esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Semi-Quantitative Analysis and Standard Curve
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Factor and Level Combination** \\ **(CTBN Content/ wt. \%)** \\ \end{tabular} } & \multirow{2}{*}{**MV**} & \multirow{2}{*}{
\begin{tabular}{c} **Difference** \\ **between MV** \\ \end{tabular} } & \multicolumn{2}{c}{**Confidence Interval**} & \multirow{2}{*}{**Significant or Not?**} \\ \cline{3-3} \cline{5-6}  & & & & **Lower Limit** & & **Upper Limit** \\ \hline
11 [FOOTNOTE:11]Footnote 11: Comparison between the virgin bitumen with 1 wt. % CTBN and those with 5 wt. % and 10 wt. % CTBN in terms of the mean value of I\({}_{\text{CTBN}}\); [22] comparison between the virgin bitumen with 5 wt. % CTBN and those with 1 wt. % and 10 wt. % CTBN in terms of the mean value of I\({}_{\text{CTBN}}\); [33] comparison between the virgin bitumen with 10 wt. % CTBN and those with 1 wt. % CTBN and those with 1 wt. % and 5 wt. % CTBN in terms of the mean value of I\({}_{\text{CTBN}}\). [33] comparison between the virgin bitumen with 10 wt. % CTBN and those with 1 wt. % and those with 1 wt. % and 5 wt. % CTBN in terms of the mean value of I\({}_{\text{CTBN}}\). [33]  
\end{table} 
Table 7: Multiple comparison results for the mean value of I\({}_{\text{CTBN}}\) for the virgin bitumen. The one-way ANOVA results in Table 9 display a \(p\)-value \(<0.0001\), which means that the effect of the CTBN content on \(\mathrm{I_{CTBN}}\) is statistically significant. Table 10 shows that all 95% confidence intervals of the \(\mathrm{I_{CTBN}}\) differences exclude zero, indicating that each pair of \(\mathrm{I_{CTBN}}\) values is significantly different and that the CTBN content significantly affects the \(\mathrm{I_{CTBN}}\) of an asphalt mixture. The correlation analysis and one-way ANOVA results indicate that the use of \(\mathrm{I_{CTBN}}\) as a quantitative analysis index to investigate the blending degree of virgin and RAP binders is reliable. According to [PERSON]'s law, \(\mathrm{I_{CTBN}}\) is directly proportional to the CTBN content; moreover, the abovementioned analysis indicated that the CTBN content has a significant effect on \(\mathrm{I_{CTBN}}\). Therefore, the CTBN contents and \(\mathrm{I_{CTBN}}\) values of the asphalt mixtures were fitted by linear regression to establish a standard curve, as shown in Figure 6. The infrared spectra of the RAM with certain proportions of CTBN were examined to calculate the \(\mathrm{I_{CTBN}}\) values at different locations of RAP in the RAM, after which the CTBN contents were obtained by fitting the standard curve quantitatively.
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
## 2 Overall System Design  
Figure 1 shows a schematic diagram of the specific scheme design of the test system. The test system consists of a front-end memory and a back-end test bench. The front-end memory has a built-in complete power supply system, ensuring that the device can independently perform test tasks at the front end. The DC/DC converter and power module provide the required voltage for each component of the control board and sensors. During system operation, the memory is located at the front-end test site and is independently  
responsible for data recording. The test bench is at a safe distance and remotely debugs, configures parameters, monitors the status, and sends instructions to the storage unit wirelessly. After the system starts, it begins to perform monitoring and data storage tasks, waiting to receive the trigger signal to determine the time reference. The physical quantity signals captured by the pressure sensor are converted into electrical signals. After being processed by the conditioning circuit, they are collected by the analog-to-digital converter module and converted into digital quantities. These digital signals are sent back to the back-end test bench through the established wireless communication link and simultaneously stored in the flash storage module. Under the control of the FPGA on the test bench, data are exchanged with the upper computer via Gigabit Ethernet [20]. After receiving the data, the upper computer uses the corresponding supporting software to process, configure, display, and store the data. The overall system architecture is shown in Figure 2.  
Figure 1: Schematic diagram of the system solution.  
Figure 2: System overall architecture.
