esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Super Resolution Using a Selective Edge Aggregation Network
## 4 Experiment
### Ablation Study  
We conduct the experiment while removing each method to see if the proposed methods worked. The experimental results are shown in Table 1. The batch size of each experiment is set to 2, and REDS4 is used as a test set.  
Detect blur is a method of detecting the presence of a blur area in \(LR_{t-k}\) using a Laplacian filter before extracting \(E_{t-k}\). This method eliminates the use of \(e_{t-k}\), which can act as noise, allowing our model to learn a complete structure feature. In the experiment, when baseline and adopting detect blur, the performance is higher than when it is not adopted. Therefore, it is proved through experiments that the blur frame acts as noise when generating \(E_{t-k}\).  
Magnitude loss \(Loss_{m}\) is a loss function that minimizes the gradient magnitude difference between \(SR_{t}\) and \(SR_{GT}\). Our model learns the structure features of \(SR_{GT}\) by minimizing the gradient difference. When \(Loss_{m}\) is adopted, that brings about a 0.14 dB improvement on PSNR.  
Incomplete contour and color correction occur in some areas of \(SR_{t}\) due to artifacts present in the test data. To overcome these limitations, the refinement module is adopted. When the refinement module is adopted, the contours become clear and the color is improved.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## 5 Analysis of Selected QoS Metrics  
The intra-body signal transmission suffers significant energy attenuation. To support stable data transmission and maintain link quality, some key QoS metrics are now selected and analyzed.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 3 Materials and Methods
### Route Calculation Algorithm  
The route is calculated with the Dijkstra algorithm finding the shortest path in the graph of transport network. In car navigation systems, more robust algorithms are used; for example, the A* algorithm or further optimisations that usually visit fewer graph nodes than the Dijkstra algorithm [60]. In the project, the performance of the Dijkstra algorithm is enough because navigation graphs for metro stations do not exceed 300 nodes.  
The costs for edges are modelled based on the length of the edge and a factor dependent on the type of _Interior Space_ or type of user. For example, it is recommended to go through the platform rather than the mezzanine if the route length is equal. There are different costs for _Indoor Installation_--usually, the escalator is faster than the stairs and the stairs are faster than the elevator. It is expected that routes should avoid excessive curves on the path and avoid passing through unnecessary objects like turnstiles, so the shape of path segments impacts the costs for the route calculation algorithm.
## 3 Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm (CPIMM-NAUKF)
### Cpiim  
#### 3.1.1 Cubature Principle  
Assuming that \(\mathbf{\chi}\sim N\Big{(}\mathbf{\chi}:\hat{\mathbf{\chi}},\mathbf{SS}^{T}\Big{)}\), where the dimension of \(\mathbf{\chi}\) is \(l\times 1\), then the first moment and second moment of any function \(\mathbf{y}=f(\mathbf{\chi})\) can be solved by the following integral form:  
\[\begin{split}\hat{\mathbf{y}}=E(\mathbf{y})=\int\limits_{R^{l}}f(\mathbf{ \chi})N\Big{(}\mathbf{\chi}:\hat{\mathbf{\chi}},\mathbf{SS}^{T}\Big{)}d\mathbf{\chi}\\ P_{y}=D(\mathbf{y})=\int\limits_{R^{l}}f(\mathbf{\chi})f^{T}(\mathbf{\chi})N \Big{(}\mathbf{\chi}:\hat{\mathbf{\chi}},\mathbf{SS}^{T}\Big{)}d\mathbf{\chi}.\end{split} \tag{13}\]  
Equation (13) is a multidimensional integral problem, and it is difficult to solve through analytic methods. The cubature principle states that a cubature point with a weighted value can be chosen to approximate the integral of the multidimensional function, and the method of choosing the cubature point is:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Mean** & **Covariance** \\ \hline MSE & 0.6000 & 3.9417 \\ MC & 0.5937 & 1.3906 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculation results of the comparison of the mean and covariance matrix after interaction; MSE: the mean square error; MC: Monte Carlo.  
\[\chi_{\pm r}=\hat{\chi}\pm\mathcal{S}\xi_{r},r=1,2,\ldots,l \tag{14}\] \[\xi_{r}=\sqrt{l}e_{r},\quad r=1,2,\ldots,l \tag{15}\]  
where \(e_{r}\) is the \(r\)th column of the unit matrix \(I_{l\times l}\).
# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 4 Performance Analysis of the UAV-BS  
The simulation is set to test the proposed solutions in terms of different performance parameters. The proposed solution is evaluated in the NS-2.34 simulator, which is integrated with SUMO and MOVE mobility modules. The NS-2.34 is based on the high enactment technical computing programming language employed for visualization, simulation, and plotting of mathematical data. UAV applications and their mobility patterns offer extensive feasible and cost-effective solutions and services. All the applications need stable routing without any delay and disconnection issues in the network. The UAV nodes need feasible data communication and security provision solutions with less overhead and fewer complexities. The cellular network nodes, the macrocell, and the UAV setting are set by using the editor. For UAV node mobility, the random walk mobility sets the altitude up to 200 to 500 feet. We considered some complex scenarios as well to test the proposed solution to check the UAV's complex electromechanical systems in terms of delay and disconnection and set the parameters accordingly to avoid any delay or disconnection issues. The performance parameters to test the proposed solution are end-to-end delay, data throughput, and 0 network overhead. We also considered a delay factor before testing the proposed model and estimated the range of delay and disconnection. These factors help avoid instability in the network. The simulator uses two types of languages: C and object-oriented tool command language (OTcl). Table 2 shows the simulation parameters used to test the proposed solution.
## 5 Discussion
### Lessons from the CISS2021-OL  
1. _Automated re-training/re-tuning AICrit_: The representative models along with the intrinsic parameters of the AICrit discussed in [2] were built and fine-tuned using the operational data. Thus, any change in the data characteristics due to process upgrades or aging factors of components leads to an unacceptable number of false alarms. For example, prior to CISS2021-OL, UF membranes in stage 3 were replaced. This caused a large variation in the data generated by the differential indicator (DPIT-301) sensor (Figure 2). Thus, the retraining process was carried out in AICrit to achieve zero false positives. However, for large-scale plants, an automated mechanism is needed to initiate the retraining process in response to a significant change in the data characteristics, such as mean and standard deviation. Nevertheless, this introduces a new research question: how frequently should this process be conducted to consistently achieve zero false alarms, given the highly dynamic nature of large-scale plant operations?
2. _Detector placement_: The placement of detectors is another important research area as it plays a crucial role in their performance. In CISS2021-OL, all detectors received the data from the clone historian, which is a copy of the primary historian. As discussed in Section 4, the adversaries were successful in disrupting the primary historian, and none of the detectors received process data. Thus, it is advised to deploy the detectors in a distributed manner so that compromising all data sources at the same time becomes harder for an attacker.
3. _Restoring SWaT operation_: One of the major challenges faced during the CISS2021-OL was to bring back SWaT to its normal state after a successful attack. It is necessary to avoid the cascading effect of an attack. This involves several processes including (i) resetting all network communications, (ii) restoring the control codes of all PLCs, (iii) bringing back all sensors and actuators to auto mode in case they are set to manual mode by the adversaries, and (iv) reverting all devices such as the SCADA, HMI, and historian to their respective states that existed prior to the attack launch. Performing the above tasks manually is a time-consuming and error-prone task and thus needs automation.
# Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data
## 4 Machine Learning Models
### Support Vector Machine  
SVMs have gained immense popularity in the field of machine learning due to their ability to solve both classification and regression problems effectively. SVMs work by constructing hyperplanes that can optimally separate data points belonging to different classes or predict target variables with maximum margin. One of the most significant advantages of using SVMs is their ability in handling high-dimensional datasets and nonlinear relationships between variables [72].  
