esa-sceva/satcom-chunk-collection huggingface dataset:
# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
## 2 Conventional Approaches
### Pathway Analysis Methods
#### 2.3.2 Functional Class Scoring (FCS)  
Functional class scoring (FCS) methods look for coordinated variations in the metabolites belonging to a specific pathway. In fact, FCS methods take into account those coordinated changes within the individual set of metabolites that, although weak, can have a significant effect of specific pathways [75; 78]. Essentially, all FCS methods comprise three steps (see Figure 12):  
1. A statistical approach is applied to compute differential expression of individual metabolites (metabolite-level statistics), looking for correlations of molecular measurements with phenotype [87]. Those mostly used consider the analysis of variance (ANOVA) [88], Q-statistic [89], signal-to-noise ratio [90], \(t\)-test [91], and Z-score [92]. The choice of the most suitable statistical approach may depend on the number of biological replicates and on the effect of the metabolites set on a specific pathway [93].
2. Initial statistics for all metabolites of a given pathway are combined into statistics on different pathways (pathway-level statistics) that can consider interdependencies among metabolites (multivariate) [94] or not (univariate) [91]. The pathway-level statistics usually is performed in terms of the Kolmogorov-Smirnov statistics [90], mean or median of metabolite-level statistics [93], the Wilcoxon rank sum [95], and the maxmean statistics [96]. Note that, although multivariate statistics should have more statistical significance, univariate statistics provide the best results if applied to the data of biologic systems (\(p\leq 0.001\)) [97].  
Figure 11: A 3D Venn diagram illustrating the relation between ORA parameters (Equation (6)) in which \(N\) corresponds to the number of background compounds, \(n\) is the number of the measured metabolites, \(M\) is the number of background metabolites mapping the \(i\)th pathway, and \(k\) represents the overlap between \(M\) and \(n\).  
3. The last FCS step corresponds to estimating the significance of the so-called pathway-level statistics. In detail, the null hypothesis can be tested into two different ways: (i) by permuting metabolite labels for every pathways, so comparing the set of metabolites in that pathway with a set of metabolites not included in that pathway (competitive null hypothesis) [75] and (ii) by permuting class labels for every sample, so comparing the collection of metabolites in a considered pathway with itself, whereas the metabolites excluded by that pathway are not considered (self-contained null hypothesis) [91].
## 4 Result
### Obstacle Recognition Distance by Road Lighting Classes  
One-way ANOVA was conducted to verify whether there was a significant difference in the subjects' recognition distance depending on the road lighting grade under the same weather conditions and the same color temperature, and the results are shown in Table 3.  
As a result, it was analyzed that the difference in obstacle recognition distance by road lighting classes in 4940 K was statistically significant in normal (F = 10.966, \(p\) < 0.001), fog (F = 35.412, \(p\) < 0.001), and rain (F = 9.126, \(p\) < 0.001) conditions. Similarly, in the lighting of 3006 K, the difference in obstacle recognition distance by road lighting classes showed significant differences in normal (F = 13.303, \(p\) < 0.001), fog (F = 10.765, \(p\) < 0.001), and rain (F = 13.468, \(p\) < 0.001) conditions.  
Additionally, Tukey HSD post hoc analysis, which is used when the sample size is equal, was performed. As a result, under the condition of 3006 K lighting in a fog, the obstacle recognition distance of M1 and M3 was found to be longer than that of M5, and in all conditions except this, the obstacle recognition distance of M1 was found to be longer than that of M3 and M5. In order to check the change in obstacle recognition distance according to road lighting grade, when the obstacle recognition distance of class M1 was set to 100% in each weather condition, the ratio in M3 and M5 situations is shown in Table 4.  
Figure 6: Subject obstacle recognition distance measurement results by experimental condition.  
Through the analysis results, it was verified that there was a significant difference in obstacle recognition distance by road lighting classes in all weather conditions, and it was confirmed that the obstacle recognition distance became longer as the road lighting class increased. Therefore, it was verified that obstacle recognition distance evaluation is necessary for each road lighting class during adverse weather.
