esa-sceva/satcom-chunk-collection huggingface dataset:
# Guided Semi-Supervised Non-Negative Matrix Factorization
## 4 Experiments
### Topic Modeling on the CIP Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Guided NMR Results (\(\lambda=0.4\))**} \\ \hline
**Topic 1** & **Topic 2** & **Topic 3** & **Topic 4** & **Topic 5** & **Topic 6** & **Topic 7** \\ \hline gang & burglari & murder & accomplic & gang & instruct & identif \\ member & sexual & shot & corrobot & member & murder & eyewit \\ crip & strike & hous & robberi & expert & manslaught & photo \\ activ & admiss & detect & instruct & beer & lesser & lineup \\ phone & instruct & vehicl & murder & estrada & theori & suggest \\ murder & threat & phone & codefend & hispan & degre & suspect \\ photo & object & apart & abet & tattoo & passion & photograph \\ territori & discret & robberi & commiss & intent & abet & pack \\ associ & impos & want & special & men & voluntari & procedur \\ shot & sex & firearm & conspiraci & robberi & premedit & expert \\ \hline \multicolumn{6}{c}{Coherence Score \(\mathcal{C}\) per Topic:} \\ \hline
1112.94 & 1388.307 & 1290.817 & 921.023 & 1109.453 & 1123.185 & 1090.895 \\ \hline \multicolumn{6}{c}{Averaged Coherence Score \(\mathcal{C}_{\text{avg}}\): 1148.089} \\ \hline  & \multicolumn{6}{c}{GSSNMF Results (\(\lambda=0.3,\mu=0.006\))} \\ \hline Topic 1 & Topic 2 & Topic 3 & Topic 4 & Topic 5 & Topic 6 & Topic 7 \\ \hline murder & instruct & detect & identif & gang & gang & burglari \\ accomplic & manslaught & phone & eyewit & member & member & strike \\ corrobot & lesser & probat & photo & crip & expert & sexual \\ vehicl & murder & waiver & lineup & associ & beer & robberi \\ robberi & self & plea & suggest & expert & hispan & impos \\ shot & passion & interrog & suspect & activ & shot & discret \\ abet & theori & confess & photograph & intent & estrada & punish \\ intent & voluntari & interview & pack & premedit & men & sex \\ degre & heat & admiss & procedur & prove & tattoo & feloni \\ hous & spont & transcript & reliabl & firearm & male & threat \\ \hline \multicolumn{6}{c}{Coherence Score \(\mathcal{C}\) per Topic:} \\ \hline
1215.826 & 1024.617 & 1188.975 & 1184.333 & 1180.321 & 1084.33 & 1281.146 \\ \hline \multicolumn{6}{c}{Averaged Coherence Score \(\mathcal{C}_{\text{avg}}\): 1165.65} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Topic modeling results of guided NMR and GSSNMF for Rank 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Rank** & **6** & **7** & **8** & **9** \\ \hline \multirow{3}{*}{\(\mathcal{C}_{\text{avg}}\)} & Classical NMF & 1247.219 & 1181.154 & 1133.603 & 1070.618 \\ \cline{2-6}  & Guided NMF & 1227.161 & 1167.535 & 1112.29 & 1058.728 \\ \cline{2-6}  & GSSNMF & 1238.868 & 1181.253 & 1118.944 & 1065.837 \\ \hline \multirow{3}{*}{Best GSSNMF Parameters} & \(\mu=0.016\) & \(\mu=0.006\) & \(\mu=0.014\) & \(\mu=0.01\) \\  & \(\lambda=0.2\) & \(\lambda=0.3\) & \(\lambda=0.15\) & \(\lambda=0.2\) \\ \hline \end{tabular}
\end{table} 
Table 2: Coherence of topics generated by Classical NMF, Guided NMF, and GSSNMF for CIP data.
