esa-sceva/satcom-chunk-collection huggingface dataset:
# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 2 Literature Review  
### Related Work  
#### 2.1.1 Research on Calculating Unimpeded Taxi-Out Time  
The Federal Aviation Administration's (FAA) Aviation Policy and Planning Office (APO) established a linear regression model to calculate UTT [2]. They grouped the flights by airline and season, calculated the number of departure flights and arrival flights in each group, and established a linear regression model for each group. By setting the coefficient of the departure flights queue variable to 1 and the coefficient of the arrival flights queue variable to 0 in the regression model, the UTT can be obtained. EUROCONTROL's European Performance Review Unit (PRU) proposed the concept of a congestion index to calculate UTT [2]. They grouped flights according to certain features, such as aircraft class, entry sector, runway, stand, and so on. Then, they counted the number of all departing and arriving flights during the period from when a flight pushes back to when it takes off, which is defined as the congestion index. Finally, they ranked the taxi times of departing flights in order of magnitude, took the 20 th percentile of these times as the UTT, and used it to calculate the congestion threshold. In addition, [PERSON] et al. [3] proposed an improved method for calculating the UTT with the handover point time. There are two phases in the process of an aircraft taxiing, from the ramp to its final arrival at the runway end, separated by the handover point. The first phase is the ramp tower control phase, and the second phase is the ATC tower control phase. To determine the UTT, they performed linear regressions on the departure and arrival flows for these two phases, respectively. They set the departure flow coefficient to 1 and the arrival flow coefficient to 0, and then summed the resulting times for each phase to obtain the final UTT.  
The domestic method for calculating the unimpeded taxiing time is the 20 th percentile method, in which flights grouped according to certain features are sorted in order from smallest to largest taxiing time, and the value at the 20 th percentile is taken as the unimpeded taxiing time. For busy airports with complex scenes and a large number of flights, this method is too simple and rough, leading to inaccurate calculations of the UTT.
## 5 Evaluation and Discussion
### Data Preprocessing Steps  
Data preprocessing involved several critical steps, initiated by importing feedback data into a panda DataFrame for the initial analysis. Subsequently, entries marked as \"None\" in specific columns were excluded to ensure the integrity of the data. The process then focused on refining the dataset for sentiment analysis by filtering out columns, specifically isolating those related to the \"rate\", and textual responses ranging from \"answer1\" to \"answer19\".
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 5 Antimicrobial Applications
### Antimicrobial Mechanism
#### 5.1.2 Oxidative Stress  
The main cause of microbial death is oxidative stress [94]. Due to the large surface area of graphene sheets, they can act as conductive bridges on which redox charge transfer occurs, further enhancing the oxidative stress potential [5]. Oxidative stress usually causes an imbalance between oxidation and antioxidants, which slows down the bacterial metabolism, disrupting cellular structure, and ultimately causing the bacteria to lose their vitality [91]. [PERSON] et al. showed that, after exposure to GO and rGO, glutathione (GSH) content was significantly reduced and ROS content increased by 3.8-fold and 2.7-fold in _POAI_, respectively [95]. Graphene-based membrane nanomaterials generate large amounts of ROS and are thought to be responsible for bacterial oxidative stress. It is generally believed that GO produces more ROS and possesses a higher GSH oxidation capacity than reduced GO. However, [PERSON] et al. found that the GSH concentration in _E. coli_ decreased while the ROS concentration did not increase after exposure to four graphene-based membranes [96]. Interestingly, conductive rGO and graphite have higher oxidative capacity for GSH than insulating GO and graphite oxide.
## 5 Results and Discussion
### Relative Importance of the Independent Variables  
Table 4 illustrates the relative importance of independent variables and the rank of relative importance in the prediction process. The importance of all independent variables was measured by relative importance, and the sum of their total importance was 100% [88]. The main findings and relative contribution of all the independent variables on global scale are presented in a bulleted list.  