In R, e1071 package [73] is commonly utilized to implement SVM models for regression and classification tasks. The package provides options for tuning hyperparameters such as the kernel function, regularization parameter, and cost parameter. One important consideration when using SVMs is their robustness to outliers and noise in the data. Outliers influence the position of the hyperplane and lead to poor generalization performance. To address this issue, Bayesian optimization can be utilized to increase the model's performance and robustness. Bayesian optimization has been shown to be effective at tuning hyperparameters in various machine-learning algorithms, including SVMs [71].
## 4 Results
### Cybersecurity Priorities for the Next Normal
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Dimension** & **D1** & **D2** & **D3** & **D4** & **D5** & **D6** & **D7** & **D8** & **D9** & **D10** & **D11** & **D12** \\ \hline D1 Governance & 1.000 & 0.596 & 0.483 & 0.392 & 0.730 & 0.547 & 0.721 & 0.488 & 0.736 & 0.345 & 0.654 & 0.733 \\ \hline D2 Asset Management & 0.596 & 1.000 & 0.344 & 0.540 & 0.500 & 0.418 & 0.487 & 0.283 & 0.413 & 0.280 & 0.352 & 0.517 \\ \hline D3 Risk Management & 0.483 & 0.344 & 1.000 & 0.478 & 0.523 & 0.442 & 0.323 & 0.239 & 0.534 & 0.382 & 0.519 & 0.455 \\ \hline D4 End User Management & 0.392 & 0.540 & 0.478 & 1.000 & 0.592 & 0.535 & 0.348 & 0.332 & 0.455 & 0.475 & 0.413 & 0.427 \\ \hline D5 Access Control & 0.730 & 0.500 & 0.523 & 0.592 & 1.000 & 0.826 & 0.717 & 0.495 & 0.826 & 0.596 & 0.728 & 0.722 \\ \hline De Data Security & 0.547 & 0.418 & 0.442 & 0.535 & 0.826 & 1.000 & 0.587 & 0.508 & 0.757 & 0.684 & 0.641 & 0.598 \\ \hline D7 Network Security & 0.721 & 0.487 & 0.323 & 0.348 & 0.717 & 0.587 & 1.000 & 0.580 & 0.706 & 0.331 & 0.646 & 0.800 \\ \hline D8 Secure System Installation & 0.488 & 0.283 & 0.229 & 0.332 & 0.495 & 0.508 & 0.580 & 1.000 & 0.681 & 0.345 & 0.673 & 0.597 \\ \hline D9 Application Security & 0.736 & 0.413 & 0.534 & 0.455 & 0.826 & 0.757 & 0.706 & 0.681 & 1.000 & 0.663 & 0.832 & 0.809 \\ \hline D10 Cloud Security & 0.345 & 0.280 & 0.382 & 0.475 & 0.596 & 0.684 & 0.331 & 0.345 & 0.653 & 1.000 & 0.444 & 0.433 \\ \hline D11 Operation Security & 0.654 & 0.352 & 0.519 & 0.413 & 0.728 & 0.641 & 0.646 & 0.673 & 0.832 & 0.444 & 1.000 & 0.773 \\ \hline D12 Respond and Recovery & 0.733 & 0.517 & 0.455 & 0.427 & 0.722 & 0.598 & 0.800 & 0.597 & 0.809 & 0.433 & 0.773 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: CTI dimension correlation matrix. Most critical dimensions: This group result is aligned with the Two-Tailed T-test results that were discussed earlier. It comprises the Data Security and Cloud Security dimensions, which received the lowest scores among the other groups. Policymakers and organization leaders should review and update their current cybersecurity strategy and investment initiatives to give priority to these two dimensions where possible. 2. Important dimensions: This group combined related dimensions that form part of the fundamental practice for identifying and mitigating cyber risks. Some organizations received decent scores on these dimensions. Organizations that are just starting to plan and implement cybersecurity programs, e.g., startups and SMEs, may consider these dimensions as a good starting point. 3. Necessary dimensions: This group mostly contains technical-oriented dimensions. All of them are necessary for building organizational capabilities to prevent, detect, respond, and recover from cyber threats. This group also has the Governance dimension, which performs the evaluate, direct, and monitor functions [28] and ensures other dimensions are strategically aligned with objectives.
