esa-sceva/satcom-chunk-collection huggingface dataset:
# A Game-Theoretic Analysis for Distributed Honeypots
## 2 Related Work
### Game Theory for System Analysis  
Game theory can be used to analyze the performance of a system with multiple players whenever rational conditions are assumed. Non-cooperative game theory and evolutionary game theory are applied to some fields (e.g., wireless sensor network, opportunistic network and software defined network).  
Non-cooperative game theory with a decentralized clustering algorithm is present in [25] to solve the problem of prolonging a network's maximum lifetime. The game theory is adopted for limiting activities of a sensor and its neighbors to save battery energy. Based on evolutionary game theory, the work [26] presents an active defense model in wireless sensor network. The reliability and stability in a network equipped with malicious nodes are analyzed. A preventive mechanism is established to force these nodes to abandon attack activities. A PT-based game-theoretic security protocol is presented in [27], which counters black hole attacks in opportunistic network (OppNets). An evolutionary game theory model is applied to this defense mechanism for analyzing the decision-making ability.  
A multi-layered game is proposed in [28]. The IDS (intrusion detection system) and the malicious vehicle are modeled as a non-cooperative game and the Nash equilibrium strategy of probabilistic IDS monitoring is adopted. The work [29] proposes a dynamic SDN (software defined network) framework with a game-theoretic model to analyze its security performance in attack protection. In the game, a defender and its adversary compete for the right of control in some controllers. Three levels (i.e., sensor level, cluster level and base station level) are applied to the proposed framework in [30], which uses a combination of specific rules and a lightweight neural network to identify illegal sensors. Based on the multi-layered intrusion detection framework, two players form out a non-cooperative Bayesian game. Game theory is used in wide scan [31] for analyzing mass scanning problem. A scanner and its target act as players in an antagonistic game. Based on game theory and reinforcement learning mechanism, a two-stage distributed algorithm is proposed [32] to improve quality of experience at runtime. A multi-cell device is modeled [33]. The allocation issue in resource block is formulated as a bilateral symmetric interaction game. Decision-making scenarios are modeled as games in information warfare [34]. The participants include an offensive player and a defensive one.
## 4 Conclusions  
In conclusion, an ecofriendly synthesis of AgNPs using _A. aspera_ and _S. dulcis_ leaf extract was successfully performed. This method provided an alternative approach for silver nanoparticle fabrication to gradually replace traditional methods. _A. aspera_ and _S. dulcis_ leaf extract had the ability to reduce silver (I) ions into AA.AgNPs and SD.AgNPs. These final silver nanoparticle products were obtained and characterized by UV-Vis spectroscopy, FT-IR, EDXA analysis, TEM and SEM micrographs. AA.AgNPs exhibited the peak centered at 433 nm and broadened more than SD.AgNPs in UV-vis spectra. Both AA.AgNPs and SD.AgNPs were modified by phytoconstituents containing C=C, C-O and O-H bonds, but each specific surface was different due to different reducing/stabilizing sources. AA.AgNPs were bigger and had more agglomeration than SD.AgNPs, but two types of these silver nanoparticles were less than 55 nm. In addition, the synthesized AgNPs showed high antifungal activity against three fungal strains including _A. flavus_, _A. niger_ and _F. oxysporum_. Especially, the _F. oxysporum_ had the most strongly reduced growth rate when contacting AA.AgNPs and SD.AgNPs. By the one-pot reaction based on the green chemistry approach, two new silver nanoparticles were fabricated and had promising scalability for various applications, especially in medical and agricultural fields as an alternative fungicide.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/7/2505/s1](http://www.mdpi.com/2076-3417/10/7/2505/s1)]([http://www.mdpi.com/2076-3417/10/7/2505/s1](http://www.mdpi.com/2076-3417/10/7/2505/s1)), Figure S1: The UV-vis spectra of AA.AgNPs (a) and SD.AgNPs (b) stored at 4 \({}^{\circ}\)C in a brown bottle after various time intervals.  
Author Contributions: Conceptualization, N.T.T.L.; methodology, N.T.T.L., H.N.T.N. and T.T.H.T.; formal analysis, N.T.T.L. and D.Y.P.N.; data curation, N.T.T.L., N.H.N., Y.C.C., D.Y.P.N. and C.Q.N.; writing--review and editing, T.T.H.T.; supervision, D.H.N., Y.C.C. and T.T.H.T.; project administration, D.H.N.; funding acquisition, D.H.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Domestic Master/PhD Scholarship Program of Vingroup Innovation Foundation (Grant number: VINIF.2019.TS.72).  