* As shown in Table 4, the bus-metro connection characteristics account for 53.77%, and the bus-network density is the most significant factor affecting transfer ridership, accounting for 27.56%, which is consistent with the results of [89]. The relative contribution rates of bus-stop density, network repetition ratio of bus-metro, and average transfer distance are 17.27%, 8.49%, and 0.45%, respectively. Similar to [30], this result emphasizes the importance of forming interfaces between different transportation systems.
* The location of metro stations in a transit network plays an important role in predicting transfer ridership, accounting for 22.1%. Closeness centrality is an important index to measure the transfer efficiency of bus-metro, ranking second among all variables, and the relative contribution rate is 21.6%. The distance to the central station has a small correlation with transfer ridership, respectively.
* The land-use attributes for trip attraction play a great guiding role in increasing the attraction of rail transit [90]. In this paper, the land-use attributes for trip attraction account for 5.39%, especially the commercial ratio, which contributes 3.28%. By contrast,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **Traditional Linear Model** & **Random Forest** & **Light GBM** & **XGBoost** \\ \hline R2 & 0.701 & 0.594 & 0.603 & 0.821 \\ MAE & 0.247 & 0.125 & 0.105 & 0.098 \\ MSE & 0.065 & 0.027 & 0.023 & 0.017 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the traditional linear and nonlinear model.  
the land-use attributes for trip production only account for 11%, which influence the ridership slightly. The result shows that the developed business circle in a region will drive the development of the surrounding bus-metro combined travel.
* In terms of demographic factors, population density (ages between 20-44) is an important factor, with a contribution rate of 5.37%, which can be attributed to younger employees that prefer to travel by public transport. The regional PGDP reflects the private car ownership in the region, to a certain extent. The number of private cars has a negative impact on passengers' bus-travel mode, so the number of private cars should be reduced [49; 91; 92].
# Reversible Image Processing for Color Images with Flexible Control
## 2 Related Works
### Contrast Enhancement Methods for Color Images  
To enhance the contrast without hue distortion, effective methods have been proposed for color images [11; 12]. A reversible CE method for color images [11] was published that combines the advantages of previous methods [5; 10]. This method applies a single reversible CE algorithm to the \(R\), \(G\), and \(B\) components independently, and raw images are perfectly restored. This method not only preserves the brightness but also refines the CE effect. However, hue distortion is still caused. [PERSON] et al. [12] designed another method by using the HSV color space. Their method enhances the brightness contrast, which is simply referred to as contrast hereafter, without saturation or hue distortion. However, rounding errors arise in the preservation functions for the saturation and hue. The restored images have a high image quality, but raw images cannot be fully reconstructed.  
These previous methods [11; 12] can carry out the CE function on color images. Nevertheless, they only focus on the CE function, and other functions have not been considered. We previously proposed a reversible method [13] for color images based on the method in [12] to not only enhance the contrast but also improve the saturation.
# Broadening of Light Coupling to Waveguide Modes in Solar Cells by Disordered Grating Textures
## 4 Conclusions  
We investigated the physical mechanism of the spectrally broad light coupling to waveguide modes in thin-film solar cells by disordered grating textures at the interfaces of the absorber layer by rigorous optical simulations with FDTD. We demonstrated that the improvement of the short-circuit current density of the solar cells achieved by introducing disorder to the grating, compared to the ordered grating, is due to a combination of a broadening of the wave vector transfer by the disordered grating texture and a spectral broadening of the waveguide mode itself. Our conclusions are supported by a detailed FFT analysis of the interface textures and an analysis of the variation of the shape of the unit cells, taking into account the isotropic growth of the absorber layer. We found that the broadening of the wave vector transfer by the disordered grating texture leads to an improvement of short-circuit current density with a saturation behavior. The broadening of the waveguide mode itself leads to an improvement of short-circuit current density only for moderate disorder due to the disturbance of the extended optical mode. Therefore, the improvement of short-circuit current density is reduced for higher disorder in this case.  
The authors thank [PERSON], [PERSON] and [PERSON] from RWTH Aachen for fruitful discussions. The FDTD simulations are carried out at the JURECA HPC cluster at Julich Supercomputing Center in the frame of the VSR project JIPV00. The financial support of the Helmholtz Association's Initiative and Networking Fund for the Helmholtz Young Investigator Group of [PERSON] is highly acknowledged.  