## 2 Materials and Methods
### Statistical Analysis  
The analyses were conducted using descriptive statistics and visual analysis techniques. The normality assumption of the data was assessed using the Shapiro-Wilk test, while potential outliers affecting the dataset were evaluated through a sensitivity analysis. The impact of the three experimental conditions on RAST metrics was examined using one-way ANOVA, and the homogeneity of variances was assessed with the Levene test. The effect of experimental conditions on RAST performance was analyzed using a repeated measures ANOVA, while a two-way repeated measures ANOVA was employed to evaluate the interaction between time and condition. The assumption of sphericity was tested using Mauchly's test, and the Greenhouse-Geisser correction was applied in cases of sphericity violation. If the F ratio was significant, _post hoc_ pairwise comparisons were conducted with Bonferroni correction. Partial eta squared (\(\eta^{2}\)) and omega squared (\(\omega^{2}\)) values were used to interpret the effect sizes. Eta squared and omega squared were interpreted according to the following benchmarks: 0.01 = small effect, 0.06 = medium effect, and 0.14 = large effect [34]. The participants' individual RAST responses to the experimental conditions were evaluated using Mann-Kendall trend analysis, and Monte Carlo simulation was applied with 1000 resamplings to increase the statistical power of the findings. All statistical analyses were conducted at a significance level of \(\alpha\) = 0.05. The analyses were performed using R software (version 4.1.0, R Core Team, Vienna, Australia) utilizing the {gggstatsplot}, {rstatix}, {ggpubr}, {patchwork}, and {tidyverse} packages. The details of the R code lines and dataset were presented via OSF ([[https://osf.io/r4](https://osf.io/r4) hcg/]([https://osf.io/r4](https://osf.io/r4) hcg/), accessed on 10 September 2024).
###### Abstract  
Plants of the _Allium_ genus produce sulphur compounds that give them a characteristic (alliaaceous) flavour and mediate for their medicinal use. In this study, the chemical composition and antimicrobial properties of _Allium cepa_ red cv. and _A. sativum_ in the context of three different drying processes were assessed using metabolomics. Bulbs were dried using either microwave, air drying, or freeze drying and further subjected to chemical analysis of their composition of volatile and non-volatile metabolites. Volatiles were collected using solid phase micro-extraction (SPME) coupled to gas chromatography-mass spectrometry (GC/MS) with 42 identified volatiles including 30 sulphur compounds, four nitriles, three aromatics, and three esters. Profiling of the polar non-volatile metabolites via ultra-performance liquid chromatography coupled to high resolution MS (UPLC/MS) annotated 51 metabolites including dipeptides, flavonoids, phenolic acids, and fatty acids. Major peaks in GC/MS or UPLC/MS contributing to the discrimination between _A. sativum_ and _A. cepa_ red cv. were assigned to sulphur compounds and flavonoids. Whereas sulphur conjugates amounted to the major forms in _A. sativum_, flavonoids predominated in the chemical composition of _A. cepa_ red cv. With regard to drying impact on _Allium_ metabolites, notable and clear separations among specimens were revealed using principal component analysis (PCA). The PCA scores plot of the UPLC/MS dataset showed closer metabolite composition of microwave dried specimens to freeze dried ones, and distant from air dried bulbs, observed in both _A. cepa_ and _A. sativum_. Compared to GC/MS, the UPLC/MS derived PCA model was more consistent and better in assessing the impact of drying on _Allium_ metabolism. A phthalate derivative was found exclusively in a commercial garlic preparation via GC/MS, of yet unknown origin. The freeze dried samples of both _Allium_ species exhibited stronger antimicrobial activities compared to dried specimens with _A. sativum_ being in general more active than _A. cepa_ red cv.  
**Keywords:** garlic; onion; metabolomics; drying; organosulphur; flavonoidsoids; anti-microbial
## Results
### Effects of Shuidouchi on Expression of c-Kit and SCF mRNA in Colon Tissues of Mice with Colitis  
The results in Figure 13 show that the expression of c-Kit mRNA in the colon of the normal group (6.38 times of that of the control mice) was significantly higher than that in the other groups. The expression of c-Kit mRNA in the colon of the sulfasalazine group (5.52 times of that of the control mice) and SDC-H group mice (4.8 times of that of the control mice) was also higher than that of the SDC-L group (2.71 times of that of the control group). Simultaneously, the expression levels of SCF mRNA in the colon tissues of the normal group, sulfasalazine group, SDC-H group, and SDC-L group were 5.33, 4.89, 4.36, and 2.21 of that of the control group, respectively.  
Effects of Shuidouchi on Expression Levels of IL-8 and CXCR2 Mrna in Colon Tissues of Mice with Colitis  
The IL-8 mRNA expression in the colonic tissue of mice of the normal group was 0.14 times that of the control group, which was weaker than that of the sulfasalazine group (0.22 times of that of the control group), SDC-H group (0.38 times of that of the control group), and SDC-L group (0.69 times of that of the control group; Figure 14). The control group exhibited the highest CXCR2 mRNA expression. The CXCR2 expression levels in the normal group, sulfasalazine group, SDC-H group, and SDC-L group were 0.19, 0.35, 0.44, and 0.65 times higher than that of the control group.  