## 4 Numerical Examples and Discussion
### Result Presentation
As shown in Figure 8, the deformation stress will reach the maximum at the major axis and repeat twice near the connected point of the wrap angle, and the variation range of the flexspline tooth ring's equivalent stress decreases obviously with the reduction in wrap angle. For a smaller wrap angle, it can be notably found that the deformation stress distribution in the non-working area would be smoother with the same double eccentric arc in working section. The maximum value of the flexspline's stress could be effectively improved with an appropriate wrap angle after the stress concentration is controlled. Moreover, with decreasing the wrap angle, the maximum value of the flexspline's equivalent stress in the non-working area would be less. Thus, it can be concluded that the stress of the flexspline could be improved by releasing the forced deformation in the non-working area, and the wrap angle would be need to optimized for more reasonable stress distribution and smaller basic deformation stress value of the flexspline. Figure 9 shows the equivalent stress on the whole structure of the flexspline under the action of the double eccentric arc cam wave generator, which is designed in the new deformation model with 30\({}^{\circ}\) in half wrap angle and solid filling in non-working area according the deflection curve. Maximum equivalent stress occurred at the round corner of the cup bottom with 385 MPa. Figure 10 shows the deformation equivalent stress at the outer end of the flexspline deformed by the double eccentric arc cam wave generator with gap or solid filling in the non-working area. The contour of the circular arc-curve wave generator is combined with two eccentric circular arcs and two corresponding reflection curves. In other words, the flexspline under the action of the circular arc-curve wave generator is fully supported by solid filling to the non-working area of the wave generator according to the deflection curve, but the non-working area of the flexspline under the action of the circular arc-line wave generator is not supported. It shows that the equivalent stresses of the flexspline on the two conditions agree with each other very well. The finite element analysis in this paper produced results essentially in agreement with references [7; 17; 30], and the stress curve is consistent with the research results of other scholars [4; 11; 12]. Therefore, the method of design of the cam based on the reflection curve in the non-working area is effective, and could gain lower deformation stress and keep the deformation shape of the flexspline unchanged during rotating.
# Evaluation of Radio Communication Links of 4G Systems
## 4 Results and Discussion  
In this part, we will give the different parameters used to improve the quality of service of the 4G LTE network. The used parameters in this study are: signal to interference and noise ratio (SINR), reference signal received power (RSRP), reference signal received quality (RSRQ), and debit.
# An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization
## 5 Expanded Experiments, Results, and Discussion
### Class Imbalance Mitigation and Impact Assessment  
Our dataset exhibits a natural class imbalance, with retracted papers comprising 72.6% and non-retracted papers 27.4% of the total samples. To evaluate whether this imbalance poses a significant problem, we analyzed its specific performance impacts and found that while the distribution is mathematically imbalanced, it may not be as problematic as initially perceived. The original model achieved a strong overall performance (F1-score: 0.9305), with the primary impact being lower recall for non-retracted papers (70.8% vs. 96.6% for retracted papers), translating to approximately 29% of legitimate papers being incorrectly flagged for editorial review. Overall, this class distribution reflects the current natural retraction patterns in academic publishing data we retrieved, and forcing a perfect balance would artificially distort realistic scenarios.  
Rather than employing synthetic data generation techniques that could introduce artificial bias, we implemented cost-sensitive learning (Table 5) with balanced class weights to address this imbalance while preserving authentic data distribution. We evaluated four different weighting strategies, with the balanced weights approach demonstrating the optimal performance by improving minority class recall from 70.8% to 78.1% (10.9% relative improvement) while maintaining a strong overall model performance with only a 0.62% decrease in F1-score. This reduced the false positive rate from 29% to 22%, demonstrating that the imbalance effects are addressable through established techniques without compromising core model functionality.