[PERSON] wrote the paper, [PERSON] performed the optical simulations. [PERSON] and [PERSON] designed the input structures and analyzed the data. [PERSON]  
Figure 6: Impact of different types of disorder on the short-circuit current density of the a-Si:H solar cell in the wavelength range from 635 nm to 780 nm.  
[PERSON] and [PERSON] supported the work with discussions and helped with the interpretations of the results.  
The authors declare no conflict of interest.
## 4 Results
### Illustrative Use Case: Applying the Questionnaire to a Hypothetical Platform  
To demonstrate the practical utility of the stakeholder-oriented questionnaire, we present a hypothetical use case involving an AI/ML competition platform designed for reinforcement learning agents. An organizer wishes to evaluate the platform's technical debt profile before launching a new edition of the competition.  
By completing the questionnaire, the organizer identifies several areas of concern:  
* In the Infrastructure Debt category (Q33-Q34), both items receive a NO response, indicating that the submission environment lacks container-based isolation and resource monitoring. Given their severity weight of 5, this yields a subtotal of 10 in this category.
* For Documentation Debt (Q26-Q28), two items are answered NO and one I Don't Know, with weights 4, 3, and 2, respectively, resulting in a subtotal of 9.
* In Accessibility Debt (Q58-Q60), all three items receive NO responses. With assigned severity weights of 3, 2, and 2, the subtotal is 7 Calculated \(-\)7.
* Conversely, in categories such as Model Debt (Q35-Q36) and Configuration Debt (Q7-Q9), most responses are YES or Not Applicable, contributing minimally or neutrally to the total debt score.  
The platform's preliminary technical debt score is calculated by summing the weighted debt contributions across all 19 categories. In this scenario, the subtotal scores suggest disproportionately higher debt in infrastructure, documentation, and accessibility.  
Based on these observations, the organizer prioritizes the following mitigation actions:  
* Containerizing the submission pipeline and introducing system-level resource controls.
* Updating the documentation portal with clear API usage instructions and dataset versioning details.
* Integrating an accessibility audit checklist into the UI design process, including keyboard navigation and WCAG-compliant color contrast.  
While this scenario is hypothetical, it reflects common challenges reported in the literature and showcases how the questionnaire can guide organizers in systematically identifying and addressing technical debt prior to competition deployment. The ability to quantify debt by category and severity also supports evidence-based prioritization.
## 4 Results and Discussion
### Data Quality Improvement and Application of Results
#### 4.3.2 Application in Safety Research and Policy  
The enhanced pedestrian crash dataset has significant implications for transportation agencies, urban planners, and policymakers. It offers high-quality, data-driven insights to support the development of effective pedestrian safety measures. By addressing inconsistencies in crash data and supplementing them with additional critical features, this study provides a more comprehensive and reliable foundation for safety analysis. The improved dataset enables researchers and decision-makers to implement data-driven safety interventions more confidently.  
One key application of this study is the enhancement of pedestrian crash severity modeling, whereby newly extracted features contribute to more accurate and reliablepredictions of crash outcomes. Additionally, refined data variables improve risk assessment and countermeasure planning, allowing predictive models to identify high-risk locations and suggest appropriate safety measures. Another significant impact is the potential for automated real-time crash data validation systems, which can assist transportation agencies in continuously improving crash reporting accuracy and ensuring more reliable data collection for future research and policy decisions.