# Design of True Time Delay Millimeter Wave Beamformers for 5G Multibeam Phased Arrays
## 5 Conclusions  
Millimeter wave communications have become one of the most promising candidates for the future wireless networks. In this paper, we address the needs of 5G and early 6G wireless communication systems and we explain why hybrid beamforming networks have to be used to address the challenges of these networks and the corresponding channel environments. Specifically, we mention that millimeter wave beamformers are appropriate for switched beam phased arrays as hybrid beamforming stages. Three different true-time-delay analog beamforming networks are developed: a multi-layer Blass matrix, a Rotman lens, and a multi-layer tree topology. For each beamforming network, we develop separately its design methodology as well as analyze its electromagnetic performance, thereby showing their suitability for future communication systems.  
**Author Contributions:** All authors equally contributed to the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by the Air Force Office of Scientific Research under grants FA9550-18-1-0191 and FA9550-19-1-0290.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Case Study and Result
### Performance Evaluation  
In order to evaluate the performance of our system, we record the time spent on various components in the process of data preparation and analysis (Figure 11). We process data and record the time on a server with 12 core/2.88 GHz CPU and 96 GB memory. The computations include 1-year annual mean, 5-year annual mean, global mean of 5-year annual mean, ensemble mean of 5-year annual mean, ensemble standard deviation of 5-year annual mean, scatter plot for model input configuration and output variables, scatter plot for Albedo analysis (_i.e._, scatter plot between thermal outgoing radiation and solar absorbed radiation), and Taylor diagram. Time is recorded respectively for data both from 1 model run and 300 model runs. As expected, the most time is spent on calculating 1-year annual mean and 5-year annual mean. The time used for calculating five 1-year annual mean values from 1956 to 1960 for data in 300 model runs can reach 831 seconds. By contrast, the time spent on the other analyses is only several seconds which is tolerable.  
Therefore, storing initial statistics like 1-year annual mean is necessary. It is impossible for users to calculate 1-year annual mean repeatedly in every analysis. But whether 5-year annual mean and its global mean should be pre-stored in the data repository depends on the frequency of data access from users.  
Figure 11: Time for performing different statistical analyses.
## 2 Results
### Distribution of Type II TA Systems Among Pathogenic E. coli  
Since there was an impressive presence of TA systems in the hybrid strain BA1250, we investigated whether this genetic background would be present in other hybrid _E. coli_ strains, as well as in EPEC and ExPEC. Therefore, we selected 53 genomes of EPEC, ExPEC, and hybrid EPEC/ExPEC strains (Figure S1 and Table S1).  
The in silico analysis revealed the presence rates of the 21 previously identified TA systems on the BA1250 genome (Table 1), among the studied type II TA systems, i.e., CcdB-CcdA, YhaV-PrIF, MazE-MazF, YoeB-YefM, and PasT-Pasl seem to be associated  
Figure 3: Relative expression of toxin-antitoxins in the stationary growth phase in duplicates of three independent experiments. _E. coli_ BA1250 toxin–antitoxin gene pairs (_ccdB/ccdA_, _yhaV/prIF_, _mazE/mazF_, _yoeB/yefM_, and _pasT/pasl_) were evaluated in the stationary growth phase under nutritional scarcity, oxidative stress, acid shock, osmotic stress, and stress-free LB medium condition. Genes were considered up/downregulated when relative average expression was \(-1>\) LogZFc \(>1\) in comparison to the LB group. Statistical significance was considered when the \(p\) value \(<0.05\) in a 2-way ANOVA test comparing toxin and antitoxin at the same condition. (*) Represent statistical significance with \(p<0.05\); (***) represent statistical significance with \(p<0.0001\).  
with hybrid strains, as 69.2% of them exhibited all these five TA systems, while 30.8% showed four out of the five (Figure S2).  
To characterize the 53 genomes selected, a phylogenetic tree was constructed comparing them to the BA1250 regarding phylogroups (Figure S3). The hybrid strain BA1250 belongs to phylogroup B2, as well as 23 out of the 26 hybrid strains studied, and the remaining three were classified in phylogroup B1. Of the 53 analyzed strains, the majority were distributed between phylogroups B2 (30 strains) and B1 (19 strains), with only 1 strain in phylogroup F and another 3 in phylogroup A (Figure S3).  