## 4 Experimental Setup
### Considered Dataset  
The performance of the proposed method is evaluated on eight publicly available UCI datasets [28] namely, Colon, Multiple Features, Isolet, Spambase, Ionosphere, WDBC, Sonar (Connectionist Bench), and SPECTF. Table 1 details the considered datasets in terms of the number of features, classes, and sample size of the considered dataset. It can be observed from Table 1 that the range of feature size in considered datasets varies extremely, which will evaluate the consistency of the proposed method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Name** & **No. of Features** & **No. of Classes** & **No. of Samples** \\ \hline Colon & 6000 & 2 & 62 \\ Multiple Features & 649 & 10 & 2000 \\ Isolet & 617 & 26 & 6238 \\ Spambase & 57 & 2 & 4601 \\ Ionosphere & 33 * & 2 & 351 \\ WDBC & 30 & 2 & 569 \\ Connectionist Bench & 60 & 2 & 208 \\ SPECTF & 44 & 2 & 80 \\ \hline \hline \end{tabular}  
* In the ionosphere, there are originally 34 features; but as the second column is the same for all rows, we have not considered it during our experiments.  
\end{table}
Table 1: Details of considered datasets.
## 3 Results and Discussion  
### The Dependence of Bottom and Surface on tine Type and Working Speed  
After harvest, usually in \"reduced-till\" system, the shallow tillage is used, but working parts of implement do not reach the bottom of tramlines, cannot loosen soil, properly mix residues into the soil in these tramlines. The sinks of soil surface were estimated 6 \(\pm\) 0.5 cm, the cross section of compressed soil area 280 \(\pm\) 15 cm\({}^{2}\) (Figure 3). The tillage depth of opposite disc was selected 6 \(\div\) 8 cm, only for soil surface aligning. The measurements of soil density, water content and soil penetration resistance (Figures 4 and 5) show that the psychical differences of compacted and not compacted soil were mostly in the ploughing soil layer from 0 to 25 cm.  
Both graphs indicate that loosening of deeper soil layer is not required strongly, in addition the undesirable subsoil layer would be raised by these shapes. This is the reason for eliminating topsoil density and the study of tillage options and determination of fuel consumption. However, we are looking for tillage regime of WTT aggregate to restore the soil properties exactly in this layer up to 25 cm.  
Figure 3: Principal scheme of measurements in tramline profile.  
Figure 4: Soil dry bulk density and water content on clay loam (**a**) and loam soil (**b**).  
Figure 5: Soil penetration resistance on loam soil (**a**) and clay loam soil (**b**).  
Various scenarios of tramlines renovation and its quality level were obtained by changing the types of tines shapes and working speed. It was found that additional settings can provide positive soil structure and its fraction aggregate size proportions in loam and clay loam soil. The WTT performs three functions: tines crust the soil, disc blades return soil to wheeling's and roller crumble clods, and that contributes to the aligning of soil surface. The WTT also mixed wheat stubble, what lying on tramlines surface after the harvest, slightly added to the tramline place and will restore the soil organic matter shortage over the year. The bottom line of loosed soil in have-been tramline was influenced by a variety of tines shapes but was not influenced significantly by working speed (Figure 6). The bottom line of restored tramlines in loam and clay loam soil were similar, loosened soil area varied in the range of error and not discussed separately. Winged tines have loosened the bigger cross area of soil and it was 860 \(\pm\) 8 cm\({}^{2}\), while narrow tines did not cover all the width of tramline and have loosened only 660 \(\pm\) 8 cm\({}^{2}\).  
The surface of tramlines was influenced majorly by working speed and particularly by variation of tines shapes. Soil surface was formed mainly by opposite discs, that at highest speed (3.6 m\(\cdot\)s\({}^{-1}\)) supplementary upthrow and formed higher soil pile (Figure 7). Moreover, better lifting, crusting of soil layer and more intense straw mixing with both tines' shapes were observed at this faster speed.