# Capabilities and Limitations of Using Desktop 3-D Printers in the Laser Sintering Process
## 3 Results and Discussion
### Powder Flowability  
The powder flowability influences both the material delivery and application on the work surface. Characterized by the Hausner ratio, LS powders should typically be below a threshold level of 1.25 [8,16]. The Hauser ratio is listed in Table 3, and the best static flowability is given by PA12 Smooth. Every tested powder meets the acknowledged requirements and is classified as having good flowability, which confirms their suitability for the LS process. However, as previously mentioned, the HR refers to static conditions and does not consider the dynamic behavior that prevails during the application of the powder in the manufacturing process. The dynamic cohesive index in relation to rotating speed was determined, and is showed in Figure 3. PA2200 and PA12 Black have a similar dynamic behavior of flow properties, which is described by the manufacturer of the testing device as good (5-10) or fair (10-20). However, it should be taken into account that the ranges provided by the manufacturer of the testing device do not explicitly refer to the specificity of powder application in the LS process. In this case, PA2200 was used as a reference. PA12 Smooth acts as a cohesive powder that gives an irregular flow with a higher average floating angle. Poor flow properties, especially at higher rotating speeds, resulted in an inability to evaluate data obtained above 20 rpm.  
Figure 3: Cohesive index curves recorded as a function of the increasing (\(\uparrow\)) and decreasing (\(\downarrow\)) rotation speed for polyamide powders.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**HR [a.u.]**} & \multirow{2}{*}{**AA [\({}^{\circ}\)]**} & \multirow{2}{*}{**\(\alpha_{t}\) [\({}^{\circ}\)]**} & \multirow{2}{*}{**\(\alpha_{t}\) [\({}_{\blacksquare}\)]**} & \multirow{2}{*}{**d50 [\(\mu\)m]**} & \multirow{2}{*}{**(d90-d10)/d50**} & \multirow{2}{*}{**\(<\)10 \(\mu\)m [\%]**} \\ \cline{5-6}  & & & & & & & \\ \hline PA2200 & 1.14 & 40.1 & 52.8 & 20.9 & 59.3 & 0.87 & 1.52 \\ PA12 Smooth & 1.09 & 62.9 & 51.4 & 60.5 & 41.0 & 0.64 & 0.0 \\ PA12 Black & 1.14 & 37.6 & 36.0 & 25.5 & 59.4 & 0.93 & 1.62 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The Hausner Ratio (HR), First Avalanche Angle, Flowing Angle, Cohesive Index and Particle Size Distribution of Different Machine Manufacturer’s Polyamide 12 Powders.  
Figure 2: SEM images of powder particles (**a**) PA2200, (**b**) PA12 Smooth, (**c**) PA12 Black.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 3 Experiments Results and Security Analysis
### Performance Analysis of the Proposed PRNG-CS  
We give below the performance obtained by the proposed chaotic generator.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 3 Machine Learning  
Machine Learning (ML) models are computational systems that are able to learn the features of a system that cannot be represented by using a conventional mathematical model approach. These models are commonly used in tasks such as regression, classification, and any interaction between an intelligent agent and an environment. After the model is trained on the given training data-set, it can be effectively applied to unknown data and  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Technology** & **5G** & **6G** \\ \hline \multirow{4}{*}{Applications} & Enhanced Mobile Broadband & Holographic-Type \\  & Communications (eMBB), & Communication (HTC), Tactile \\  & Ultrareliable Low Latency & Internet, Intelligent Transport and \\  & Communications (URLLC), & Logistics, Intelligent and \\  & Massive Machine Type & automated machines, Virtual \\  & Communications (mMTC) & Reality (VR), Augmented Reality \\  & & (AR), Extended reality (XR) \\ \hline Peak data rate & 10 Gbps & 1 Tbps \\ \hline Frequency & 3–300 GHz & 1000 GHz \\ \hline Latency & 10 ms & \textless{}1 ms \\ \hline Mobility support & Up to 500 km/h & Up to 1000 km/h \\ \hline Spectral efficiency & 30 bps/Hz & 100 bps/Hz \\ \hline Reliability & 99.9999\% & 99.99999\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of 5G and 6G networks.  
perform any decision based on the training data. ML is usually classified into three major categories [13]: supervised, unsupervised, and reinforcement learning.
