esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework to Quantify the Quality of Source Code Obfuscation
## 6 Experiment
### Potency Measurement Results  
Figure 4 displays the outcomes of the five indicators used to quantify Potency. Count is the unit of all measurements in Figure 4. Stunnix C/C++ Obfuscator and Semantic Designs C-GCC4 Obfuscator showed no impact on the McCabe cyclomatic complexity, control flow graph size, control flow depth, and instruction count. It is notable, however, that the values of program length were affected. This occurred because Stunnix C/C++ Obfuscator removes newlines and white spaces during format alteration, while Semantic Designs C-GCC4 Obfuscator adds them. Conversely, Tigress Obfuscator and Obfuscator LLVM showed an increase in all metrics compared to the baseline. Specifically, the numbers increased when we added the options in Tigress Obfuscator. Obfuscator LLVM reveals a significant increase in program lengths and instructions due to the addition of numerous bogus control flows. Table 4 details the quantification results for obfuscation Potency.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Tools**} & \multirow{2}{*}{**McCabe**} & \multicolumn{5}{c}{**Potency (Num)**} \\ \cline{3-7}  & & **McCabe** & **CFG Size** & **CF Depth** & **Program Length** & **Instruction Count** \\ \hline Baseline & Average & 6.60 & 14.00 & 5.20 & 94.08 & 79.17 \\ \hline Stunnix C/C++ Obfuscator & Average & 6.60 & 14.00 & 5.20 & 33.32 & 79.17 \\  & Growth Rate & 0\% & 0\% & 0\% & \(-\)64.54\% & 0\% \\ \hline Semantic Designs C Obfuscator & Average & 6.60 & 14.00 & 5.20 & 116.89 & 79.17 \\  & Growth Rate & 0\% & 0\% & 0\% & 24.77\% & 0\% \\ \hline Obfuscator LLVM & Average & 43.71 & 111.86 & 5.78 & 723.14 & 1311.50 \\  & Growth Rate & 530.50\% & 735.44\% & 78.25\% & 674.41\% & 1529.57\% \\ \hline Tigress C Obfuscator (Level 1) & Average & 7.28 & 13.95 & 4.37 & 119.48 & 126.70 \\  & Growth Rate & 12.77\% & 7.51\% & \(-\)6.26\% & 27.55\% & 59.02\% \\ \hline Tigress C Obfuscator (Level 2) & Average & 12.87 & 24.78 & 5.61 & 343.41 & 363.35 \\  & Growth Rate & 103.42\% & 116.41\% & 44.82\% & 264.23\% & 359.91\% \\ \hline Tigress C Obfuscator (Level 3) & Average & 49.59 & 69.50 & 5.00 & 654.50 & 502.63 \\  & Growth Rate & 679.42\% & 533.65\% & 57.86\% & 595.34\% & 536.32\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of measurement for quantifying obfuscation quality—Potency.
## 3 Image Feature Extraction and Feature Matching Based on SIFT  
The problem of charging port identification in the target area must be first solved to achieve automatic charging. The charging pile is generally placed outdoor with strong electromagnetic field, due to which the collected images are easily affected by noise. Therefore, the adopted charging port image identification method should possess good anti-interference performance in addition to strong robustness under different ambient light backgrounds. The target detection algorithm based on binocular vision is one of the most promising, as well as practical, methods in recent years, with its key lying in the feature point extraction and feature matching algorithm. SIFT (Scale-invariant feature transform), which is a scale-invariant feature transform algorithm, is a local feature detection method based on spatial scale extreme points. As the algorithm has invariance to rotation operation, scaling operation and brightness change in addition to strong robustness to noise and the characteristic of scalability, it can accurately extract the corner features in the image [11]. The extracted image features are then matched with the pre-stored charging port features to complete the identification function of the charging port. Figure 2 shows the flowchart of the image identification process of the charging port.  
The extraction of feature points by the SIFT algorithm can be realized by steps such as the construction of the scale space, the calculation of the spatial extreme points, the positioning of the stable key points, the information distribution of the direction of the stable key points, as well as the description of the key points which are shown as follows.