The majority of the genes of the analyzed aEPEC, tEPEC, ExPEC, and hybrid ExPEC/EPEC strains were predicted to be located in the chromosome (Figure 4A,B), varying from 53 to 100% presence rate of genes from each type of TA system, with only a small percentage of genes from TA systems type I and IV predicted to be plasmid located in all pathotypes (Figure 4C). However, the predicted presence of type I, II, IV, and V systems in the chromosome was more frequent in the hybrid strains than in the aEPEC strains, indicating a possible gain of function for the first group.
# Vibration Response Law of Aircraft Taxing under Random Roughness Excitation
## 2 Random Vibration Models
### Statistical Linearization of the Nonlinear System
#### 2.2.1 Nonlinear Function Expression  
(1) Nonlinearity of landing gear elastic element  
In general, the landing gear spring force increases at an increasing rate as the moving displacement rises, as shown in Figure 2.  
Multiple regression analysis is used to examine different fitting models. Considering the fitting effect and the complexity of the formula, a cubic polynomial can be applied to describe the nonlinear characteristics of Figure 2. The fitted formula is shown in Equation (8).  
\[F_{\text{s}}=k_{1}x+k_{2}x^{2}+k_{3}x^{3} \tag{8}\]  
where \(F_{\text{s}}\) is the spring force; \(x\) is the moving displacement; and \(k_{1}\), \(k_{2}\), and \(k_{3}\) are fitting parameters.  
(2) Nonlinearity of landing gear damping element  
Generally speaking, the damping elements of the landing gear have nonlinear characteristics, which can be expressed by piecewise functions at different velocities, as shown in Equation (9). The relationship curve between the damping force and the moving speed is shown in Figure 3.  
\[C(x)=\begin{cases}C_{3}u|u|,&u<0\\ C_{4}u|u|,&u\geq 0\end{cases} \tag{9}\]  
where \(C(x)\) is the damping force; \(u\) is the moving speed; and \(C_{3}\) and \(C_{4}\) are fitting parameters.  
Figure 2: Variation curve of landing gear spring force with moving displacement.
## 2 Materials and Methods
### Data Processing
The absence of rainfall is indicative of a constant increase over time, although the periods with the highest dust presence in the photovoltaic panels usually correspond to the spring months, when losses of around 15% are reached. Therefore, the number of days without rain, together with the amount of rainfall, is a very decisive factor in interpreting the cleanliness factor achieved. The testing facility is located at sea level, so, no influence of industry or contaminant processes can be considered to contribute to the presence of dust on the PV panels. Another variable was calculated: PM\({}_{10-2.5}\), consisting of the concentration of particulate matter for particles whose diameter is greater than 2.5 \(\upmu\)m and smaller than 10 \(\upmu\)m. Equation (2) was used to calculate this. This calculation was performed to avoid overlap, since \(PM_{10}\) considers all forms of particulate matter whose diameter is less than 10 \(\upmu\)m, which includes \(PM_{2.5}\) (a diameter of less than 2.5 \(\upmu\)m). \[PM_{10-2.5}=PM_{10}-PM_{2.5} \tag{2}\]  
The data were transformed from minute-long intervals to daily intervals, resulting in a total of 322 days for the variables shown in Table 3 which are the ones used to train the models. \begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Unit** \\ \hline Soiling Loss (SL) & \% \\ Ambient temperature & \({}^{\circ}\)C \\ PM\({}_{2.5}\) & \(\upmu\)g/m\({}^{3}\) \\ PM\({}_{10-2.5}\) & \(\upmu\)g/m\({}^{3}\) \\ Relative Humidity (RH) & \% \\ Wind Speed (Wspeed) & km/h \\ Precipitation & mm/days \\ Days since last rainfall (DSLR) & days \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Variables used to develop the models. Figure 2: DSLR for different cleaning thresholds (CTs).
## Appendix A Experimental Reproducibility Details
### Sample Human Evaluation Examples  
**Example 1--High Quality Translation:**  
* **Tarifit Source:** Azul, mlih cha?
* **GPT-4 Translation:** Hello, how are you?
* **Reference Translation:** Hello, how are you?
