esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evaluation of Presented Algorithms  
An assessment of the presented data protection procedure was made in the paper. The evaluation of the algorithms involved in the procedure was made by calculating appropriate parameters for the evaluation of cardio data:  
1. The Euclidean error-distance parameter (Percentage Residual Difference) accurately estimates the distortion in the decrypted data relative to the initial cardio input data [29]: \[\text{PRD}=\sqrt{\frac{\sum_{i=1}^{L}(x_{ori}(i)-x_{tr}(i))^{2}}{\sum_{i=1}^{ L}(x_{ori}(i))^{2}}}\cdot 100[\%],\] (8) where: \(x_{ori}\) is the original initial cardio signal, \(x_{tr}\) is the converted cardio signal, \(L\) is the length of the investigated signal.  
2. The parameter Signal to Noise Ratio (SNR) is: \[\text{SNR}=10\cdot log_{10}\frac{\sum_{i=1}^{L}(x_{ori}(i)-x_{tr}(i))^{2}}{\sum_{i=1 }^{L}(x_{ori}(i))^{2}}.\] (9)
3. The parameter Peak Signal to Noise Ratio (PSNR) is the ratio of the maximum value of the converted cardio signal to the mean squared deviation of the original, initial cardio signal (where max is the maximum value from the original ECG signal) [30]: \[\text{PSNR}=10\cdot log_{10}\frac{max^{2}}{\frac{1}{L}\sum_{i=1}^{L}(x_{ori}(i )-x_{tr}(i))^{2}}.\] (10)
4. The parameter Mean Square Error (MSE) is used to quantify the reconstruction error [31]: \[\text{MSE}=\frac{1}{L}\sum_{i=1}^{L}\cdot(x_{ori}(i)-x_{tr}(i))^{2}\] (11)
5. The parameter Root Mean Square Error (RMSE) [32] is: \[\text{RMSE}=\sqrt{\frac{1}{L}\sum_{i=1}^{L}(x_{or}(i)-x_{tr}(i))^{2}}.\] (12)
6. The parameter Mean Absolute Percentage Error (MAPE) [32] is: \[\text{MAPE}=\frac{1}{L}\sum_{i=1}^{L}\biggl{(}\frac{|x_{or}(i)-x_{tr}(i)|}{x_ {or}}\biggr{)}\cdot 100\%.\] (13)
7. The parameter Bit Error Rate (BER) [33] is: \[\text{BER}=\frac{\sum_{i}x_{or}\oplus x_{tr}}{L}.\] (14)
8. The parameter evaluating the relative entropy Kullback-Leibler divergence (KLD) [34] is: \[\text{KLD}(p_{or},\ p_{tr})=\int p_{or}(x)log\frac{p_{tr}(x)}{p_{or}(x)}dx.\]
# Error-Function-Based Penalized Quantile Regression in the Linear Mixed Model
## 5 Discussion  
We conducted Monte Carlo simulations of the proposed method under various conditions, including different SNRs, sample sizes \(n\), correlation coefficients \(\rho\), and sparsity levels. The results demonstrate that DERF-QR consistently achieves the lowest PE and highest TN in most scenarios, indicating its strong performance in both variable selection and parameter estimation.  
Despite DERF-QR's strong overall performance, its estimation accuracy declines under Cauchy-distributed errors. This can be attributed to the heavy-tailed nature of the Cauchy distribution, which introduces more extreme outliers and may compromise the robustness of coefficient estimation. Therefore, evaluating the robustness of DERF-QR and related methods under heavy-tailed error distributions remains an important direction for future research.  
The simulation results show that \(\sigma\) significantly affects DERF-QR's estimation performance. We conducted simulations under the setting to examine how different values of \(\sigma\) affect the PE at \(\tau=0.25\), \(0.5\), and \(0.75\), respectively. Taking \(n=30\), \(\rho=30\), \(\epsilon_{ij}\sim N(0,1)\), Figure 6 shows the trend of how the PE varies with increasing \(\sigma\) at different quantile levels. As \(\sigma\) increases, the PE increases across all three quantile levels. When \(\tau=0.5\), the impact of \(\sigma\) on PE is relatively smaller compared to the other quantiles. Based on these observations, we recommend selecting the \(\sigma\) within a relatively small range through cross-validation in practical applications.  