###### Abstract  
Electroencephalography (EEG)-based measurements of fine tactile sensation produce large amounts of data, with high costs for manual evaluation. In this study, an EEG-based machine-learning (ML) model with support vector machine (SVM) was established to automatically evaluate post-stroke impairments in fine tactile sensation. Stroke survivors (\(n=12\), stroke group) and unimpaired participants (\(n=15\), control group) received stimulations with cotton, nylon, and wool fabrics to the different upper limbs of a stroke participant and the dominant side of the control. The average and maximal values of relative spectral power (RSP) of EEG in the stimulations were used as the inputs to the SVM-ML model, which was first optimized for classification accuracies for different limb sides through hyperparameter selection (\(\gamma\), C) in radial basis function (RBF) kernel and cross-validation during cotton stimulation. Model generalization was investigated by comparing accuracies during stimulations with different fabrics to different limbs. The highest accuracies were achieved with (\(\gamma=2^{1}\), C = \(2^{3}\)) for the RBF kernel (76.8%) and six-fold cross-validation (75.4%), respectively, in the gamma band for cotton stimulation; these were selected as optimal parameters for the SVM-ML model. In model generalization, significant differences in the post-stroke fabric stimulation accuracies were shifted to higher (beta/gamma) bands. The EEG-based SVM-ML model generated results similar to manual evaluation of cortical responses to fabric stimulations; this may aid automatic assessments of post-stroke fine tactile sensations.  
Stroke; fine tactile sensation; electroencephalography; machine learning; evaluation +
Footnote †: journal: Applied sciences
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 6 Conclusions and Future Work  
In this study, we proposed the combined CNN-BILSTM model for forecasting short-term traffic flow on highways. First, the spatial features of the traffic flow data were extracted through the CNN model, and the output data were inputted into the BILSTM model for temporal feature extraction and prediction. Subsequently, the results were compared with alternative forecasting models. The comparative analyses of the prediction performances of the models revealed the superior performance of the proposed model, including the cases of forecasting on overall data, categorizing data into daily and weekly cycles to account for the influence of periodicity, or considering the prediction effect of the model at various time intervals. The prediction precision of the model was greater than that of the comparison model, which established the enhanced prediction effect of the proposed model.  
Although the proposed model offers several advantages for predicting traffic flow, it poses certain limitations, as well. In the future, we should take into account the influence of speed on traffic flow and take into account the influence of weather factors, occupancy rate, and road construction. In this context, we can further study multilane traffic prediction, which is not limited to the prediction of total traffic flow. This will further enhance the precision of the model.  
Writing--original draft, Y.C.; Writing--review & editing, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Fujian Zhi-Iian-yun Supply Chain Technology and Economy Integration Service Platform from the Fujian Association for Science and Technology, the Fujian-Kenya Silk Road Cloud Joint R&D Center (2021D021) from Fujian Provincial Department of Science and Technology, the Fujian Social Sciences Federation Planning Project (FJ2021Z006), General program of Fujian Natural Science Foundation (2022)J01941) and the Development Fund of Scientific Research from Fujian University of Technology (GY-S18109).  
Not applicable.  
The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author.  
The authors would like to thank the reviewers and editors for improving this manuscript.  
The authors declare no conflict of interest.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 2 Communication Networks for UAVs
### Parameters for Measuring the Performance and Quality of the Data Transmission Network
#### 2.2.2 Throughput  
[PERSON] et al. [42] define the throughput as the ability of data packets to be successfully sent from the source node to the destination one in the time unit. In this study, this measure is given in bits per second and calculated as Equation (2):  
\[\tau=\frac{8\,Tps}{Tlps-Tfps} \tag{2}\]  
where \(Tps\) is the total number of sent bytes and \(Tlps\), \(Tfps\) are the time instants at which the last and the first packets were sent, respectively.