Figure 14: mRNA expression levels of interleukin-8 (IL-8) and C-X-C chemokine receptor type 2 (CXCR2) in the colon tissue of mice. Values represent the mean \(\pm\) standard deviation. *-e Mean values with different letters in the same bars indicate significant difference (\(p<0.05\)). SDC-L: 2 g/kg Shuidouchi extract treatment; SDC-H: 4 g/kg Shuidouchi extract treatment; Sulfasalazine: 20 mg/kg sulfasalazine treatment.  
Figure 13: mRNA expression levels of c-Kit and SCF in colon tissues of mice. Values represent the mean \(\pm\) standard deviation. *-e Mean values with different letters in the same bars indicate significant difference (\(p<0.05\)). SDC-L: 2 g/kg Shuidouchi extract treatment; SDC-H: 4 g/kg Shuidouchi extract treatment; Sulfasalazine: 20 mg/kg sulfasalazine treatment.  
Effects of Shuidouchi on Expression Levels of IL-6, IL-12, and TNF-A Protein in Colonic Tissues of Mice with Colitis  
The IL-6, IL-12, and TNF-\(\alpha\) protein expression levels in the colonic tissue of mice of the normal group were weakest, but their expressions in mice of the control group were the strongest (Figure 15). Following Shuidouchi treatment, the IL-6, IL-12, and TNF-\(\alpha\) protein expression were decreased when compared to the control group, and the SDC-H showed a better reducing effect than SDC-L; the effects of SDC-H were close to that of sulfasalazine.
## 5 Ground-Based Calibrations  
The purpose of the ground-based calibration of the HEPD was to verify and accurately present the detection indexes, including the energy range accuracy, flux accuracy, measuring range, and energy linearity. In this paper, the calibration results of the main indexes such as the energy range and flux accuracy are briefly analyzed and introduced.
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
## 1 Introduction
The dispersive nature of Lamb waves causes signal waveform distortion and time delay and induces slight variations in subcarrier frequencies during propagation. Moreover, the power supply conditions for Lamb wave sensor nodes are usually stringent, and continuous communication can accelerate the energy consumption of the communication system [24; 25]. Although existing Lamb wave communication methods have alleviated these issues to some extent, limitations such as low transmission rates and high energy consumption remain. This paper proposes a Lamb wave-based encoding method using FDM-PPM to address these issues. By assigning carrier frequencies to sensor nodes and subsequently processing the Lamb wave acoustic data with FDM and PPM, this method achieves the efficient transmission of Lamb wave acoustic data in plate structures while significantly reducing the energy consumption of the communication system. The remainder of this paper is organized as follows. Section 2 introduces the proposed Lamb wave-based FDM-PPM scheme and its procedure. Section 3 experimentally validates the FDM-PPM scheme's bit error rate (BER) and communication rate. Section 4 discusses the characteristics of the proposed method and compares it with other traditional methods.
# Implementation of a Fast Link Rate Adaptation Algorithm for WLAN Systems
## 3 The Link Adaptation Firmware
### Function snr2 thr  
The inputs that are fed to the snr2 thr function are the mcsnum integer data type (the MCS index loop variable for which the throughput is to be computed), the snr_subcarr pointer to float data type (the SNR per subcarrier), and the snr_offset pointer to float data type (the SNR offset value for each MCS). The snr2 thr function receives the MCS index loop variable and the array of subcarrier SNRs and calculates the MMI by summing up the MI of subcarriers as to the current MCS loop variable. More specifically, through the conditional statement the MCS values are distinguished according to the modulation schemes. Then, each MI computation function corresponding to the distinguished modulation scheme is decided as follows. As displayed in Figure 8, in case of BPSK (MCS 0), the snr2 mi_bpsk function is selected, and in case of QPSK (MCS 1 and 2), the snr2 mi_qpsk function is selected. For 16-QAM (MCS 3 and 4), the snr2 mi_16 qm function is picked up and for 64-QAM (MCS 5-7), the snr2 mi_64 qm function is chosen. The procedure of computing the MI value from the corresponding function is reiterated over all the subcarriers and the MI values are summed up. The total sum of the MI values are multiplied by the inverse of the number of subcarriers, yielding the MMI, which is input to the mmi2 thr function to obtain the expected value of the throughput. This throughput value is the output of the snr2 thr function.
## 5 System Servo Performance Evaluation and Verification
### Experimental Verification
#### 5.2.1 The Influence of Friction Torque on Servo Performance  
When simulating friction torque, the electric load simulator and servo device are considered as a whole. When the direction of the load torque is the same as the system speed, it is considered to reduce the friction torque of the system. When the two directions are opposite, it is considered to increase the friction torque of the system.  