* **Evaluator Scores:** Adequacy: 5/5, Fluency: 5/5
* **Comments:** Perfect translation preserving greeting convention  
**Example 2--Good Translation with Minor Issues:**  
* **Tarifit Source:** Tamghart ni tsawar tamazight
* **GPT-4 Translation:** That woman speaks Amazigh
* **Reference Translation:** That woman speaks Berber
* **Evaluator Scores:** Adequacy: 4/5, Fluency: 5/5
* **Comments:** Accurate but uses \"Amazigh\" instead of more common \"Berber\"  
**Example 3--Translation with Morphological Error:**  
* **Tarifit Source:** Netta wa ditis cha
* **GPT-4 Translation:** He will not coming
* **Reference Translation:** He will not come
* **Evaluator Scores:** Adequacy: 3/5, Fluency: 2/5
* **Comments:** Meaning preserved but grammatical error in English  
**Example 4--Cultural Context Challenge:**  
* **Tarifit Source:** Chha sta3 at?
* **GPT-4 Translation:** How many hours?
* **Reference Translation:** What time is it?
* **Evaluator Scores:** Adequacy: 2/5, Fluency: 4/5
* **Comments:** Literal translation misses idiomatic time-asking expression
# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 2 Related Work
### Edge Detection Using Deep Learning  
[PERSON] et al. proposed an edge detection algorithm for the detection of cancer images using deep learning [37]. The reconstruction accuracy of edge detection algorithm is improved by combining the edge detection algorithm with the deep learning algorithm.  
First, the dataset of cancer images are selected to train the neural network model. Based on the neural network model, the reconstruction of a three-dimensional cancer image is constructed, and the features of cancer cells are extracted from the image by using the edge contour feature extraction method, and cancer image edge detection results are obtained. Finally, segmentation method is used for information recombination. Their results show 95% reconstruction ability with high accuracy to detect the edges of the cancer images.  
[PERSON] et al. proposed a tumor edge detection approach in mammography images using quantum and machine learning approaches [38]. The approach includes quantum genetic algorithm and a support vector machine. The quantum genetic algorithm is used to resolve the thresholding problem based on Tsallis entropy, whereas support vector machines are trained to detect the edges of mammographic images. The proposed approach is compared with some standard edge detection methods on mammographic images. The effectiveness of proposed approach is measured using PSNR, SSIM and FSIM metrics.  
[PERSON] proposed a deep learning-based approach to resolve the problem of edge detection in image processing [39]. First, a dataset of natural images are used as input. The input image undergoes a pre-processing step of noise removal. Afterwards, a convolutional neural network (CNN) scans the whole image and makes predictions for edges directly from the image patches. At the end, morphological operations are applied as post-processing step to thin the output edge map. The approach is simple and does not need any feature extraction method.  
The above approaches are relevant but they have not considered performance of edge detection in brain MRI images and their types, T1, T2, and flair. Furthermore, in machine learning algorithms, the accuracy of edge detection depends not only on the algorithm but also on the dataset used for training. Therefore, we have not selected these approaches for evaluation of metamorphic testing.
## 3 Methodology
### Dataset and Data Preprocessing
#### 3.1.2 Data Preprocessing
In our research, we employ the recursive feature elimination (RFE) technique along with the random forest classifier algorithm for feature selection. RFE is a widely adopted method that systematically ranks and eliminates features based on their importance. The process involves the following key steps:  
* Initialization: we start by creating a random forest classifier model, which serves as the base estimator for the RFE algorithm. * Recursive Elimination: The RFE algorithm recursively eliminates the least important features by retraining the model at each iteration. Features are ranked based on their contribution to the classification task, and the least significant feature is removed. * Convergence: The recursive elimination process continues until the desired number of features is reached. In our case, we select the top 20 features with the highest importance scores. By applying the RFE technique, we achieve the following advantages* Enhanced Model Efficiency: with a reduced feature set, the IDS can process data more efficiently and expedite the intrusion detection process. * Improved Model Interpretability: the selected features provide a concise representation of relevant information, facilitating easier interpretation of the IDS's decision-making process. * Optimized Performance: by focusing on the most informative attributes, the IDS can achieve higher accuracy in distinguishing between normal network traffic and potential cyber threats. The 20 selected features, derived through the RFE-random forest classifier approach, form a powerful subset that captures the essential characteristics of network traffic data. As a result, our AI-based IDS is equipped to perform with exceptional accuracy and effectiveness in identifying and mitigating various cyber threats commonly encountered in IoT networks.
## 2 Materials and Methods  
This cross-sectional study was conducted to evaluate the cross-sectional root canal shape among different age groups in the period between November 2018 and March 2020.  
