lowest performance of all evaluated cases, that is, for the SNR in Figure 10a, the channel capacity in Figure 10b, and the BER in Figure 10c. Figure 10 also indicates that for worse channel conditions at hop 1, represented by \(-83\) dBm noise power that decreases over consecutive hops, high channel state variation across hops gives better performance, and vice versa. Alternatively, for good channel conditions at hop 1 (\(-103\) dBm noise power), that worsen progressively over consecutive hops, lowest variation gives best overall system performance. This result is also consistent with (24) and (25) that bound the minimum system performance of a multihop MI-WUSN by the link with highest noise. On the contrary, if we compare the two possible states of hop 1 characterized by best and worst-case scenarios, the gap in system performance is large. This gap emphasizes the significance of accounting for deviation in channel state for practical system design. From the results in Section 4.3, it is evident that end-to-end multihop communication performance is significantly influenced by the channel state between hops. Consequently, the assumption of identical hops may be useful only for simplifying system modeling, not for the practical deployment and analysis of the underground MI communication system. Therefore, the assumption of the IID of hops is impractical for the practical deployment of MI-WUSNs. Figure 9: Multihop underground MI communication system’s performance for INID of channel states between hops, with hop 1 subject to an average noise power of \(P_{n}=-93\) dBm. The panels show the variation of the (**a**) SNR (dB), (**b**) channel capacity (bps) and (**c**) BER with the noise power across hops.
# Evaluation of Resource Exhaustion Attacks against Wireless Mobile Devices
## 5 Experimental Results
The current consumption measurements in the idle mode regularly changed by the sleep mode for SP period as well as energy consumption undo the attack are represented in Table 6. These are the mean data for one wakefulness loop of 1 sec activity and sleep during 10 sec. Under the conditions assuming that the attack is carried out uniformly, with the same intensity along its entire length, the wasting current during the modeled attack of a constant intensity is calculated as a constant value \(I_{ATTACK}=51\) mA, whereas in the sleep mode it is 8 mA. The attack effectiveness is expressed by:  
\[E=I_{ATTACK}\cdot(t_{2}-t_{1})/\int\limits_{t_{1}}^{t_{2}}I_{IDLE}(t)dt, \tag{2}\]  
where the consumption in the idle mode \(I_{IDLE}\) is calculated as a mean value by the Lagrange interpolation method. The experiment was performed repeatedly over the time period \(t_{2}\) - \(t_{1}=600\) sec. The effectiveness \(E=4.488\) indicates that an attack more than 4 times faster exhausts the battery life of the XBee node.
# Biological and Technical Challenges Related to Cultured Meat Production
## 3 Challenges Related to Cultured Meat Production
### Biotechnological Challenges
#### 3.1.3 Methods for Cell Harvesting  
Another factor to be considered is the method of taking the cell biopsy. Satellite cells can be harvested either at the time of slaughter of the animal, which is not an accepted route for cellular agriculture, or through biopsies of muscle tissue. Tissue biopsy, a procedure widely used within veterinary medicine, involves the use of needle biopsy. Although this technique is quick and causes little stress to the animal, it allows a limited sample to be taken, around 0.5 g, and is not very precise due to the blind nature of the sample [31]. A second option involves a small incision at the sampling site, allowing more sampling, around 15 g, and greater success, although it is characterized by greater invasiveness for the animal [31].  
To reduce the number of donors required for cultured meat production, it is desirable to maximize the number of biopsies taken from each animal, considering the levels of stress and discomfort caused. As reported by [PERSON] et al., a possible approach to harvesting muscle biopsies for cultured meat production could involve taking multiple biopsies (up to four) from each donor animal in one session every three months, using the needle biopsy technique, thus ensuring appropriate recovery times for animal welfare [31].
# Electron Impact Cross Sections and Transport Studies of C\({}_{3}\)F\({}_{6}\)[PERSON]
## 2 Calculation Methods
### Transport Studies  
To analyze the swarm transport properties, the Boltzmann equation (BE) was solved. BOLSIG+ can be used to solve the BE of electrons in weakly ionized gas in a uniform electric field [8]. The interactive behavior of electrons in the ionized gas is represented by Boltzmann integro-differential transport  
\[\frac{df}{dt}+\vec{v}.\Delta f-\frac{e}{m}\vec{E}.\Delta_{v}f=C[f] \tag{9}\]  
where \(f\) is the electron energy distribution function (EEDF) in six-dimensional phase space. \(e\) and \(m\) are the elementary charge and mass of the electron. Further, \(E\) is the electric field, \(v\) is the velocity, and \(\Delta_{v}\) represents the velocity gradient operator. \(C\) is the rate of change of \(f\) due to elastic and inelastic collisions. Solving the above equation gave us the EEDF, which was then used to derive the ionization and attachment coefficients. BOLSIG+ provided us the density-reduced coefficients. The Townsend ionization coefficient (\(\frac{g}{N}\)) represents the total number of electrons created per unit length and the Townsend attachment coefficient (\(\frac{g}{N}\)) is defined as the total number of electrons lost per unit length. The critical field is obtained at the point where these two coefficients become equal. The BOLSIG+ also provided us with various other swarm data apart from the Townsend coefficients, such as the mean energy (\(\langle e\rangle\)) and mobility (\(\mu\)). To obtain the drift velocity (\(v_{d}\)), we used the electron mobility data and applied the following fundamental relation:  
\[v_{d}=\mu E \tag{10}\]  
To initiate the BOLSIG+ calculation, input parameters, such as gas temperature, excitation temperature, and plasma density, were provided. The software utilized these parameters along with cross sections for extrapolation at high energy and interpolation within the range. In regions below the threshold energy, the inelastic cross sections were assumed to be zero. For our computation, we utilized a gas temperature and excitation temperature of 300 K, along with a plasma density of \(10^{19}\) per m\({}^{3}\).