Figure 6: The influence of a single factor on (**a**) equivalent stabilization accuracy, (**b**) speed stability, (**c**) comprehensive servo performance.  
The sine following response test results of the system under different friction torques are shown in Figure 7. From the graph, it can be seen that the variation of friction torque has a significant impact on the speed dead zone. When the friction torque decreases to 50% of the reference value, the phenomenon of speed dead zone is basically eliminated. When the friction torque increases to twice the reference value, the system speed dead zone also increases by two times. Due to the presence of surplus torque, when the friction torque of the system is reduced or increased, the fluctuation of the system speed intensifies and the speed stability decreases.  
Consistent with the simulation analysis, the friction torque is simulated in seven equal parts in the range of 50-200% of the reference value. The relationship between the friction torque and equivalent stabilization accuracy is shown in Figure 8.  
The friction torque has a significant effect on the servo performance of the system, and for every 25% reduction of the friction torque, the equivalent stabilization accuracy is improved by 17%. The significant influence of friction torque on the servo performance of the system is verified by experiments and simulations. Reducing the friction torque of  
Figure 8: The relationship between the friction torque and equivalent stabilization accuracy.  
Figure 7: The influence of friction torque on the sinusoidal following response.  
the transmission components has great potential for improving the servo performance of the system.
# Activity Recommendation Model Using Rank Correlation for Chronic Stress Management
## 4 Result and Evaluation
### Performance Evaluation  
The data for performance evaluation uses data provided by the National Health and Nutrition Survey [25]. The data of the National Health and Nutrition Survey produces statistical results for identifying health and nutritional levels, which allow people to check their health status and to manage health and prevent diseases. As the data for evaluating the performance of the proposed model, used is the pre-processed data of the National Health and Nutrition Survey. The data of 1965 persons and the data of 4582 persons are used as a test set and a training set, respectively. The model learns with  
Figure 6: Activity recommendation system for stress management.  
the use of the training set, and its performance is repeatedly evaluated 20 persons with the use of the test set [33].  
Extraneous factors of the data are removed, and modeling for statistical analysis, visualization, and causality inference are conducted to use the data in the activity recommendation model. After the proposed activity recommendation model predicts the data of the training set, its precision and recall are compared based on its recommendation results. Formula (3) presents the F-measure to compare precision and recall [34; 35; 36].  
\[F-measure\ =\ \frac{2\times Precision\ \times Recall}{Precision+Recall} \tag{3}\]  
For the performance evaluation, the proposed Spearman Stress Management Recommendation Model (SSMRM), Pearson Recommendation Model (PRM), and Cosine Similarity Recommendation Model (CSRM) were applied to recommend the activities to reduce PSS and then their effects were compared. Table 3 shows the performance evaluation results of the activity recommendation model for the chronic stress management using SSMRM, PRM, and CSRM. It repeats the recommendation five times for 20 users and evaluates the F-measure as an average of recall and precision.  
The table shows that as the number of recommendations to users increases, the recall, and precision increase. Besides, the results of the performance evaluation showed that the performance of the proposed SSMRM model is better than that of PRM and CSRM with the average accuracy of 0.846, recall of 0.891 and F-measure of 0.868. SSMRM uses rank rather than directly using observations through the Spearman correlation analysis and improved the accuracy by solving the missing value problem by substituting the default rank value if missing or inaccurate. As a result, more accurate relationships than PRM using [PERSON]'s correlation analysis and CSRM using cosine similarity were found, enabling the recommendation of appropriate stress management activities to users.
## 5 Simulation and Evaluation
### Evaluation Metrics  
We evaluated the performance of FQ-AGO by measuring different performance metrics:  
* **Throughput** defines as the rate of successfully delivered data packets per second over the communication channel from source to destination.
* **Packet delivery ratio** defines as the ratio of packets successfully delivered to the corresponding destination compared to the total number of data packets transmitted by the sender. This can be easily calculated by dividing the number of received data packets at the destination by the number of data packets that were transmitted by the sender.
* **Average hop-count** refers to the number of intermediate nodes that participate in the transmission process to deliver data packets from the source node to a given destination.
* **Average packet delay** defines as the average delay that elapses between sending a data packet from a source and its arrival at a given destination, which may include delays due to route discovery, queuing, and re-transmissions.
## 3 Results and Analysis
### Failure Patterns  
The failure pattern of specimens at different high temperatures are shown in Figure 7 and Table 5.  