## 3 Results
### Effects of SHE on LPS-Treated RAW264.7 and Caco-2 Cells  
The cytotoxicity of SHE was evaluated in RAW264.7 and Caco-2 cells. After 48 h, SHE exhibited survival rates of 74.2% and 72.3% for RAW264.7 cells at 25 and 50 mg/L, and 78.3% and 77.6% for Caco-2 cells at 25 and 50 mg/L, respectively (Figure 2A). TNF-\(\alpha\) and IL-6 inflammatory markers were analyzed in RAW264.7 macrophages treated with SHE for 4 and 20 h. LPS treatment significantly increased the levels of TNF-\(\alpha\) and IL-6 compared to those in the control, whereas SHE demonstrated an anti-inflammatory effect by lowering the levels of TNF-\(\alpha\) and IL-6 by 67.2% and 51.9%, respectively, compared to those in the LPS control at 40 mg/L (Figure 2B,C). Caco-2 cells treated with LPS were used to examine the expression of genes involved in the tight junction and mTOR axis to determine the underlying mechanisms of SHE (Figure 2D-J). LPS significantly decreased _Occludin_ mRNA level, and increased _Motr_ mRNA level compared to those in the control (Figure 2D,I), whereas SHE significantly decreased _Pi3k_, _Motr_, and _S6k_ mRNA expressions compared to those in the LPS control at 50 mg/L (Figure 2G,I,J). There were no significant differences in the mRNA levels of _Claudin1_, _Zo-1_, and _Akt_ (Figure 2E,F,H).
## 3 Experimental Results
### Sensing Properties Analysis
It has been reported in the literature that the temperature and the geometric shape of the sensor (i.e., pellets or films) plays a quite important role in the chemical interaction between the test gases and the chemisorbed oxygen on the material's surface [20; 24; 38; 39], contributing to producing changes in the electrical resistivity, as shown in Figure 7a,b. This trend is typical of a semiconductor material like the one studied in this work [20; 24; 37; 38; 40]. Performing the test on a particle at 300 \({}^{\circ}\)C, the resistivity of the material was high, with an almost linear trend regardless of the CO concentrations. However, by increasing the operating temperature to 200 \({}^{\circ}\)C, the conductivity increased, and the resistivity decreased. In the case of the oxide pellets, they showed inflection points at 200 \({}^{\circ}\)C for both the CO atmospheres (see Figure 7b). The resistivity inflection points shown by the pellets at this temperature are clear indications of the transition that the material undergoes due to the increase in the operating temperature of the sensor. On the other hand, at 300 \({}^{\circ}\)C, the resistivity in the atmospheres decreased significantly, where the minimum value of resistivity in carbon monoxide was of 94.0 \(\Omega\)m at 300 ppm. Considering the results shown in Figure 7a,b, the high response is attributed mainly to the nanometric size obtained during the synthesis process of the oxide. Recent reports have shown that by reducing the particle size of semiconductors, the area-to-volume ratio considerably increases thus favoring high gas adsorption on the surface of the material [30; 40; 41], leading to a high response in different types of gases.
# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 5 Conclusions  
This study introduces a novel method for detecting breast cancer in mammograms, combining region of interest optimization and deep learning with gradient-weighted class activation mapping to generate bounding boxes. The proposed method is evaluated on six public datasets with diverse characteristics: VinDr-Mammo, MiniDDSM, CMMD, CDDC-CESM, BMCD, and RSNA. Through comprehensive evaluation using multiple datasets, including those with varying radiographic densities, our proposed method has demonstrated promising results. Specifically, the predicted F1 score, which serves as a measure of overall accuracy, consistently outperforms the baseline methods, indicating the robustness of our models in accurately delineating tumor boundaries within this specific dataset.  
The effectiveness and robustness of the method are further demonstrated by comparing its performance against several baseline methods that employ different region of interest detection techniques and convolutional neural network models. Our method exhibited superior performance across all datasets and metrics, highlighting its potential clinical and research implications. The proposed method has several noteworthy implications. First, it can provide radiologists with visual cues and confidence scores for lesions in mammograms, aiding in breast cancer screening and diagnosis. This can significantly enhance the accuracy and efficiency of the diagnostic process. Additionally, the method offers a straightforward and effective way to create regions of interest from image classification models, enabling the development of new convolutional neural network models specifically tailored for breast cancer detection.  