The authors declare no conflict of interest.
## 3 Results
The proposed DNN model with 3 hidden layers and 64 hidden neurons is trained with various batch sizes on all the datasets mentioned in this paper, and accuracy levels for each batch size are presented in Figure 13. From Figure 13, it has been observed that the DNN model trained with a batch size of 15 provides good training accuracy of 96.26%, 99.73%, 100% and 99.26% and testing accuracy of 96.88%, 99.73%, 100% and 99.32% on Dataset-1, Dataset-2, Dataset-3 and Dataset-4, respectively. Figure 11: Training performance of DNN model on variety of data. (**a**) Noise level: 10 to 50 (Dataset-1). (**b**) THD level: 10 to 50 (Dataset-2). (**c**) THD level: 50, noise level: 10 to 40 (Dataset-3). (**d**) Noise level: 5 to 20 (Dataset-4). Figure 12: Testing performance of DNN model on variety of data. (**a**) Noise level: 10 to 50 (Dataset-1). (**b**) THD level: 10 to 50 (Dataset-2). (**c**) THD level: 50, noise level: 10 to 40 (Dataset-3). (**d**) Noise level: 5 to 20 (Dataset-4). Figure 13: Training and testing performance of DNN model with respect to various batch sizes. (**a**) Noise level: 10 to 50 (Dataset-1). (**b**) THD level: 10 to 50 (Dataset-2). (**c**) THD level: 50, noise level: 10 to 40 (Dataset-3). (**d**) Noise level: 5 to 20 (Dataset-4). The proposed DNN model with 3 hidden layers and 64 hidden neurons is trained with various epoch sizes on all the datasets mentioned in this paper, and accuracy levels for each epoch size are presented in Figure 14. From Figure 14, it has been observed that the DNN model trained with an epoch size of 250 provides good training accuracy of 96.43%, 99.09%, 100% and 99.47% and testing accuracy of 96.08%, 98.64%, 100% and 99.49% on Dataset-1, Dataset-2, Dataset-3 and Dataset-4, respectively. The proposed DNN model with 3 hidden layers, 64 hidden neurons and 250 epochs for all the datasets mentioned in this paper is trained with various window sizes as presented in Table 6. From Table 6, it can be observed that the proposed DNN model performs better for all the datasets with window size 15.
# Student Dropout Prediction for University with High Precision and Recall
## 5 Experiment and Analysis  
### Environment  
As we analyzed in Section 3, the features used in the existing works vary. We ran preliminary experiments using our data with the existing methods and identified important features using permutation importance and SHAP analysis. Furthermore, we found that most existing works did not meet our criteria of producing high dropout precision and recall rates. We excluded senior students' records because they were about to graduate from the university. After sanitization and anonymization of the data, we gathered 67,060 student records. The features used in the paper are described in Table 6. Table 8 describes the hardware and software specifications and versions used for the experiment.  
We preprocessed the data for XGBoost and CatBoost with SMOTE and RandomOverSamplerSMOTEENN, respectively. We used 10-fold cross-validation to reduce the dependencies on the data in all experiments unless otherwise stated. The architecture of the SDP system is illustrated in Figure 3. The results of the two methods are combined in the combiner, and the results are prioritized based on the precision rate. We further analyzed the result to identify \"Low-Risk\", and \"High-Risk\" groups; the identified groups can be delivered to the counselors and administrators for decision-making and supportive actions.  