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
## 4 Results
### Hydrocarbon Sealing Faults (F1-F6)
#### 4.1.2 Faulting History  
The hydrocarbon-bounded faults F1-F6 exhibit a general trend of strong faulting activity during faulting initiation followed by a gradual weakening over time (Figure 5). Most faulting occurred during the Late Oligocene (E\({}_{3}\)z sequence) and the Early Miocene (N\({}_{1}\)z\({}_{2}\) sequence) post-rifting stage, with only limited faulting occurring after the Middle Miocene. The F1 fault began its activity during the Early Oligocene (E\({}_{3}\)z sequence) when it displayed the highest displacement rate, approximately 60 m/Ma. From the Late Oligocene (E\({}_{3}\)z sequence) onwards, the displacement rate of the fault rapidly reduced to around 20 m/Ma, maintaining a relatively stable level of activity until the Late Miocene (N\({}_{1}\)y sequence). Similarly, the F2 fault also initiated its activity during the Early Oligocene, but with a relatively weaker displacement rate of no more than 20 m/Ma. It maintained a relatively stable level of activity until the Early Miocene, including the deposition of the N\({}_{1}\)z\({}_{1}\) and N\({}_{1}\)z\({}_{2}\) sequences. However, after the deposition of the N\({}_{1}\)z\({}_{1}\) sequence (18.3-16 Ma), the displacement rate of the F2 fault rapidly decreased and eventually ceased. The F3 fault began its activity in the Late Oligocene, exhibiting very low displacement rates generally not exceeding 10 m/Ma. It stopped being active after the Middle Miocene (N\({}_{1}\)h sequence). The F4 fault followed a pattern of early activity starting in the Early Oligocene and ceasing during the deposition of the N\({}_{1}\)z\({}_{2}\) sequence (23-18.3 Ma). Its displacement rate ranged between 20 and 30 m/Ma. The F5 fault was active from the Late Oligocene to the Early Miocene, but with a very low displacement rate. On the other hand, the F6 fault remained active for a significant duration, initiating its activity in the Oligocene and persisting until the Middle Miocene. The fault displacement rate of the F6 fault was generally very low, except during the deposition of the N\({}_{1}\)z\({}_{1}\) sequence.
# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 4 Performance Evaluation
### Effect of Delay  
Simulations using network topology were conducted to clarify the effect of delay on SCTP congestion control. Delay represents how long it can take for a bit of data to be transmitted across the network from one node to the destination over a specific data link. Delay is presented in multiples or fractions of seconds.  
Figure 9 displays the delay up to 50 s. The favorable performance of the proposed congestion control mechanism is clearly reflected in Figure 9. The simulation time was set to 50 s. Given the lack of a congestion control mechanism in ORG-SCTP, the delay was prominent in the high-traffic-load environment. The total delay rates of SCTPs differed significantly within the period of 50 s. The performance of ORG-SCTP deteriorated (high delay indicates poor performance). The proposed congestion control of IMP-SCTP performed 14.5% better than that of ORG-SCTP. The performance of ORG-SCTP showed consistent degradation (Figure 9). ORG-SCTP and IMP-SCTP experienced a slight increase in performance from 1 to 10 s. Beyond 10 s, the delay for ORG-SCTP declined continuously, whereas the delay for IMP-SCTP was less significant. Furthermore, the throughput for ORG-SCTP was minimized from 14 to 26 s and showed the highest drawback at 33-38 s. The delay of ORG-SCTP increased again and continued to fluctuate until the end of the experiment. Thus, IMP-SCTP outperformed ORG-SCTP in terms of delay from 11 to 35 s. IMP-SCTP showed a high delay rate at 45 and 47 s; nevertheless, the delay of IMP-SCTP was less than that of ORG-SCTP. The data in Figure 9 were measured at a time step of 0.05 s. This period was derived from the overall simulation time. Furthermore, the sample was taken from one connection between a specific UE and an eNB across a high-load-traffic bottleneck. The result of the paired sample \(t\)-test demonstrates the significant difference between the delays of ORG-SCTP and IMP-SCTP. The mean of the delay for ORG-SCTP (m = 1118.023) was greater than that for IMP-SCTP (m = 971.849). Again, the equation t(df) = t state, \(p\leq 0.05\), was used to determine the significance of the difference between the two SCTPs, t(1275) = 2.93, \(p\leq 0.05\). The \(p\)-value was 0.003 at 50 s. Given that \(0.003\leq 0.5\), the difference between the delays of ORG-SCTP and IMP-SCTP was significant. Overall, the proposed enhancement improved the delay of IMP-SCTP.  