## 2 Proposed Methodology
### Image Cropping and Data Augmentation  
According to [PERSON] et al. [29], the quantity of images or data directly influences the convergence of CNN classification models. As a result, offline techniques are utilized to enhance the database, encompassing suggested approaches such as rotation, inversion, and contrast enhancement. Furthermore, [PERSON] et al. [30] proposed data augmentation through geometric transformations like horizontal and vertical flips to expand the database while preserving the inherent characteristics of the original images. An additional method employed to enlarge the database involved modifying the initial images using slight amounts of salt and pepper noise. Furthermore, there are documented studies where researchers have examined CNN's resilience by employing this approach [31]. This paper presents an initial database containing 600 thermal images of size 320 \(\times\) 240 pixels; these images were acquired during the experimental setup; there are 50 images for each class or the different conditions of the induction motor. Initially, the region of interest of the original images was selected, and the image was cropped to a size of 140 \(\times\) 70 pixels, which is the size required to have the complete image of the motor and gearbox. The eliminated information corresponds to the background and the load, which does not affect the objective of this work. On the contrary, this reduction in the image area allows less information to be processed, resulting in a lower computational load and faster training and inference time. To increase the database by 200 images for each test listed in Table 1, pre-processing was performed by changing the intensity levels in each original image by 10%, 20%, and 30%, resulting in a database of n = 2400. Changes in intensity levels are one of the techniques used as they help improve the training of the network, besides being one of the main scenarios when acquiring thermographic images [32]. Then, a horizontal flipping transformation was applied to the resulting database, obtaining a total of 4800 images. The optimal CNN configuration to achieve the expected results was determined from this database.
## 1 Introduction
The objective of this paper is to investigate the dynamic compressive mechanical properties of the 3D four-step braided composites along the longitudinal direction after cyclic hygothermal aging. All of the research in this paper can be divided into three stages: first, identify the moisture absorption of 3D four-step braided composites after cyclic hygothermal aging, observe morphology evolution of the cross section for the 3D four-step braided composite by using SEM; then, quantify the dynamic mechanical property degradation caused by the cyclic hygothermal aging effect; finally, evaluate the cyclic hygothermal aging effects on the dynamic compressive mechanical performance of 3D four-step braided composites. In this work, the 3D four-step carbon/epoxy (T700-12K/TDE-86) braided composites were exposed to cyclic hygothermal environment. An accelerated hygothermal aging spectrum for a military aircraft was applied. SEM was used to observe composites' morphology changes at different cyclic hygothermal aging days. A split Hopkinson pressure bar (SHPB) was employed to evaluate the dynamic compressive mechanical property along the longitudinal direction of the 3D four-step braided composites at various cyclic hygothermal aging days.
## 3 Acoustic Emission Analysis
### Attenuation Coefficient B  
In order to evaluate energy attenuation [28], the ratio of AE signal energies recorded at both sensors is calculated for each source \(n\). For an easier identification of attenuation coefficient \(B\), \(x(n)\) is defined as the natural logarithm of this ratio. From Equations (2) and (3), it comes:  
\[\mathrm{X}(n)=\mathrm{log}\frac{\mathrm{E_{1}(n)}}{\mathrm{E_{2}(n)}}=\mathrm{ log}\frac{\mathrm{A_{1}}}{\mathrm{A_{2}}}-2\cdot\mathrm{B}\cdot\mathrm{x}(n) \tag{5}\]  
The second term corresponds to the effect of propagation, related to source location \(x(n)\) and to attenuation coefficient B. Both parameters can be estimated from \(x(n)\) for various sources (1,2,..., n,...) since \(x(n)\) is a linear function of \(x\). The coefficient of attenuation B can be determined from the slope of the linear fit.  
Because attenuation varies with damage, parameters estimation is done at successive time intervals. For a given time interval, values of \(x(n)\) are analyzed in many space intervals in order to consider uncertainties in localization of AE sources. Estimation of attenuation constants was performed as follows. Attenuation is evaluated for each time interval with the median values of \(x(n)\) in all space interval (width: 10 mm, overlapping: 5 mm). Each time interval is defined by 2000 AE successive sources. Each median value of \(x(n)\) corresponds to a few hundred AE sources located in the same space-time interval. The linear approximation is repeated in consecutive time intervals. At every increment of 500 sources, a new time interval is considered. The overlapping is set in order to accurately monitor evolution of both attenuation parameters.