Additionally, we focus exclusively on EBIC performance for \(\lambda_{\beta}\) and \(\lambda_{\varepsilon}\) selection. Other parameter selection approaches, including the Schwarz information criterion (SIC), generalized approximate cross-validation (GACV), and cross-validation methods, were not evaluated in our comparative analysis. Future comparisons of these methods would strengthen tuning strategy evaluation.  
Figure 5: Coefficient estimation results across different quantile levels.
## 4 Results and Experimental Evaluation
### Experimental Setup  
#### 4.1.1 Dataset and Preprocessing  
All experiments were carried out using the TrajAir dataset [30] collected from Pittsburgh-Butler Regional Airport (KBTP), a non-towered airport with various general aviation operations. The dataset contains trajectory coordinates (x, y, z) and corresponding weather information (wind components) recorded over multiple months, providing a comprehensive testbed for model evaluation.  
We implemented a comprehensive preprocessing pipeline to ensure data quality and reliability. For missing data handling, we removed trajectory sequences with significant ADS-B reporting gaps to maintain temporal continuity, applied linear interpolation for missing weather data when gaps were sufficiently small, and used cubic spline interpolation for brief trajectory gaps to preserve smoothness. Our outlier detection employed statistical methods to identify and remove anomalous data points, including velocity outliers detected through unrealistic speed changes, altitude rate anomalies exceeding typical aircraft performance limits, and erroneous wind measurements validated against meteorological constraints.  
We standardized all features and segmented continuous trajectories into sequences using a sliding-window approach. We used a sequence length of 20 time steps for input and predicted 10 time steps in the future. To assess model robustness to different temporal resolutions, We evaluate performance at three temporal resolutions: 1 Hz (original data), 0.5 Hz (downsampled by factor of 2), and 0.25 Hz (downsampled by factor of 4).
## 3 Design of Control Strategy  
### System Structure  
Different from the traditional ESC control strategy, this paper designs a controller based on optimal linear control, and proposes an ESC control strategy based on tire force observation control, in order to be able to use with tire force sensor to improve the control accuracy of the electronic stability control system. First, in the nominal value calculation part, the influence of the maximum lateral force of the tire under extreme conditions by the longitudinal force is considered, and the expected value of the control index is determined according to the road surface information, the vehicle state information, and the measured tire force. After that, based on the nominal and actual values of the control indicators that determine the vehicle's instability state, if the vehicle is determined to be unstable, the controller will be triggered to work. Next, after the vehicle is determined to be unstable, the upper controller determines the tires to be braked according to the control command. Vehicle state controller outputs target longitudinal force based on desired state. What's more, the lower controller determines the braking pressure applied to the target wheel based on the target longitudinal force. At last, spoke-type three-component force sensor detects the force of the controlled tire in real time and serves as input to the controller, making the system a closed loop. The controller structure is shown in Figure 3.  
Figure 2: Installation location of the spoke-type three-component force sensor.
# An Empirical Study on Software Defect Prediction Using CodeBERT Model
## 6 Experiments and Results
### RQ2: Do the Different Prediction Patterns Affect Software Defect Prediction Performance?  
We decompose RQ2 into two sub-research questions, RQ2a and RQ2b, that use the CVPSC and the CPPSC dataset for cross-version and cross-project defect prediction, perspectivity.  
3.1 RQ2a: Do the Different Prediction Patterns Affect Software Defect Prediction Performance in Cross-Version Defect Prediction?  
