esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Association between the KRAS Expression and LUAD Patient Clinical Features  
The relationships between _KRAS_ gene expression and the clinical characteristics and demographic status LUAD patients were determined from the UALCAN database (Figure 3). Firstly, expression of _KRAS_ was found to be higher in tumor samples (n = 515) than in normal tissue (n = 59) (\(p\) = 7.88 \(\times\) 10\({}^{-13}\)). _KRAS_ expression level was also found to be significantly higher in all cancer stages than in normal samples (\(p\) < 0.05). _KRAS_ was overexpressed in both men and women compared to non-cancerous samples (\(p\) < 0.05). Moreover, significantly higher _KRAS_ expression was noted among all age groups except for 21-40 years in LUAD samples. Figure 3e shows _KRAS_ expression for current smokers, reformed smokers, and non-smoker.  
_KRAS_ expression was also strongly associated with smoking status, regardless of the time that they had stopped smoking before the diagnosis or of whether they were active smokers. Significant differences in expression levels were found between normal  
Figure 3: _KRAS_ expression profile in lung adenocarcinoma. Box-whisker plots show the expression of _KRAS_ in normal and tumor samples (**a**), tumor subgroups based on individual cancer stages (**b**), patient sex (**c**), age (**d**), smoking status (**e**), nodal metastasis (**f**) (Data derived from the UALCAN server ([[http://ualcan.path.uab.edu/](http://ualcan.path.uab.edu/)]([http://ualcan.path.uab.edu/](http://ualcan.path.uab.edu/)) (accessed on 15 June 2022))).  
vs. non-smoker, normal vs. smoker, normal vs. reformed (according to their duration of abstinence and time of diagnosis of cancer \(>\)15 and \(\leq\)15 years). Differences were also found between non-smokers vs. the above-mentioned subgroups (\(p\) < 0.05). Regarding metastatic potential, _KRAS_ showed overexpression in N0 to N2 of the nodal metastasis status group in LUAD patients (\(p\) < 0.05). However, the association between the expression level of _KRAS_ and N3 lymph node metastatic groups was not statistically significant (\(p\) = 0.79). The statistical analyses are presented in Supplementary Table S1.
## 1 Introduction
### Remote Healthcare  
eHealth can be used for patient monitoring, remote diagnostics, activity monitoring, lifestyle suggestions and personal security to enable novel patient-physician interactions. In terms of challenges, new threats regarding ethical issues, such as online professional practice, informed agreement, privacy and equity, are posed by the remote aspect of the technology.  
Contemporary eHealth reality consists of a combination of sensors for individual's vital sign measurements or even tracking his position in case of an emergency. These sensors come in the form of lightweight portable (or wearable) devices for enhanced user acceptability. State-of-the-art remote monitoring is achieved by combining short-range communications (personal area network (PAN)) for the sensors and general packet radio service (GPRS) access on another device.  
Most eHealth applications occur in urban operational environments. Remote management includes cases in which health practitioner intervention is required. There is necessity for high reliability, due to the sensitive nature of data. The eHealth scenario considers event-triggered connections having benefits on network signaling and scheduling. eHealth applications may use mesh routing with multi-hop connectivity. High mobility is expected because of the tracking of moving objects/persons.