The metrics used for measuring the prediction performance are based on the confusion matrix. We used the information on dropout as the label, and the definitions of the metrics we used are as follows:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model/Risk** & **High Risk** & **High Risk** & **Low Risk** & **Non-Dropout** \\ \hline High-Precision Model (SMOTE+XGBoost) & True & True & False & False \\ \hline High-Recall Model (RandomOverSamplerSMOTEENN+CatBoost) & True & False & True & False \\ \hline \hline \end{tabular}
\end{table}
Table 7: Risk Criteria.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Type** & **Description and Version** \\ \hline \hline \multirow{6}{*}{Hardware} & CPU & AMD Ryzen9 \\ \cline{2-3}  & Memory & DDR4 128 GByte \\ \cline{2-3}  & Mainboard & X570 AORUS ELITE \\ \cline{2-3}  & Storage & Samsung SDD 970 plus 1 TB \\ \cline{2-3}  & GPU & \multicolumn{1}{c}{Geforce RTX 2080 super} \\ \hline \hline \multirow{6}{*}{Software} & Python & 3.8 \\ \cline{2-3}  & Pytorch-tabnet & 3.1.1 \\ \cline{2-3}  & Catboost & 0.26.1 \\ \cline{2-3}  & Xgboost & 1.4.2 \\ \cline{2-3}  & Lightgbm & 3.2.1 \\ \cline{2-3}  & Scikit-learn & 0.24.2 \\ \cline{2-3}  & Numpy & 1.19.5 \\ \cline{2-3}  & Pandas & 1.2.4 \\ \cline{2-3}  & Category-encoders & 2.5.1.post0 \\ \cline{2-3}  & Imbalanced-learn & 0.8.0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experiment Environment.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]  
\[DropoutRecall=\frac{TP}{TP+FN}\]  
\[DropoutPrecision=\frac{TP}{TP+FP}\]  
\[DropoutF1=2*\frac{DropoutPrecision*DropoutRecall}{DropoutPrecision+Dropout Recall}\]
## 3 Design Methodology
### CPW Effects  
The proposed antenna is fed with a CPW structure as shown in Figure 1. Therefore, to optimize the antenna performance, the authors applied a study to realize the effects of varying the proposed CPW dimensions on the antenna performance in terms of S\({}_{11}\) and gain spectra. As shown in Figure 5a, the antenna shows a significant enhancement by changing the distance between the antenna patch and the ground plane part of the proposed CPW. The antenna gain is found to be enhanced by reducing the air gap between the proposed patch and the proposed CPW ground plane. This is due to eliminating the effects of the electrostatic capacitive coupling between the antenna parts and the ground plane [5]. The antenna is found to provide five main bands at 1.6 GHz, 2.6 GHz, 3.9 GHz, 4.5 GHz, and 5.85 GHz with S\({}_{11}\) below \(-10\) dB at 4.9 mm distance. Nevertheless, the antenna gain is affected by various changes at different frequency bands as shown in Figure 5b. However, the antenna bandwidth at 4.9 mm distance is found to be significantly enhanced over other distances.  
Figure 4: Antenna performance with and without TR structure: (**a**) S\({}_{11}\) spectra and (**b**) gain.
## 3 Micro-Settlement Control Techniques
### Techniques for Reducing the Settlement Superimposition Effect Induced by Group Pipe Construction  
Sequential construction of individual pipes during the tunnel pipe-roofing process can effectively minimize the deformation superimposition effect but inevitably extend the construction period. In contrast, simultaneous construction of multiple pipes can shorten the construction period; however, it may cause a greater deformation superimposition effect, resulting in a significant increase in surface settlement. To accelerate construction progress and control surface settlement, a simultaneous alternate pipe jacking method is employed for pipe-roof construction, and the interval number of jacked pipes is reasonably determined based on the width of the surface settlement trough. According to Equation (22), the interval number of jacked pipes is multiplied by the designed pipe spacing to calculate xi, which is then utilized to determine the group pipe superimposition coefficient, as illustrated in Figure 10. If the pipe spacing exceeds a critical value, the settlement troughs of adjacent pipes are no longer superimposed.  
As illustrated in Figure 11, the pipe roof construction sequence begins with the simultaneous installation of pipes No. 1, No. 6, and No. 7, during which no settlement trough superimposition occurs among the group pipes. After these pipes are installed, pipes No. 2 and No. 5 are constructed, followed by pipes No. 3 and No. 4. This rational construction sequence effectively mitigates the group pipe superimposition effect, thereby minimizing overall surface settlement.  
Figure 10: Superimposition coefficient for alternate construction of the pipe roof.  
Figure 9: Relationship curve between support system stiffness and surface settlement.