We perform cross-version defect prediction experiments on the CVPSC dataset to compare the prediction performance of CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT model on F-measure, as illustrated in Table 5. For instance, we achieve an F-measure of 0.505, 0.52, and 0.493 on the CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT model for the Camel project. The RANDOM model for the Camel project achieves an F-measure of 0.287, which is lower than the three CodeBERT-based models. Considering the average F-measure, CodeBERT-PS and CodeBERT-PK models perform very similarly, which get 0.616 and 0.613, respectively. The CodeBERT-PT model performs poorer than the CodeBERT-PS and CodeBERT-PK models as a 5% decrease in F-measure. All three models outperform the RANDOM model, which achieves an F-measure of 0.397.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Training Set** & **Test Set** & **CodeBERT-PS** & **CodeBERT-PK** & **CodeBERT-PT** & **RANDOM** \\ \hline Camel 1.4 & Camel 1.6 & 0.505 & **0.52** & 0.493 & 0.287 \\ \(|\)Edit 4.0 & JEdit 4.1 & 0.661 & **0.701** & 0.642 & 0.267 \\ Lucene 2.0 & Lucene 2.2 & **0.743** & 0.741 & 0.707 & 0.546 \\ Xalan 2.5 & Xalan 2.6 & **0.684** & 0.675 & 0.627 & 0.484 \\ Xerees 1.2 & Xerces 1.3 & **0.36** & 0.313 & 0.321 & 0.231 \\ Synapse 1.1 & Synapse 1.2 & **0.566** & 0.548 & 0.443 & 0.402 \\ Poi 2.5 & Poi 3.0 & **0.795** & **0.795** & 0.72 & 0.562 \\ \hline \multicolumn{5}{c}{Average} & **0.616** & 0.613 & 0.565 & 0.397 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of CodeBERT-PS, CodeBERT-PK, CodeBERT-PT, and RANDOM models on the CVPSC dataset for cross-version defect prediction. The float values are the average F-measure values. The best F-measure values are highlighted in bold.  
Figure 7: The fluctuating level of F-measure on CodeBERT-PT and CodeBERT-NT on the CPPSC dataset for cross-project defect prediction.  
Summary of RQ2a: The CodeBERT-PS and CodeBERT-PK models behave similarly, and both outperform the CodeBERT-PT model in cross-version software defect prediction.
## 3 Results
### Effect of He-Ne Laser Pre-Sowing Seed Treatment on the Bioactivities of S. marianum Fruit Extracts  
#### 3.5.1 The Anti-Dictyostelium discoideum  
Cell Growth Activity of _S. marianum_ Fruit Extract  
The high silybin (A + B) contents in fruit extracts of plants from 6-min-irradiated seeds prompted us to further investigate the bioactivities of these extracts as compared with those of plants from non-irradiated seeds. The growth inhibition effects of fruit extracts from the 6 min and control treatments were compared using the _D. discoideum_ wild-type axenic strain Ax-2 cells. Cells were grown in HL-5 media in 24-well plates at a density of \(2\times 10^{4}\)/mL per well. The cells were then treated with different concentrations (100 ug, 300 ug, and 600 ug) of laser-treated _S. marianum_ extract or laser-untreated _S. marianum_. Then, the cells were counted with a hemocytometer over three days (Figure 2A-C). For  
Figure 1: Fold expressions of chalcone synthase (CHS) genes as related to the composition of silybin (A + B). The expressions of _CHS1_, _CHS2_, and _CHS3_ genes in plants from control (non-irradiated) treatments were normalized to one. The expressions of CHS genes for all other treatments (2, 4, 6, 8, and 10 min laser irradiation) were expressed as fold expressions with respect to the expressions of the corresponding genes from the control treatment. The content of silybin (A + B) was computed as milligrams (mg) per 100 g of fruit dry weight (D.W), based on the HPLC quantification data.  
control samples, cells were treated with DMSO at concentrations of 0.1%, 0.3%, and 0.6%, which were used as solvents for the _S. marianum_ extracts.  
The data showed no significant differences between the WT sample and the DMSO-treated samples as negative controls (Figure 2D). However, 6-min laser-treated _S. marianum_ extract caused an inhibition of cell growth compared to the DMSO control and laser-untreated extract. A significant decrease was observed after 24 h of incubation with laser-treated _S. marianum_ extract at concentrations of 300 \(\upmu\)g and 600 \(\upmu\)g compared to the DMSO control sample (Figure 2B,C). A significant decrease in cell growth was also observed after 72 h of incubation with laser-treated _S. marianum_ extract at a concentration of 600 \(\upmu\)g compared to the DMSO control sample (Figure 2C).
# GNSS RTK Positioning Augmented with Large LEO Constellation
## 3 Situation and Strategy
### Constellation Configuration  
In this work, four kinds of LEO constellations are simulated through Satellite Tool Kit (STK) [30] software to study the influence of LEO satellite number on augmented GNSS RTK performance, where 60-, 96-, 192-, and 288-LEO satellites schemes are selected for analysis, respectively. Aim for achieving the global coverage, these LEO satellites are allocated to 10 or 12 equally spaced orbital planes, with 6-24 satellites in each plane. Furthermore, these orbital planes of different LEO constellations, all belong to the polar circular orbits, are inclined at 90\({}^{\circ}\) to the equatorial plane and 1000 km in altitude [23]. And the detailed information of four LEO constellations is given in Table 1. Besides, the 24 GPS simulated satellites based on the nominal constellation configurations, are equally distributed over medium earth orbits and assigned in six orbital planes with a 56\({}^{\circ}\) inclination angle at 20,180 km in altitude [31]. Slots for the GPS 24-slot constellation are specified in terms of the Right Ascension of the Ascending Node (RAAN) and the Argument of Latitude, all which are listed in Table 2.  