Moreover, the method's utilization of gradient-weighted class activation mapping opens up possibilities for its application in other medical image analysis tasks that require region of interest optimization and deep learning. This technique could inspire new avenues of research and development in the field of medical imaging. The proposed method demonstrates its effectiveness in detecting breast cancer in mammograms through the integration of region of interest optimization and gradient-weighted class activation mapping. Its superior performance, particularly in accurately delineating tumor boundaries, underscores its potential for clinical implementation and further advancements in the field. Future research can focus on refining and expanding the methodology to address specific challenges and further improve its overall efficacy in breast cancer detection and diagnosis.  
Software, A.T.T. and H.N.H.; methodology, T.N.T. and H.N.H.; data curation, T.N.T. and A.T.T.; writing--original draft preparation: H.N.H. and T.N.T.; writing--review and editing: T.N.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study.  
The authors declare no conflict of interest.
# Capacity Analysis of Incentive Schemes in Opportunistic Networks
## 3 Verification of the Model with Simulations  
The verification compares incentive degrees and delivery ratios with two typical incentive schemes, namely, the incentive and privacy-aware data dissemination scheme (IPAD) [12] and the probabilistic routing scheme based on game theory (PRGT) [13]. The two credit-based schemes use different calculation parameters. IPAD uses time-to-live for prices and degree of selfishness for rewards. PRGT evalutes social degrees and transmission history with a Markov chain to calculate prices and message importance for rewards. The following simulations are conducted by OMNet++ ([[https://omnetpp.org](https://omnetpp.org)]([https://omnetpp.org](https://omnetpp.org)) (accessed on 20 August 2022)) with the OPS framework ([[https://github.com/ComNets-Bremen/OPS](https://github.com/ComNets-Bremen/OPS)]([https://github.com/ComNets-Bremen/OPS](https://github.com/ComNets-Bremen/OPS)) (accessed on 20 August 2022)). The parameter setting is listed in Table 1 for maritime environments where communications are over long ranges, which is associated with sparse density of node points. Random movement is selected because it is the part of mobile model in the latter network capacity analysis used to show the average theoretically. Nodes' selfish behavior is partly cooperative [17] in our simulations. Certain parameters, such as movement and the type of selfish behavior, are reflected by \(\lambda_{m}\) indirectly.  
Because successful transmissions in opportunistic networks are highly dependent on mobility and node forwarding, selfish nodes reduce delivery ratios [13] remarkably. Utility  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulation time & 8 hours \\ Area size & \(5\times 5\) km\({}^{2}\) \\ Transmission range & 300 m \\ Transmission speed & 1 Mbps \\ Node count & 100 \\ Message interval & 15 s \\ Message size & truncnormal \((1.5,1)\) MiB \\ Buffer size & 56 MiB \\ Node selfish & bernoulli \((0.7)\) \\ Movement change interval & truncnormal \((30,15)\) min \\ Movement angle delta & normal \((0,30)^{\circ}\) \\ Movement velocity & normal \((30,10)\) mps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\(\$^{*}\) represents the incentive degree, which can be reflected by delivery ratios in the network with selfish nodes. In comparison, the co-domain of \(\$^{*}\) is rescaled with the same co-domain of delivery ratios due to the different scalars. The rescaled values are analyzed by mean square errors (MSE), as shown in Table 2 and Figures 3 and 4. We analyze each of them in the following subsections. Another important metric for opportunistic networks is the contact time of the nodes. Because selfish nodes decrease contact times due to the lower forwarding rate and incentive schemes try to increase the rate shown by delivery ratios, it is implied in delivery ratios that there is a higher delivery ratio with a lower contact time if there are no extra factors, such as attackers.
## 2 Materials and Methods
### Data Collection and Preprocessing
#### 2.1.2 Dealing with an Imbalanced Dataset  
Due to the smaller sample size of ICU patients who either died or were readmitted compared to those who survived and were not readmitted, we employed the synthetic minority oversampling technique (SMOTE) to balance the dataset. SMOTE is a widely used oversampling method in machine learning for handling imbalanced data [30]. This technique generates new samples for the minority class by interpolating between the nearest neighbors of existing minority class instances. These synthetic samples are created based on the features of the original dataset, ensuring that they closely resembled the original minority class instances [24].