## 4 Performance Analysis
### Summary and Handover Considerations
The comprehensive analysis leads to the derivation of the following handover and network design considerations:
* When any underwater handover technology is designed, the BS motion needs to be considered as one of the critical factors. If the BS motion is in the same direction as the movement of an AUV, it can reduce the AUV-BS distance compared to when the BS is stationary, causing potential errors. Errors can also occur depending on the motion direction of BS1 and BS2. The direction of BS motion can alter the difference between the AUV-BS1 and AUV-BS2 distances. This situation results in the AUV incorrectly choosing a BS due to the BS motion, as it tends to connect to the network of the closest BS. However, it is hard to predict the exact direction of BS motion, so handover strategies should be developed to accommodate the worst-case scenario where BS motions are in the same direction. * The magnitude of BS motion can also affect the extent of positional changes in the x, y, z coordinates. In other words, larger BS motions can increase the variability in the AUV-BS distance. To cope with various magnitudes of BS motion, the handover mechanism needs to be flexible. When the weather or sea conditions change, the handover triggering mechanism should be able to dynamically reflect these changes. Moreover, the threshold value of the handover criterion should not be fixed but adaptively adjusted based on the sea state to enhance network reliability. * Since the variability induced by BS motion in the overlapping region can impact handover, it is sufficient to focus exclusively on the time duration passing through the overlapping region for handover parameters such as AUV-BS distance, without aggregating data over the entire time. * A wider network radius extends the communication range between BSs, providing a broader margin necessary for handover decisions. This flexibility and improved reliability imply that the network radius should be set to offset the handover decision errors caused by BS motion. * Deriving the triggering point for handover, considering the fixed positions of the BSs when an AUV moves in a certain direction (e.g., from BS1 to BS2), it is acceptable to consider triggering after passing the midpoint between the two BSs. Rather than collecting information for handover triggering from the moment a message is received from the new BS, initiating information collection for handover triggering from the point identified through analysis is more effective. This approach is efficient, as it can prevent the wasteful use of various resources, including reception power, message overhead, and processing in handover attempts. This applies equally when the network radius is large or the BS motion is minor.
# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
###### Abstract  
Deep learning has extensively been used in natural language processing with sub-word representation vectors playing a critical role. However, this cannot be said of Swahili, which is a low resource and widely spoken language in East and Central Africa. This study proposed novel word embeddings from syllable embeddings (WEFSE) for Swahili to address the concern of word representation for agglutinative and syllabic-based languages. Inspired by the learning methodology of Swahili in beginner classes, we encoded respective syllables instead of characters, character n-grams or morphemes of words and generated quality word embeddings using a convolutional neural network. The quality of WEFSE was demonstrated by the state-of-art results in the syllable-aware language model on both the small dataset (31.229 perplexity value) and the medium dataset (45.859 perplexity value), outperforming character-aware language models. We further evaluated the word embeddings using word analogy task. To the best of our knowledge, syllabic alphabets have not been used to compose the word representation vectors. Therefore, the main contributions of the study are a syllabic alphabet, WEFSE, a syllabic-aware language model and a word analogy dataset for Swahili.  
syllabic alphabet; word representation vectors; deep learning; syllable-aware language model; perplexity; word analogy Article
## 3 Enhanced Mathematical Model with Atmospheric and Environmental Effects
### Channel Capacity and Outage Probability with Environmental Effects  
Channel capacity \(C\), using the Shannon-Hartley theorem, is  
\[C=B\log_{2}\bigg{(}1+\frac{P_{r}}{N_{0}}\bigg{)} \tag{16}\]  
For multipath scenarios, effective channel capacity \(C_{eff}\) over distance \(D\) is  
\[C_{eff}=B\int_{0}^{D}\log_{2}\bigg{(}1+\frac{P_{r}(d)}{N_{0}}\bigg{)}\,dd \tag{17}\]  
Outage probability \(P_{out}\), the likelihood that SNR \(\gamma\) falls below threshold \(\gamma_{thr}\), is  
\[P_{out}=1-e^{-\frac{7\gamma_{thr}}{\gamma}} \tag{18}\]  
This model provides a comprehensive framework to assess 5G-satellite integrated networks, accommodating diverse propagation phenomena crucial for reliable IoT and urban applications under various environmental conditions.  