## 3 Results and Discussion
### Choice of the \(R_{0}\)
This finding signals that there are more accurate estimations with \(T_{soil}\) and agrees with [PERSON] and [PERSON]'s (1995) [24] notes on the Q\({}_{10}\) temperature coefficients that the microbial biomass responsible for SR is better reacted to the \(T_{soil}\) changes--the immediate substrate--than to the \(T_{air}\) changes. This conclusion also agrees well with the observed (Figure 4) significant spread of the air temperatures (\(T_{air}\approx\) -12-+2 \({}^{\circ}\)C) for the soil temperatures close to zero Celsius (\(T_{soil}\) = 0 \({}^{\circ}\)C), seen at both sites and fewer smaller spreads of the soil temperatures (\(T_{soil}\approx\) 0+5 \({}^{\circ}\)C) for \(T_{air}\) = 0 \({}^{\circ}\)C. On the other hand, the \(T_{soil}\approx\) 2 \({}^{\circ}\)C threshold serves as a clear indicator of the temperature-regime change. When \(T_{soil}\) > 2 \({}^{\circ}\)C, air and soil temperatures are in a close coupling with each other (red line, Figure 4), while for \(T_{soil}\leq\) 2 \({}^{\circ}\)C, this coupling behavior has been effectively broken due to the strong influence of liquid water keeping soil from freezing. The individual values of the measured SR at near zero temperatures, when there is not any freezing of the top-soil level occurred (colored dots, Figure 3), are generally associated with the end-of-the-year cold periods with not very large monthly precipitation (Figure 2). Investigating the large scattering of these SR values, we found some evidence that they depend both on the monthly precipitation and soil properties together. For the Entic Podzol (sandy soil with poor water-holding ability and larger pores), the lower precipitation  
Figure 4: The interrelationship between \(T_{soil}\) measured at the sites and \(T_{air}\) obtained from the monitoring station; horizontal blue line—\(T_{air}\) = 0 \({}^{\circ}\)C; vertical brown line—average soil temperature \(T_{soil}\approx\) 2 \({}^{\circ}\)C for \(T_{air}\approx\) 0 \({}^{\circ}\)C; red line—warm-period; and blue line—cold-period linear fits. periods (red and brown dots, Figure 3 left) have the extremely low SR--good drainage easily dries out this soil. However, for Haptic Luvisol (loamy soil with high water-holding ability and smaller pores), the lower precipitation periods (red and brown dots, Figure 3 right) are actually associated with the higher SR--this soil is over saturated with water [36] in cold periods. These evidences point out the importance accounting for the soil properties in SR modeling. All those mentioned above ensure us that the \(R_{0}\) values we obtained from the modeling (colored horizontal lines, Figure 3) behave as expected in comparison to the measurements.
## 3 ULFO Suppression Control Method for Hydropower and PV  
### Cascade Correction Control of Hydropower  
The governor of the hydropower generator has an important influence on the generation of ultra-low-frequency oscillation, but modifying the parameters of the governor will affect the effect of the primary frequency modulation of the hydropower generator. In order not to affect the performance of primary frequency modulation, this paper designs a cascaded correction controller on the governor side. The frequency adjustment model of a single hydropower generator after adding the controller is shown in Figure 5.  
Among them, the mathematical model of the governor, hydro turbine, and generator has been introduced in detail in Section 2. The cascaded correction controller includes three parts: gain, high-pass filter, and lead-lag link. The mathematical expression of its transfer function is:  
\[G_{c}(s)=K_{c}\frac{sT_{f}}{1+sT_{f}}\frac{1+sT_{1}}{1+sT_{2}}\frac{1+sT_{3}} {1+sT_{4}} \tag{13}\]  
where \(K_{c}\) is the proportional coefficient of the gain link of the controller, \(T_{f}\) is the time constant of the high-pass filter link, and \(T_{i}\) (\(i\) = 1, 2, 3, 4) are the time constants of the lead-lag link, generally set to \(T_{1}\) > \(T_{2}\) > 0 and \(T_{3}\) > \(T_{4}\) > 0, so that the transfer function can provide phase-lead compensation. According to the damping torque analysis method, when the ultra-low-frequency oscillation angular frequency of the system is \(\omega_{s}\) and the series correction link provides an advanced phase for compensation, the damping torque of the system is:  
\[D_{T}=\mathrm{Re}[G_{c}(j\omega_{s})G_{gov}(j\omega_{s})G_{h}(j\omega_{s})] \tag{14}\]  
Figure 5: Control structure of the hydropower generator.  
After adding the series correction link, a leading phase is generated, and the damping torque changes from negative to positive, providing positive damping for the system, which can effectively suppress ultra-low-frequency oscillation [29].  
It can be seen in Figure 6 that before the series correction controller is added, the negative damping torque coefficient of the system in the ultra-low-frequency band is very large. After adding the series correction controller, the damping torque coefficient of the system in the ultra-low-frequency band has increased and improved.