Sample size was determined according to other publishing studies, namely those of [PERSON] et al. [21; 22], and taking into account the three age groups, three hundred CBCT scans for 300 subjects, aged (20-70) years, were included in this study. The study was conducted in accordance with the Helsinki Declaration of 1975, as revised in 2000, and was approved by the ethical committee of the Peoples' Friendship University of Russia University (Protocol 6 at 21.02.2019). The study was conducted in university clinical clinics and radiologic diagnostic centers for three-dimensional radiological scanning in Moscow. Subjects attending the university clinics for endodontic therapeutic purposes were asked to participate in this study and to do a CBCT scan. After collecting the sample, subjects were divided into three age groups by 100 subjects for each: young age (20-44) years, middle age (45-59) years, and elderly (60-70). Written consent was obtained from the subjects participating in the study.  
The CBCT scans were taken using a 3D eXam (KaVo, Biberach, Germany) with standard exposure settings (23 cm \(\times\) 17 cm field of view, 0.3 mm voxel size, 110 kv, 1.6-20 s) and were viewed by three examiners in a semi-dark room using I-CAT viewer software (version 10, Hatfield, UK).  
The morphology of the cross-sectional root canal shape was studied on healthy teeth in axial slices in three thirds of the root canal, coronal at 2 mm level down the root canal orifice, middle of the root canal, and apical at 2 mm level of the apex in each age group.  
The root canal shape was recorded according to this classification: round, oval, long oval, and ribbon.  
The criteria for root canal shape classification were as follows: the round canal was defined when the mesio-distal diameter of the root canal was equal to the bucco-lingual/palatal diameter. The oval and long oval canals were determined when the bucco-lingual/palatal diameter was two to four time greater than the mesio-distal diameter of the root canal. The ribbon canal shape (that contain isthmus) was studied according to [PERSON]'s Classification [10] as follow:  
* Type I: Incomplete isthmus between two canals.
* Type II: A complete isthmus between two canals.
* Type III: A short, complete isthmus between two canals.  
* Type IV: A complete or incomplete isthmus between three or more canals.
* Type V: two or three canals without obvious connection between them.  
The changes in root canal shape recorded in coronal, middle and apical thirds of the root canal system.  
Statistical analysis: IBM SPSS Statistics v 22.0 licensed package (IBM, Chicago, IL, USA) was used for statistical processing of the study data. The method of descriptive statistics used for statistical processing of the received data. To compare the new classification of root canal shape among age groups, the Kruskal-Wallis test and Mann-Whitney U test were used. Statistical significance was set at 0.05.
# Practical Real-Time Phase Drift Compensation Scheme for Quantum Communication Systems
###### Abstract  
Quantum communication systems are susceptible to various perturbations and drifts arising from the operational environment, with phase drift being a crucial challenge. In this paper, we propose an efficient real-time phase drift compensation scheme in which only existing data from the quantum communication process is used to establish a stable closed-loop control subsystem for phase tracking. This scheme ensures the continuous operation of transmission by tracking and compensating for phase drift in the phase-encoding quantum communication system. The experimental results demonstrate the effectiveness and feasibility of the proposed scheme with an average quantum bit error rate of 1.60% and a standard deviation of 0.0583% for 16 h of continuous operation.  
quantum communication; phase drift; real-time phase compensation +
Footnote †: journal: _entropy_
## 5 Comparison of Calculation and Reality
### Results  
The determined natural frequencies, their corresponding eigenmodes and the associated damping ratios show substantial agreement with the results derived from the in situ test, as shown in Table 3. In particular, the discrepancies highlighted in Section 3 have been significantly reduced.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(f_{1}\) [Hz]/\(\zeta\) [\%] & \(f_{2}\) [Hz] & \(f_{3}\) [Hz] & \(f_{4}\) [Hz] \\ \hline Calculation & 9.9/6.9 & 13.8 & 25 & 28.3 \\ Model & 10.1/6.4 & 14.7 & 25 & 28 \\ \(\text{(ambient)}\) & 2/7 & 6 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the numerical and in situ measurement results regarding the dynamic characteristics for bridge 12,842.  
An examination of the deviations in Table 3 shows that the first torsional mode has a more pronounced measured deviation than the other eigenmodes. Studying the eigenmodes in Figure 13, it can be seen that the second mode has more curvature proportions in the abutment regions, particularly in the transverse direction, than the third mode. Consequently, the in situ stiffnesses of the transverse contribution exceed those obtained numerically.