## 4 Conclusions  
The main functionalities of a software-defined BeiDou B1 receiver were presented, while highlighting the similarities and differences of BeiDou B1 signal with the existing GPS L1 C/A signal. A novel acquisition scheme for long coherent integration in the presence of NH code was presented and implemented for acquiring BeiDou IGSO and MEO satellites. Furthermore, a similar phase-preserved acquisition scheme was implemented for BeiDou GEO satellites acquisition. Real GNSS data was collected with a dual frequency front-end, which is then processed with the implemented software-defined GNSS receiver. The positioning results were presented for different GNSS  
Figure 11: Position fix with BeiDou Satellite Navigation System in Google Earth.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline  & \multicolumn{4}{c}{BeiDou} & \multicolumn{4}{c}{GPS} & \multicolumn{4}{c}{Multi-GNSS} \\ \cline{2-13}  & **East** & **North** & **Up** & **3D** & **East** & **North** & **Up** & **3D** & **East** & **North** & **Up** & **3D** \\ \hline RMS & 0.77 & 1.78 & 8.0 & 8.24 & 1.19 & 1.77 & 1.82 & 2.80 & 1.0 & 1.76 & 1.88 & 2.77 \\ \hline PDOP & \multicolumn{4}{c}{2.05} & \multicolumn{4}{c}{1.87} & \multicolumn{4}{c}{1.81} \\ \hline \end{tabular}
\end{table}
Table 3: Position error statistics with respect to true position.  
constellations for a static scenario with 60 s long dataset. It was shown that the BeiDou has comparable positioning performance with that of GPS, provided that both the systems have somewhat similar DOP values. A true multi-GNSS positioning results were also computed with GPS and BeiDou systems. The true benefits of multi-GNSS will be more noticeable in harsh GNSS environment with obstructed sky view. The future work includes investigating the performance of a multi-GNSS software-defined receiver in harsh multipath environments (_i.e._, in urban anyons), as well as developing a Receiver Autonomous Integrity Monitoring (RAIM) system considering the signal quality of individual satellites from different GNSS constellations.  
This research has been conducted within the Detection analysis, and risk management of satellite navigation jamming (DETERJAM) project funded by the Scientific Advisory Board for Defence of the Finnish Ministry of Defence and the Finnish Geodetic Institute, Finland and the FINCOMPASS project, funded by the Finnish Technology Agency TEKES with the Finnish Geodetic Institute, Microsoft Corporation, Roger-GPS Ltd., and Vaisala Ltd.  
[PERSON] developed the BeiDou part of the multi-frequency multi-system FGI-GSRx software-defined GNSS receiver including the novel acquisition schemes mentioned in the manuscript. He also wrote most part of the manuscript. The co-authors contributed on writing some part of the manuscript and helped during the phase of data collection and data analysis.
## 2 Materials and Methods
### Web-Based Platform
#### 2.2.3 Database  
Data storage plays a critical role within the monitoring system's web platform, encompassing sessions, user information, node configuration, and, most notably, the measurements themselves. To effectively meet this requirement, a deliberate approach was adopted, taking into account the limited number of sensor node types involved. Specifically, the system focuses on monitoring seven soil parameters, which allows for a fixed number of columns in the database. This deliberate design simplifies data management processes and ensures consistent storage practices.  
To leverage the advantages inherent in a relational database management system like MySQL, the platform was structured accordingly. The structured nature of the data eliminates the need for dynamic schema modifications, streamlining data management procedures. Additionally, the adoption of a relational database approach enhances the system's scalability, enabling the seamless integration of new sensor node types through the creation of additional tables.  
By choosing a relational database approach, the platform ensures the efficient storage and retrieval of measurements. It can effectively handle large volumes of data while maintaining data integrity. Moreover, this design choice provides flexibility for future expansions, facilitating the smooth integration of new sensor node types through the creation of dedicated tables.
## 4 Simulation Experiment and Result Analysis
### SDR/SIR Analysis  
This section analyzes the degree of distortion and interference of the estimated speech signal. Here, the quantitative evaluation is based on SDR and SIR, which verifies the goodness of the separation results of the Fast IVA algorithm. The performance index is shown in Figure 7, where the SDR and SIR data values are the average values obtained from 10 tests.  