## 4 Results
### Models' Comparative Analysis
#### 4.4.6 Discussion of Results  
This paper focused on forecasting GHI at one radiometric station in South Africa using high-frequency data (measured at one-minute intervals) obtained from the Vuwani radiometric station (USAid Venda). The data is from January 2020 to October 2020. Based on the RMSE and rRMSE, the GA model was found to be the best model. However, based on MAE and rMAE, RNN was the best model. With these results, it was then difficult to select the best model. Further evaluation of the forecasts were then done based on the Diebold-Mariano tests, Giacomini-White tests, and Murphy diagrams. From the Diebold-Mariano tests, the null hypothesis that the forecast accuracy between a pair of forecasts from the two methods is the same was rejected for all the three pairs. Based on the Murphy diagrams, the GA dominated both RNN and KNN, meaning that it provides the greatest predictive ability. Similarly, with the Giacomini-White tests, GA was found to have the best conditional predictive ability compared to the other two models.  
Motivated by previous research by other authors such as [13, 52], among others, a GA model was developed and compared to RNN and KNN models. Using South African hourly GHI data from University of Pretoria radiometric station, [52] used the long short-term memory (LSTM) network, feed forward neural network (FFNN), support vector regression (SVR), and principal component regression (PCR). Based on the RMSE, the GA model used in the current paper has a smaller evaluation metric value compared to those of the models used in [52].
# Research on Compressed Sensing Spectrometry Based on Electro-Optical Transmittance Coding
## 2 Principle
The point is that the coding measurement matrix \(\Phi\) (\(\Phi\in R^{M\times N}\)) must be optimally designed to ensure that it is irrelevant as far as possible. By combining Equations (1) and (2), the design and implementation of \(\Phi\) can be achieved by coding the spectral transmittance via the external electric field voltage. \(\Phi\) composed of different spectral transmittance coding can be further described as  
\[\Phi=\left[ \begin{array}{ccc}T_{(\lambda_{1},V_{1})}&\ldots&T_{(\lambda_{N },V_{1})}\\ \ldots&T_{(\lambda_{i},V_{j})}&\ldots\\ T_{(\lambda_{i},V_{M})}&\ldots&T_{(\lambda_{N},V_{M})}\\ \end{array} \right] \tag{5}\]  
For the \(N\) spectrum channels, \(M\) external electric field voltages are used to obtain the compressed sensing measurement data, where M << N, as shown in Figure 2.  
The measurement matrix in Equation (5) is particularly similar to the partial Fourier matrix in the typical compressed sensing measurement matrix. Each element of \(\Phi\) is \(T_{(\lambda_{i},V_{i})}=\sin^{2}(\delta_{(\lambda_{i},V_{i})}/2)=1/2-1/2\cos( \beta_{(V_{i})}2\pi/\lambda_{i})\), where \(\beta_{(V_{i})}\) is determined by the optical path difference induced by the LiNbO\({}_{3}\) electro-optic crystal. This parameter can be randomly selected after the transmittance calibration experiment is carried out. From the calibration results, the most uncorrelated transmittance under \(M\) external electric field voltages is selected, and \(M\) highly uncorrelated compressed sensing measurements can be realized. Most of the spectral signals are sparse. The sparsity for the \(N\) channels spectral signals can be expressed as \(x=\Psi\alpha\). \(\alpha\) is the K-sparse vector of spectral signals (including K <<\(N\) non-zero elements), and \(\Psi\) is the sparse matrix. Equation (4) can be rewritten as  
\[y=\Phi x=\Phi\Psi\alpha=A\alpha, \tag{6}\]  
where \(A\) is the sensing matrix, this is, \(A=\Phi\Psi\). For continuous spectral signals, mathematical transformations such as FFT, DCT and the wavelet basis or learning dictionary can be applied to the sparsity of spectral signals [7]. Thus, the sparse matrix \(\Psi\), combined with the coding measurement matrix \(\Phi\), can be used to determine the sensing matrix \(A\). Therefore, with the compressed sensing measurement signals \(y\) and after solving the inverse problem of Equation (6) and the optimal spectral sparse signal, \(\hat{\alpha}\) can be solved. \[\hat{\alpha}=\underset{\alpha}{\text{argmin}}\{\|y-A\hat{\alpha}\|_{2}+r\| \alpha\|_{1}\}. \tag{7}\]  
where, \(r\) is a regularization parameter. The spectrum signal can be recovered from the sparse signal \(\hat{\alpha}=\Psi\hat{\alpha}\).