Figure 1 shows the simulated constellations of GPS and LEO satellites, all of which clearly display the scale difference above the earth. As can be observed, the LEO satellites are very close to the earth, which means that the LEO satellites can move faster than GPS satellites during the same period. Compared with the 3.87 km/s operational speed and 11.97 h orbital period of GPS satellite, the LEO satellite at 1000 km orbit can reach speeds of 7.35 km/s and the correspondingly orbital period is about 1.75 h. However, at meanwhile, the above-mentioned phenomena also imply that the LEO constellation with smaller footprint must require more satellites to achieve global coverage. It is acknowledged that the 288-LEO scheme exhibits the best coverage of satellite visibility on a global scale. Based on  
this, more numbers of LEO satellites transmitting GNSS-like signals will help to improve observation quality and utilization for the ground tracking stations.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 3 Fractional Frequency Reuse
### ICIC Techniques
#### 3.1.3 Soft Fractional Frequency Reuse  
The last technique is the Soft Fractional Frequency Reuse (SFFR). It divides the bandwidth into three distinct sub-bands: center, middle, and edge, as illustrated on Figure 4. The middle sub-band has a reuse factor of 1 and the edge sub-band has a reuse factor of 3. The center region reuses the frequency band from the edge of the adjacent cells.  
Figure 3: Bandwidth distribution and power allocation for the soft frequency reuse algorithm in a cluster of three cells.  
Figure 2: Bandwidth distribution and power allocation for the Strict frequency reuse algorithm in a cluster of three cells.  
Each sub-band is served with a different power level, increasing from center to edge, and user allocation on sub-bands is also done based on the reported RSRQ.
## 4 Control and Monitoring of MRI-Assisted Interventions
### DT Supervision of MRI-Assisted Interventions  
The detailed DT supervision organization of an image-assisted robotic control is fashioned in this section. In general, concerning the abovementioned communications between the DT sides, the processed data of the physical side deliver detected information compared and adjusted by external Internet of Things \"IoT\" data and the learnt historical one. The outcome, once trained as data analysis, is conveyed, with an appropriate model reduction indication, to the DT virtual side. Actually, a swift communication (matching) between the sides of DT necessitates a faithful virtual model but with undersized execution time. Thus, the comprehensive coupled model, which realistically represents the physical procedure, would be reduced, allowing moderate computation time but preserving the picture of the real procedure. The management of an image-guided robotic control employing a DT pair fulfills an operational adaptive control procedure involving the real procedure matched with a reduced virtual model [80]. Figure 3 schematically illustrates the features of a DT monitoring an image-assisted robotic surgery. Such procedure monitoring could be used for staff training, predictions using physical phantoms and their digital replicas, or a real patient-virtual replica model, involving self-ruling matching with staff in the loop.  
The coupled model includes all components and actions of the image-guided robotic surgery procedure illustrated in Figure 1, namely, the MRI scanner, tissue-affected area, surgical tool, position and action processing, robotic control, and actuation. A significant part of the procedure is related to the MRI imaging involving the tissues and the surgical robotic tool. As mentioned earlier, the two components of the MRI magnetic fields related to the static and gradient fields are compensated and regulated, while the RF field arrangement is directly related to the images. This occurs through the processing and transformation of the field signals following the excitation and restoration of the B\({}_{1}\)-wave RF field. Thus, the  
Figure 3: Schematic illustration of the features of a DT monitoring an image-assisted robotic surgery.  
3-D RF field distribution in the birdcage comprising the tissues and the tool enables the outcome of the imaging process as well as the control of the MRI compatibility.