Figure 7 represents the performance evaluation of the separation results by different metrics; that is, the SDR and SIR values between each separated signal and the source signal, where s1 and s2 represent the two source speech signals and Chen is the chaotic signal. As shown in Figure 7a, the dark blue bars indicate the SDR values obtained after comparing the separated signal \(y_{1}\) with s1, s2 and s3. The SDR values with s1 and s2 are negative, and the SDR value with Chen is 27.9537 dB, which determines that the separated signal \(y_{1}\) is chaotic. Furthermore, the SDR values of the estimated signals of source speech s1 and s2 are also both close to 20 dB, which shows that the separated signals are less affected by distortion and interference. Similarly, in Figure 6b, the SIR values with s1 and s2 are negative due to the higher energy and larger bandwidth of the chaotic signal, and the SIR with s3 is calculated to be 3.6834 dB. The SIR values of both s1 and s2 estimated  
Figure 7: The evaluation of algorithm separation performance with different metrics. (**a**) SDR value; (**b**) SIR value.  
signals are calculated to be around 15 dB, while the SIR value of the chaotic signal is almost 0, indicating that the estimated signal has few components separated from other sources, which in turn proves the superior performance of the separated signal.
## 4 Experimental Validation
### Fault Diagnosis Based on Data Feature Argumentation and CNN Model  
In order to further verify the fault diagnosis effect of the proposed algorithm, the time domain signal is first collected for analysis. In total, 400 groups of data under each condition were collected as the training sample set, and then cyclic spectrum analysis was performed on it. At the same time, each set of conditions collected 70 sets of data as a test data set, shown as Table 2. Subsequently, test data set is input into the model for analysis.  
During the analysis, the model parameters are first determined. The Gaussian kernel function is used to initialize the convolutional layer connection coefficients. During the weight update phase, the learning rate \(\eta\) and weight update coefficient \(\beta\) have a great influence on the identification results. Based on the experience accumulated in the previous research process, to prevent overfitting, set \(\eta\) and \(\beta\) to 0.001 and 0.95, respectively. In addition, the weight update times are studied; increase the number of update times of connection weights and calculate the recognition accuracy rate corresponding to each update number. Perform three times continuously and the results are shown in the histogram of the three colors in Figure 7. Among them, the red curve represents the average recognition accuracy. It can be seen from the figure, by increasing the number of iterations, the matching degree of the system and the stability of the results can be improved, but it will bring a computational disaster. To balance the calculation efficiency and classification results, the number of weight updates is set to 49. Using the algorithm proposed in this paper, the final classification accuracy is 97.86%.  
To further improve the visualization of classification results, the spatial representation feature analysis diagram obtained by t-SNE is shown in Figure 8. It displayed that the planetary bearing condition monitoring data after two-dimensional spatial feature extraction can enhance intra-class aggregation and class-to-class differentiation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Type Number** & **Fault Location** & **Training Sample Size** & **Testing Sample Size** \\ \hline
1 & Normal & 400 & 70 \\
2 & Inner-race Fault & 400 & 70 \\
3 & Outer-race Fault & 400 & 70 \\
4 & Ball Fault & 400 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test condition description.  
Figure 7: Weight iteration update times and classification accuracy graph; The blue, orange, and gray bars represent the results of executing the program three times at different iterations.
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 1 Introduction
Although there are some attempts to use DL in IDS, they still rely on legacy datasets (e.g., old network traffic) to train the models. As a result, these IDS cannot detect new and emerging attack patterns, such as ICMPv6 flooding DDoS attacks. Additionally, IDS for IPv4 networks cannot detect IPv6 attacks due to protocol structure differences [9]. Therefore, this research proposes a deep-learning-based approach to detect ICMPv6 flooding DDoS attacks accurately and ensure IPv6 network service continuity. This research paper has three contributions to the body of knowledge. First, we propose an ensemble method for selecting the most significant traffic features that contribute to detecting ICMPv6 DDoS flooding attacks. Second, we provide a comparative performance analysis of DL-based classifiers for IDS to identify the best DL model for detecting ICMPv6-based attacks. Third, we hybridize the best DL-based classifier with an ensemble feature selection based on the selected features for IDS. This work differs from other existing works, such as in [5; 10; 11; 12], by emphasizing the selection of features that highly contribute to detecting ICMPv6 flooding DDoS attacks by proposing an ensemble feature selection mechanism. The feature selection is further improved by proposing a feature intersection based on a predefined threshold. The selected features are then used to train a solid deep learning model to accurately detect ICMPv6 DDoS flooding attacks. The organization of the rest of this research paper is as follows: Section 2 provides an overview of IPv6, ICMPv6, ICMPv6 flooding DDoS attacks, and DL-based intrusion detection systems. Sections 3 and 4 discuss related works and the proposed approach, respectively. Section 5 outlines the experimental results and discussion. Finally, Section 6 concludes this paper and offers several potential future works.