Figure 8: Receiver operating characteristic (ROC) curve for the C4.5 DT classifier.
## 3 Results
### Analysis of Ionospheric DVTEC Disturbances  
In this section, we analyze the DVTEC disturbances generated by the storm on 10 November 2018. Subsequently, we make a comparison of the ionospheric response produced on this day, when Atmospheric Electrical Activity was present, with the ionospheric response of a day without electrical activity (9 September 2018). Finally, we analyze the data by applying the wavelet transform to the DVTEC values.
## 1 Introduction
In addition, there is no algorithm in the HDR imaging literature which can predict the distortion in terms of the mean squared error caused by the message concealment. * We present a secure image encryption scheme, which adopts a 2D-SLMM to generate a pseudo-random sequence with better hyperchaotic behavior. Pixels are ciphered through the bit-level permutation using the random permutation algorithm with the time complexity of \(O(N)\). As a result, our scheme can completely shuffle the whole bits aligned by the Red, Green, Blue and Exponent channel in an HDR image. * We present a thorough and comprehensive security analysis for stego HDR RGBE images. Our scheme offers 18% to 32% more embedding rate than that provided by the current state-of-the-art works without impairing the quality of stego image. In addition, our scheme provides high image encryption security, surpassing those of our competitors. To the best of our knowledge, our scheme is the first that can report the security of HDR cipher image from six aspects: visual perception, histogram, correlation, entropy, image sensitivity, and key security. The rest of this paper is organized as follows: Section 2 presents our proposed methods in detail. The experiments and analysis are given in Section 3. Finally, Section 4 describes conclusions and future work.
# Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain
## 6 Conclusions  
In this paper, a mobile vehicle data sharing scheme based on consortium blockchain was designed to build a decentralized data sharing system framework. The data transaction time in the system can be reduced, and the entity security of vehicles and RSUs is ensured. In addition, we proposed an optimal revenue strategy approach between vehicles and data-demanding devices through the Stackelberg game. The amounts of the optimal buyer's bid and seller's available data are determined, which effectively maximizes the benefits of both transaction sides. Finally, the performances of the BCB scheme, SPRS and POS scheme were simulated and compared, and a performance comparison between the consortium blockchain framework and traditional blockchain was also presented. Simulation results show that the proposed BCB scheme has a higher level of performance. On the premise of protecting entity privacy and data security, the data of mobile vehicles can be shared with buyers more quickly.  
Conceptualization, Y.T. and J.Y.; Funding acquisition, C.Y.; Methodology, And X.N.; Project administration, J.Y.; Writing--Original draft, Y.T.; Writing--Review & editing, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by General project of philosophy and social science research in Colleges and universities of Jiangsu Province under Grant 2020 SJA1023; in part by the 2021 University Philosophy and Social Science Research Fund Project of Jiangsu Province under Grant 2021 SJA2413; in part by Natural Science Foundation of China under Grant 62003101; in part by the Guangdong Basic and Applied Basic Research foundation under Grant 2022A1515010181.  
Not applicable.  
Figure 7: Comparison of block transmission performance.
### Contribution  
To address these issues, this paper utilizes swarm intelligence algorithms to mine numerical association rules from ICS and applies these rules for anomaly detection. This approach effectively identifies anomalies and highlights deviations from normal patterns, providing clear explanations of anomalies to experts. Consequently, this significantly enhances the diagnostic speed and response efficiency of experts when encountering system anomalies. The content extracted is as follows:  
1. This study introduces a numerical association rule method based on swarm intelligence algorithms for anomaly detection in ICSs, demonstrating broad applicability and strong generalization across diverse environments.
2. A feature selection and task partitioning strategy optimizes the search space for swarm intelligence algorithms, enhancing the efficiency of processing large-scale ICS datasets and ensuring high-quality invariant rule generation.
3. An implemented anomaly detection system based on the generated invariants achieves over 84% accuracy on the SWaT dataset, providing precise identification of operational deviations and intuitive explanations for anomalies.  
The remainder of this paper is structured as follows: Section 2 provides a brief introduction to numerical association rule mining and swarm intelligence algorithms; Section 3 introduces the methodology design; Section 4 presents the experimental results; and the final section, Section 5, summarizes the paper and outlines future perspectives.
## 4 Experiments and Discussion  
To verify the advantages of the IBSGM method, we compared it with three other methods: top-hat transformation (THT), mean iterative filtering (MIF), improved new top-hat transformation (INTHT). These methods were used with the same simulated and real images.