## 3 Experiment and Results
### Inspection System  
Figure 10 shows the prototype of the inspection system. In the magnetic leakage testing (MFLT) module, the magnetizer has a pole distance of 15 mm and has manufactured the profile following the tube and membrane surfaces. It maintains about 1.0 mm of distance above the specimen surface by the support of the three wheels. The magnetizer has 100 turns of copper wire and supplied by a current of about 200 mA to produce a magnetic field into the specimen. There are 48 Hall sensors arrayed at an interval of 2.5 mm on a curve following the tube and membrane surfaces. The MFLT probe scanned the specimen with steps of 4.0 mm. In the FUP, there are 6 flexible ultrasound transducers having a center frequency of 5 MHz. The MFLT module, including the magnetizer and magnetic sensor array, can be exchanged with the FUP module. The measured signal can be processed and displayed in real-time in a LabVIEW software on a notebook.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**\#**} & **Width** & **Length** & **Depth** & **Position** & **\#** & **Width** & **Length** & **Depth** & **Position** \\  & **[mm]** & **[mm]** & **[mm]** & **(mm)** & **\#** & **[mm]** & **[mm]** & **[mm]** & **[mm]** & **[mm]** \\ \hline \multicolumn{8}{c}{**Weld-1**} \\ \hline w11 & 7 & 30 & 0.9 & 230 & w31 & 7 & 60 & 0.6 & 245 \\ \hline w12 & 7 & 60 & 0.9 & 530 & w32 & 7 & 60 & 1.5 & 545 \\ \hline w13 & 7 & 100 & 0.9 & 830 & w33 & 7 & 60 & 3.0 & 845 \\ \hline \multicolumn{8}{c}{**Weld-2**} \\ \hline w21 & 7 & 40 & 0.9 & 330 & w41 & 7 & 60 & 0.9 & 345 \\ \hline w22 & 7 & 70 & 0.9 & 630 & w42 & 7 & 60 & 1.8 & 645 \\ \hline \end{tabular}
\end{table}
Table 4: Specification of slit-type damages on the Weld-1–4.  
Figure 9: Sample specimens with four water-cooled tubes and artificial damages.
## 4 Numerical Example and Verification
### Effect of Applied Torque Levels  
The applied torque should be limited within a certain range to prevent the contact stress exceeding the elastic limit of the material, which can be determined by using this verified model. Taking the case of an NNM and LCM cycloid gear pair in the previous section as an example, the effects of various applied torque levels from 50 to 550 Nm on the contact stress and contact pattern were investigated, as shown in Figure 13.  
Figure 12: The loaded transmission error and gear ratio of the NNM cycloid gear pair for four cases of TLM in a mesh circle.  
Figure 13: Contact stresses and contact patterns of the NNM and LCM cycloid gear pair under applied torque values of 50, 150, 250, 350, 450, and 550 Nm.  
It was observed that under lightly loaded conditions, the example cycloid gear pair had a lower number of tooth pairs in contact--only one or two. As the applied torque increased, this number increased due to the deflection of the contacting tooth pairs, to four under torque of 550 Nm in this case. Figure 13 also shows the contact pattern predicted by the proposed model. It can be seen that the contact was localized at the middle of the tooth width of the gear with no edge contact when the load distributed on the tooth was low, referring to the contact pattern on roller number 9 under torque of 150 Nm. Increasing torque caused the contact pattern to extend towards the edge of the tooth, accompanied by increasing contact stress.  
Figure 14 shows the effects of the various applied torque levels on the LTE and GR. It can be seen that as the torque level increased, the peak-to-peak and absolute mean values of LTE, which is represented by the error bars, as well as the peak-to peak values of GR, changed proportionally with torque, which is a result of the possibility of the contact deformation becoming significant with the increase of the load distributed on the tooth. For instance, at torque of 150 Nm, the peak-to-peak and absolute mean values of LTE were 1.70 arcseconds and 14.49 arcseconds, respectively, and the peak-to-peak value of GR was about 0.08. As the torque increased to 450 Nm, the values increased proportionally to 3.77 arcseconds, 24.19 arcseconds, and 0.26.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 5 Experimental Verification  
To validate the TCS design of the Luojia 1-01 satellite, and also to provide reference for model modification, the flight model of the satellite has gone through the ground thermal balanced test. Satellite on-orbit performance is normally predicted by thermal balanced test.