## 3 Results
### Reading Experience Preference  
The findings regarding preferences for reading experiences are illustrated in Figure 4. Within the realm of electronic reading, the 150 lx illumination level within the 3000 K group was the only one to achieve a significantly higher preference score compared to 50 lx. In the 4000 K group, the 100 lx level was notably preferred over the 50 lx setting. For the 5000 K group, both 150 lx and 200 lx levels received significantly higher preference scores than the 50 lx level, indicating a clear favorability towards higher illuminance settings. Between the different CCT groups, there were no significant differences observed.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Subjective Evaluation** & **Source** & **SS** & **df** & **MS** & **F** & \(p\) \\ \hline Eye burning & illuminance & 0.883 & 3 & 0.294 & 0.304 & 0.823 \\ CCT & 1.244 & 2 & 0.622 & 0.642 & 0.528 \\ illuminance * CCT & 8.160 & 6 & 1.360 & 1.403 & 0.216 \\ \hline \hline \end{tabular}  
* Significance levels are set at \(p\) < 0.05
* and <0.01 **. Results with a significance level have been marked and italicized.  
\end{table}
Table 4: _Cont_.  
Figure 3: Overall preference results: (**a**) digital reading; (**b**) paper reading. Statistical significances \(p\) < 0.05 marked with *.  
Regarding preferences for the paper reading experience, both the 200 lx and 150 lx settings in the 3000 K group significantly outperformed the 50 lx level. In the 4000 K category, the 150 lx and 200 lx levels were both greatly preferred over the 50 lx setting, with the 150 lx also significantly surpassing the 100 lx level in terms of preference. Similarly, in the 5000 K group, the 150 lx and 200 lx levels were significantly more favored than the 50 lx setting. Notably, when comparing across groups, the preference score for the 4000 K setting was significantly higher than that of the 5000 K, suggesting a distinct overall preference for the 4000 K lighting condition in paper reading experiences.
# Solanesol Biosynthesis in Plants
## 4 Key Enzymes in Solanesol Biosynthesis
### Solanesyl Diphosphate Synthase (SPS)  
SPS catalyzes the synthesis of SPP from IPP, DMAPP, GPP, FFP, and GPPP, and SPP is a precursor in solanesol and plastoquinone synthesis (Figure 2) [3,6]. To date, _SPS_ genes have been identified in _A. thaliana_[21,22,52], _H. brasiliensis_[53], _Oryza sativa_[23], _S. lycopersium_[24], and _N. tabacum_[6], _A. thaliana_ carries two _SPS_ genes: _AtSPS1_ and _AtSPS2_, the expression of which is significantly higher in leaves and stems than in roots [22]. Silencing of _AtSPS1_ and _AtSPS2_ lowered the leaf plastoquinone content, thus inducing photoinhibition [52]. Fibrillin 5 (FBN5) binds with AtSPS1 and AtSPS2 to regulate the synthesis of plastoquinone [54]. In _O. sativa_, two _SPS_ genes have been identified: _OsSPSS1_ and _OsSPSS2_, which are expressed in the mitochondria and plastids, respectively; _OsSPSS1_ preferentially catalyzes the synthesis of ubiquinone-9 from FFP in mitochondria, while _OsSPSS2_ preferentially catalyzes the synthesis of plastoquinone-9 from GPP in plastids [23]. In tobacco, _N1 SPS1_ and _N1 SPS2_ are expressed in various organs, with the levels decreasing in the order leaf > stem > root, which is consistent with the distribution of solanesol and chlorophyll in these organs [55]. _N1 SPS1_ and _N1 SPS2_ are both located in chloroplasts, which is consistent with the subcellular localization of _S. lycopersium_ SPS [24]. _N1 SPS1_ and _N1 SPS2_ both contain two conserved DDxxD domains that participate in coordinating the binding between divalent metal ions and pyrophosphate groups, and thus serve an important role in the positioning of the reaction substrates [8]. Overexpression of tomato _SPS_ in tobacco significantly increased the plastoquinone content of immature leaves and the solanesol content of mature leaves [24], and co-expression of potato _SPS_ genes with _DXS_, _DXR_, _IPI_, and _GGPPS3_ significantly increased the solanesol content of _N. benthamiana_[1]. Hence, _SPS_ is a key enzymatic gene for the biosynthesis of solanesol, and its overexpression promotes the accumulation of downstream metabolites such as solanesol.