Figure 6: Temperature–time curve of the bone zone between rebar and SFRC at (**a**) 200 \({}^{\circ}\)C, (**b**) 400 \({}^{\circ}\)C, and (**c**) 600 \({}^{\circ}\)C.  
Figure 7: (**a**) Comparison of failure modes, (**b**) splitting failure pattern, and (**c**) split-pullout failure patterns of specimens.  
It is shown in Figure 7 and Table 5 that the temperature significantly alters the failure patterns of NC specimens and has less of an effect on the failure patterns of SFRC specimens. In NC specimens, splitting failure occurred below 400 \({}^{\circ}\)C and split-pullout failure above 600 \({}^{\circ}\)C. In comparison, split-pullout failure occurred in all SFRC specimens at each temperature. The main longitudinal crack widths of the destructive SFRC specimens were significantly smaller than those of the NC specimens. The width and length of the cracks gradually decreased as \(V_{f}\) increased. The SFRC specimens had many small uneven cracks scattered in a radial pattern on both sides of the main crack.  
The reason for the above two failure patterns can be explained as follows. The protruding rib of the rebar produces an oblique squeeze force on the concrete after the rebar is pulled. The horizontal component of the oblique extrusion force causes horizontal tensile and shear force in the concrete around the rebar. This results in internal oblique tapered cracks in the concrete. The radial component of the oblique squeeze force causes the concrete to produce circumferential tensile stress, which causes internal radial cracks in the concrete. The temperature reduces the tensile strength of the concrete in NC specimens, which allows radial cracks to extend over the surface of the specimen. Radial cracks link up between reinforcing ribs and splitting cracks form. The mechanical bite force is quickly lost and splitting failure occurs. SFRC specimens have higher tensile strength than concrete due to the incorporation of steel fibers. This retards the generation of cracks within the concrete. Moreover, the synergy between steel fibers and aggregates allows them to act as a rigid skeleton for bridging cracks [17; 21; 22]. Expansion of the internal oblique tapered cracks and radial cracks is prevented. Ultimately, split-pullout failure occurs in the SFRC specimens.
## 4 Discussion
### Buckling
#### 4.3.2 Fbln5\(-\)/\(-\) Mouse Model  
Buckling has not been observed, in the course of the FEM simulations, for the Fbln5\(-\)/\(-\) Fung law, when the initial stretch was 1.4, although very high internal pressure (400 mmHg) was applied. The authors of [19] also failed to estimate a reasonable value of buckling pressure for \(\lambda_{z}=1.4\) (it is greater than 600 mmHg). Buckling occurred in the remaining cases. Similarly, as for the Eln+/+ mouse, the Fbln5\(-\)/\(-\) arteries buckle in the FEM analysis in the form shown in Figure 7b for the Fung law and \(\lambda_{z}=1.0\) and as presented in Figure 7a for the remaining cases of initial longitudinal stretch.  
The accuracy of the FEM analyses for \(\lambda_{z}=1.0\) for both laws is rather low, as the relative errors between the calculations and the experimental value are 43.1% and 41.4% for the Fung and Holzapfel laws, respectively. The FEM results resemble the experiment for the carotid artery stretched at the beginning of the test to 1.2 quite precisely for the Fung law (the error is 8.7%), while the response calculated with the help of the Holzapfel law is slightly overestimated (the error yields \(\sim\)18.3%). There is a significant difference between the buckling pressure estimations between the Fung and Holzapfel laws for the higher stretch, 1.4 here. The Fung material law does not allow the artery to buckle, whereas the Holzapfel law does, and the difference between the experimental reference value and the FEM estimation is actually not so big. It must be emphasized that according to [19], 9 out of 10 tested \(\mathrm{Fbln5}\)\(-\)/\(-\) carotid arteries, which were stretched initially to 1.4, did not buckle. The maximum pressure during the test for this case was 200 mmHg. Therefore, the experimental buckling pressure for this artery was determined by the linear extrapolation of the results for lower stretch ratios. For this reason, this experimental reference may be treated as uncertain and is additionally marked with * in Table 3. In view of this, we claim that it is possible that the artery stretched to \(\lambda_{z}=1.4\) would still buckle, as observed in FEM analysis when the Holzapfel law was used. These results suggest that the sets of constants of the Holzapfel constitutive relation proposed in Section 3.3 allow for a better estimation of \(\mathrm{Fbln5}\)\(-\)/\(-\) carotid artery buckling response compared to the predictions of buckling performed with the aid of the Fung law and the constants available in [19].