## 5 Discussion and Implications
### Current Implementation of ML and GenAI in Higher Education (RQ1)  
The integration of machine learning and generative AI into learning analytics follows two methodological paths: the consolidation of traditional ML models and the exploratory adoption of GenAI systems.  
ML-based LA systems are well-established for predicting academic performance (e.g., random forest, SVM, logistic regression), detecting dropout (e.g., LSTM, GRU, decision trees), modeling engagement (clustering, sentiment analysis), and optimizing feedback using k-means or regression. These tools operate mainly on structured LMS data--clickstreams, assessments, participation logs--and are valued for accuracy and simplicity, though they often lack alignment with classroom practice.  
GenAI-based LA systems are emerging in personalized feedback (LLMs like GPT-4, BERT), emotional and cognitive engagement modeling (sentiment/discourse analysis), SRL support (tutoring agents), and real-time content scaffolding. These implementations are typically experimental, text-focused, and integrated through prompt engineering or fine-tuning. While studies--such as RoBERTa for peer feedback [123] and GenAI-enhanced instructional design [124]--show promise, most lack empirical validation, scalability, or connection to learning theory. Additionally, multimodal inputs (e.g., gaze, audio, gesture) remain underutilized.  
ML-based LA systems are mature, data-driven, and prediction-focused; GenAI systems are flexible, interaction-oriented, and still experimental. Despite their complementarity, both paradigms face common limitations: fragmented implementation, weak pedagogical integration, and minimal educator involvement.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 3 Background
### SIR Model  
The SIR model is an epidemiological model used to describe the spread of infectious diseases in a population [32] and has been a foundational model in epidemiology for nearly a century. It illustrates fundamental trade-offs and provides a simple framework that can be easily built on. Subsequent work in epidemiological theory has extended the model in various ways, and modern epidemiological forecasts typically work with variants that are more flexible in a number of dimensions [33]. The SIR model divides the population into three categories: Susceptible--this group is not immune and can be infected; Infected--this group is already infected with the disease and can spread it to susceptible people; Recovered--this population has recovered and gained immunity and is no longer infected or transmitting the disease. The modeled transmission process can be described by Equation (4):  
\[\left\{\begin{array}{l}\frac{dS}{dt}=-\beta*S*I\\ \frac{dI}{dt}=\beta*S*I-\gamma\\ \frac{dR}{dt}=\gamma*I\end{array}\right. \tag{4}\]  
where \(S\) represents the number of susceptible persons, \(I\) represents the number of infected persons, \(R\) represents the number of recovered persons, \(\gamma\) is the immunization rate, indicating the number of recovered persons per unit time, and \(\beta\) is the infection rate, indicating the number of infected persons that can transmit to susceptible persons per unit time.  
The propagation process of the SIR model is as follows: A node is initially designated as the source node and set to the infected state. During the first infection, its neighboring nodes are randomly infected based on a given propagation probability. After the infection is completed, the source node is set as a recovered node. At this point, newly infected  
Figure 1: The k-shell decomposition process: where the number represents the node ID. Nodes with the same color mean that they are in the same layer. Circles of different colors represent different layers.  
nodes continue to spread the infection in subsequent rounds until only susceptible and recovered nodes remain in the network.
## 1 Introduction
### Sensor deployment and target detection distance
Experimental results from data collected by dogs vary due to the quality of protocols used to train the dogs and the capabilities of personnel involved in the research. It is particularly challenging to accurately design and execute a research study where dogs are trained for olfactory investigations and then used to collect data. Dogs learn independently, often picking up on subtle clues from their handlers or observers who are unaware they are influencing the dogs' responses [34]. Dogs are also exceedingly more sensitive than instrumentation, thus great care is required to prevent cross contamination of target and non-target material. Also as a result of the dog being more sensitive than instrumentation and better at detecting certain targets than humans is the fact that the dog's correct response cannot always be validated by humans, either quickly if at all. Finally, researchers are typically not trained nor do they necessarily understand that these behavioral and physical sensitivities exist [35]. It is easy to inadvertently train a dog to detect an unexpected or impure source when attempting to train to a pure compound. This error is amplified in the field setting where the complexities of dog-handler dynamics, terrain, weather, 'noisy' odor conditions, and stress affect dog performance. Finally, the need exists to conduct studies quantifying POD for dogs searching an outdoor, natural environment. Search behavior in a laboratory setting has been evaluated for rats [36] and researchers at Sokoine University of Agriculture in conjunction with the University of Antwerp have developed grid search strategies for rats trained to detect buried landmines in the field setting [37]. Given that dogs were shown to be effective and reliable at finding Mojave desert tortoises we sought to devise a methodology that would capture the fine detail of a dog working to target source for the purpose of determining what an expected range of detection distances for Mojave desert tortoises on the landscape surface (as opposed to in burrows underground) might be. Ultimately this methodology and resulting information could be used to refine search strategy and reported POD during actual surveys and in other search and detection operations.