# Green Synthesis Method and Application of NaP
## Results and Discussion
### SEM Results Analysis  
The morphology and particle size of the raw material CGCS and the prepared NaP zeolite were investigated by using different magnification SEM (Figure 3). The SEM micrograph of the original CGCS after thorough ground and mixed is irregularly granular and has no pore structure on the surface (Figure 3a). The NaP zeolite prepared by the green synthesis method has the characteristic structure of a rectangular prism crystal plane, which is consistent with the crystal morphology reported in the literature (Figure 3b,d) [24; 25]. The morphology and average particle size of the samples were slightly different at different SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar ratios. At a lower magnification, a honeycomb crystal polymer can be observed (Figure 3c). When the magnification is increased, about 4-7 \(\upmu\)m \"cauliflower-like\" aggregates (Figure 3d) can be observed. These aggregates are composed of anisotropic growth of rectangular prismatic crystals with an average diameter of less than 1 \(\upmu\)m [26].  
Figure 3: SEM images of CGCS and different SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar ratio NaP zeolites: (**a**) CGCS, (**b**) SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) = 3.40, (**c**,**d**) SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) = 5.20.
# Tradeoffs When Building and Running Cohort and Patient-Level Markov Simulation Models
## 2 Materials and Methods
### Simulation  
In the CH model, the cohort transitioned from one cycle to the next based on transition probabilities, with the cohort distribution updated each cycle. In the PL model, individual patients followed randomly sampled transitions [14], with their health status updated each cycle until the end of the two-year period or until death. This process was repeated for 10,000 iterations (base case analysis), and individual results were aggregated to present average patient pathways in the cohort. Both models were illustrated using a 'Markov trace', which depicts the distribution of patients across health states in each cycle.  
It is important to clarify the difference between a PL model and a Monte Carlo model. In our PL model, the input parameters for transition probabilities, costs, and utilities are deterministic, while the variability comes from generating random numbers that determine patient pathways. For each patient in each cycle, random numbers dictate transitions between health states, mortality, treatment switches, and adverse events. For example, if a random number falls below the predetermined probability of an adverse event, the event occurs.  
To minimize the effect of first-order uncertainty, we simulated 10,000 patients, ensuring that random variations averaged out, leading to more stable outcomes. Unlike a traditional Monte Carlo simulation, we did not sample input parameters from probability distributions, as that would resemble probabilistic sensitivity analysis (PSA), which was not our aim here.  
The only exception to this approach was age, which was sampled from a normal distribution with a mean matching the CH model and a standard deviation of 5 years. Age was set within a range of 12 to 89 years, reflecting the plausible onset of the disease. Additionally, gender was determined using a random number based on the male-to-female ratio set in the model. Therefore, while our model employed random numbers to simulate patient pathways, it fundamentally differed from a typical Monte Carlo simulation, as our input parameters remained deterministic throughout the simulation.