# Study on Microwave Deicing of Carbon-Fiber-Modified Concrete under Multi-Factor Coupling Effect
## 2 Materials and Methods  
### Raw Materials  
The material used in this test is ordinary Portland cement (P-O) produced by Leish Brothers Company in Hangzhou, with a density of 3.0 g/cm\({}^{3}\), a fineness of 1.7 and specific surface area of 340 m\({}^{2}\)/kg. The main chemical components of silica, calcium oxide and aluminum oxide accounted for 23.14%, 61.57% and 5.73%, respectively. Gravel from Yongshun Stone Processing Plant in Shijiazhuang city, Hebei Province, was selected as fine aggregate, with a fine aggregate density of 2.51 g/cm\({}^{3}\) and a bulk density of 1.48 g/cm\({}^{3}\). The coarse aggregate density was 2.75 g/cm\({}^{3}\), and bulk density was 1.68 g/cm\({}^{3}\), comprising a mixture of coarse aggregate medium gravel (20-40 mm), medium- gravel (10-20 mm) and small gravel (5-10 mm) in a ratio of 1:1.0.5. After full mixing, the mud content of the two aggregates was less than 0.4%. In this experiment, we used carbon fiber produced by Youlida Company, China. The diameter of the carbon fiber was 8 um, with tensile strength of 3 GPa, carbon content of 95%, Young's modulus of 228 GPa and resistivity between 1.2 \(\Omega\)-cm and 1.6 \(\Omega\)-cm, as shown in Figure 1.  
In the pouring process, concrete was prepared with a self-made model of 500 \(\times\) 500 \(\times\) 10 mm, which was maintained by standard curing method.  
To prepare concrete covered with ice, insert the upper part of the cured concrete specimen into the marked scale indicator ruler, place the central and edge portions of the concrete into the temperature sensor, and water the concrete in 3 batches. The watering depth of a single time is controlled from 30 mm to 40 mm. After watering, transfer the sample to cold storage (\(-10\pm 1\)\({}^{\circ}\)C) for 4 h and repeat the above steps to achieve the desired ice thickness.  
In Figure 2, part 1 and part 2 show temperature measuring devices for the central point position and the edge point position, respectively, and 4 identifies the ruler with marker scale.  
Figure 1: Basic morphology of carbon fiber after cutting.  
Figure 2: A concrete specimen covered with ice.  
For the experiment, we used microwave instruments with an independent microwave launcher joint structure design. In Figure 3, part 5 shows the microwave source selection produced by the Samsung ESGN model, with a transmitter microwave frequency of 2.45 GHz, and 6 and 7 identify the microwave component microwave diffusion trumpet, respectively, which were the target for the uniform microwave irradiation test.
# Problem-Driven Scenario Generation for Stochastic Programming Problems: A Survey
## 4 Problem-Driven Scenario Generation  
### Difference between Distribution-Oriented and Problem-Oriented Scenario Generation  
Classical scenario generation methodologies are mainly distribution-based. Statistical models are used to simulate the distribution of the random variables and then the sampling techniques are applied to generate the scenario-trees. In this sense, the quality evaluation of a scenario-tree is measured by how precise it can represent the statistical features of the underlying data. An example is the use of the Kolmogorov-Smirnov test [43], which is a widely used statistical test that measures the distance between the empirical distribution and the theoretical distribution.  
To illustrate, Figure 4 shows an example of a scenario-tree generated with dynamic scenario-tree generation methodologies proposed by [13] using a Julia package [44]. Given a predefined tree structure for a Gaussian random walk process across four stages, the scenario generation process uses random vectors drawn from conditional distributions that are conditioned on prior data points and sample trajectories, while incorporating a distance criterion that evaluates the fidelity of the approximation to generate the tree. The resulting scenario-tree is shown on the left side and the corresponding probability distribution of scenarios is shown on the right side. For more instances showing distribution-based scenario generation methodologies, we recommend the surveys in the literature [6; 7].  
While the distribution-oriented methods are commonly used to generate scenarios from the stochastic process before solving the Stochastic Programming problem, it is pointed out in [45] that, given the significance of the link between the scenario generation process and the decision model for the SP problem to be solved, there is no single scenario generation method that would be universally suitable for all Stochastic Programming models, even if they were influenced by identical random phenomena. Therefore, a concept of problem-oriented scenario generation has been proposed, in which the quality of the decision takes precedence over the precision of the probability distribution estimation.  
In the following section, we provide an overview of the existing literature concerning problem-driven scenario generation, which takes into account the role of the optimization problem in the scenario generation process in a certain way.