# Title-to-Length Coupling Analysis of an Off-Axis Optical Bench Design for NGGM
## 1 Introduction
However, the assembly and alignment errors of the optical components are not considered and a certain idealistic assumption is also made on the coincidence of the phase centre of the optical system with the centre of mass of a satellite. As a result, the TTL coupling noise is grossly underestimated. It is the aim of the present work to consider a more realistic scenario and strive to understand in depth, both the local as well as the global TTL coupling noise. This will pave the way for their possible mitigation in the prototype development stage. The paper is structured as follows. In Section 2, the detailed design of the off-axis optical bench layout is presented, with more refinements added compared with the previous design. Section 3 presents the analysis of the local TTL originated from the assembly and alignment errors of optical components. With the employment of 1B data on star sensors from the GFO mission, in Section 4 we probe into the global TTL noise generated by the relative attitude jitter between two spacecraft. Some remarks are made concerning future work in Section 5 to conclude the present work.
## 3 Design of Experiments
### Latin Hypercube Sampling
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \(\mathbf{C/C_{\text{ref}}}\) & \(\mathbf{D/C_{\text{ref}}}\) & \(\mathbf{G/C_{\text{ref}}}\) & \(\mathbf{\theta_{\text{up}}}\) & \(\mathbf{\theta_{\text{down}}}\) & \(\mathbf{\theta_{\text{total}}}\) \\ \hline g01 & 1.00 & 0.18 & 0.61 & 31.50 & 23.37 & 54.87 \\ g02 & 0.96 & 0.15 & 0.70 & 49.46 & 21.83 & 71.29 \\ g03 & 1.01 & 0.14 & 0.39 & 29.67 & 17.15 & 46.82 \\ g04 & 1.03 & 0.13 & 0.51 & 23.70 & 13.98 & 37.68 \\ g05 & 0.98 & 0.17 & 0.50 & 38.14 & 23.66 & 61.80 \\ g06 & 1.03 & 0.14 & 0.44 & 25.31 & 16.01 & 41.32 \\ g07 & 0.96 & 0.16 & 0.91 & 53.34 & 23.41 & 76.75 \\ g08 & 1.02 & 0.16 & 0.59 & 26.64 & 19.98 & 46.61 \\ g09 & 1.05 & 0.15 & 0.68 & 21.46 & 15.80 & 37.26 \\ g10 & 1.04 & 0.17 & 0.97 & 22.27 & 19.10 & 41.37 \\ g11 & 0.95 & 0.16 & 0.64 & 58.05 & 24.23 & 82.28 \\ g12 & 0.97 & 0.14 & 0.73 & 43.44 & 18.33 & 61.77 \\ g13 & 0.98 & 0.17 & 0.95 & 40.13 & 24.29 & 64.42 \\ g14 & 1.01 & 0.14 & 0.59 & 28.63 & 15.54 & 44.17 \\ g15 & 1.00 & 0.15 & 0.74 & 33.60 & 19.36 & 52.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Geometrical definition of the set of samples considered for the definition of the surrogate models. Figure 3: Samples definition in the geometric parametric space (**a**) 3D view, (**b**) projection on the \(D/C_{\text{ref}}\) vs. \(C/C_{\text{ref}}\) plane, (**c**) projection on the \(G/C_{\text{ref}}\) vs. \(C/C_{\text{ref}}\) plane and (**d**) projection on the \(G/C_{\text{ref}}\) vs. \(D/C_{\text{ref}}\) plane.