4 A 5G-Satellite Convergence Analysis: Practical Considerations for IoT Applications in Smart Cities  
The following results illustrate the performance and signal characteristics of a 5G-satellite integrated network using the two-ray model and free-space path loss (FSPL) model. Parameters such as satellite height, frequency, transmitter height, and dielectric constant were varied to observe their impact on received power, path loss, reflection coefficient,channel capacity, and outage probability. The simulation was conducted with the following parameters in Table 1 to model the two-ray propagation and free-space path loss (FSPL):
# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 4 Works Based on Runtime Information
### Methods Based on Analysis Coverage  
There are also approaches based on runtime coverage to determine if a test case is a duplicate. As shown in Figure 13, typical fuzzing tools maintain a seed queue during runtime. When the target program requires input, the fuzzing tool retrieves seeds from the queue according to certain rules. The tool also maintains several bitmaps to record the execution paths of the seeds. During runtime, the coverage is evaluated by considering new coverage information, and it is used to determine if a bug is non-duplicate. These methods utilize runtime boundary coverage information to evaluate the level of test duplication. The coverage range during program execution and the frequency of visiting each boundary coverage range can be obtained through instrumentation. When a bug occurs and covers a previously unidentified path or does not match the boundaries covered by previous bugs, it can be considered a non-duplicate crash.  
Figure 14 illustrates the discovery of new coverage information during runtime using AFL as an example. It shows two scenarios: discovering a new path and detecting a change in the execution count of a path. AFL defines corresponding data structures, such as trace_bits and virgin_bits, to determine the presence or absence of a path.  
[PERSON] et al. proposed CRAXTIrage [42], a triage tool that utilizes binary code coverage information. It compares the coverage paths of successful executions with the execution path that triggers a bug, aiming to eliminate unnecessary execution paths that contribute to the bug. This approach helps in comparing coverage paths to determine their similarity.  
Figure 13: Seed processing in fuzz testing.
# High-Resolution Radio Observations of Five Optically Selected Type 2 [PERSON]
## 5 Discussion
### VLBI Structure of the Target Sources
While this interpretation might seem appealing in view of the extended lobe structure reported by [PERSON] [11], in our VLBI data, we do not find any supporting evidence for large scale-jets, which makes this interpretation less likely. Based on our EVN measurements, we derived the observed brightness temperatures of SDSS J1026\(-\)0042 at both frequencies using Equation (1) [42]. Here, \(z\) is the redshift, \(S_{\
u}\) is the flux density (measured in Jy) at \(\
u\) frequency (GHz), and \(\theta\) is the FWHM of the fitted circular Gaussian model component (mas). \[T_{\rm b,obs}=1.22\times 10^{12}(1+z)\frac{S_{\
u}}{\theta^{2}\
u^{2}}\quad[ \rm K] \tag{1}\]  
The derived values are \(T_{\rm b,obs}=(1.15\pm 0.22)\times 10^{9}\) K at 1.7 GHz, and \(T_{\rm b,obs}=(3.01\pm 0.74)\times 10^{9}\) K at 5 GHz. The values \(T_{\rm b,obs}>10^{6}\) K indicate non-thermal emission originating from AGN (e.g., [7]). We also derived the \(K\)-corrected monochromatic radio power values for the compact VLBI-detected components of SDSS J1026\(-\)0042: \(P_{\rm 1.7 GHz}=(7.08\pm 0.48)\times 10^{23}\) W Hz\({}^{-1}\) and \(P_{\rm 5 GHz}=(1.10\pm 0.13)\times 10^{23}\) W Hz\({}^{-1}\). For this calculation, we used Equation (2), where \(D_{\rm L}\) is the luminosity distance and \(\alpha=-1.73\) is the spectral index between 1.7 and 5 GHz derived from our VLBI model component flux densities (Table 2). \[P_{\
u}=4\pi D_{\rm L}^{2}S_{\
u}(1+z)^{-1-\alpha} \tag{2}\]  
With these values at hand, we can assess whether the radio emission could originate from supernova remnants in the host galaxy [7]. The supernova rate corresponding to our radio powers (\(\
u_{\rm SN}\) = 149 and 52 yr\({}^{-1}\) at 1.7 and 5 GHz, respectively) exceeds the rates of the most powerful starburst galaxies (e.g., [43,44]). An individual supernova remnant is also ruled out, and complexes of supernova remnants do not concentrate at mas scales. A recent study investigating radio-quiet AGNs at \(z<0.8\), with \(4\times 10^{21}<P_{\rm 1.4 GHz}<7\times 10^{24}\) W Hz\({}^{-1}\), and \(\rm L_{bol}<10^{38}\) W [45] concluded that the radio emission originating from these sources is dominated by AGN activity and not the host galaxy. Similar results were found by [PERSON] et al. [6] and [PERSON] et al. [46].