esa-sceva/satcom-chunk-collection huggingface dataset:
# Panchromatic and Multispectral Image Fusion Combining GHS, NSST, and PCA
## 4 Results
### Experimental Results
#### 4.2.1 Experimental Results with References
The scene image is reddish with these two methods, while the GS and PCA methods are greenish. Conversely, the SFIM method and the method in this paper have an excellent overall spectrum. The quantitative evaluation results in Table 4 show that the methods in this paper obtained the best results for the SSIM, CC, UIQI, and SAM indexes. At the same time, SFIM had the best results for the AG and ERGAS indexes, which is also consistent with the visual results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AG** & **SSIM** & **CC** & **UIQI** & **SAM** & **ERGAS** \\ \hline GT & 19.4637 & 1.0000 & 1.0000 & 1.0000 & 0.0000 & 0.0000 \\ EXP & 6.7747 & 0.7495 & 0.9647 & 0.4070 & 46.5098 & 1.9421 \\ GIHS & 12.5299 & 0.9168 & 0.9845 & 0.7176 & 33.7666 & 2.2510 \\ SFIM & 11.5038 & 0.9169 & 0.9877 & 0.7100 & **30.3048** & **1.8093** \\ Brovey & 12.4041 & 0.9204 & 0.9855 & 0.7184 & 32.9954 & 1.9421 \\ GS & 12.2915 & **0.9308** & 0.9872 & 0.7311 & 32.1928 & 2.0890 \\ PCA & 12.2212 & 0.9089 & 0.9781 & 0.7120 & 39.3873 & 2.3635 \\ Proposed & **12.5323** & 0.9117 & **0.9882** & **0.7436** & 32.1953 & 2.1581 \\ \hline \hline \end{tabular} Bold indicates best results.  
\end{table} 
Table 3: Objective assessment indexes of the WV-4 image fusion results. Figure 5: Fusion results for the WV-4 images using different methods. Figure 7 shows the fusion results of the GFDM images using different algorithms, and the scene consists mainly of a desert. The figure shows that the color bias of the GIHS, Brovey, and GS methods is serious, while the color bias of the PCA method is better for the desert scene. Furthermore, the results of the SFIM and this paper's method are relatively good. In addition, the sharpness of this paper's method is significantly higher than the other methods. This can also be verified from the quantitative evaluation results in Table 5. The AG and SSIM indexes of this paper's method demonstrate that it had the best performance, while SFIM obtained better results for CC, SAM, and ERGAS, indicating its better spectral retention ability. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AG** & **SSIM** & **CC** & **UIQI** & **SAM** & **ERGAS** \\ \hline GT & 26.7046 & 1.0000 & 1.0000 & 1.0000 & 0.0000 & 0.0000 \\ EXP & 13.9148 & 0.7457 & 0.9262 & 0.5906 & 2.1305 & 1.7529 \\ GIHS & 21.3507 & 0.7901 & 0.9346 & 0.7120 & 2.1663 & 1.7281 \\ SFIM & **25.1842** & 0.8235 & 0.9444 & 0.7147 & 2.0730 & **1.4396** \\ Brovey & 21.6986 & 0.7861 & 0.9273 & 0.7061 & 2.1305 & 1.7403 \\ GS & 22.6489 & 0.7383 & 0.9035 & 0.6493 & 3.2087 & 2.0708 \\ PCA & 24.7823 & 0.5646 & 0.7376 & 0.4954 & 4.8963 & 3.1430 \\ Proposed & 21.5021 & **0.8312** & **0.9492** & **0.7149** & **2.0596** & 1.5690 \\ \hline \hline \end{tabular} Bold indicates best results.  
\end{table} 
Table 4: Objective assessment indexes of the GF-7 image fusion results. Figure 6: Fusion results for the GF-7 images using different methods.
## 4 Engineering Application of the ASRB Parameter Design Method
### Analysis and Design Optimization of ASRB Parameters
#### 4.3.2 Sensitivity Analysis  
To reflect the sensitivity of the test indicators to various factors, the range of the average stress release rate and the average volume release energy under each ASRB arrangement scheme obtained from the 16 groups of orthogonal tests mentioned above were calculated. If the range was large, this indicated that the factor has a significant impact on the test index, and vice versa. The range results of each scheme are shown in Tables A1 and A2, and the range changes of each factor at different monitoring locations and areas are shown in Figure 15.  
From Tables A1 and A2, and Figure 15, the following conclusions can be drawn. (1) Compared with the calculated results of stress and energy, the two image trends were basically the same, and the laws and change trends of each factor were roughly the same. Similar conclusions were obtained for both angles, thus proving the reliability of the results to a certain extent. (2) By comparing the extreme variation in each factor, it can be seen that on each stress release area fore and atf of the current tunneling head, the order of sensitivity of each factor to the stress release effect was \"aperture\" > \"inclination\" > \"included angle\" > \"spacing\" > \"length\" for the most part. According to the order of sensitivity, the arrangement parameters of the corresponding ASRBs can be adjusted to effectively improve the pressure release and  
Figure 14: TBM excavation simulation model and relevant dimensions of ASRBs (the model of test plan 1 is taken as an example).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment Number**} & \multicolumn{5}{c}{**Average Stress Release Rate (\%)**} & \multicolumn{5}{c}{**Average Volume Release Energy (KJ/m\({}^{*}\))**} \\ \cline{2-10}  & **I** & **II** & **III** & **IV** & **V** & **A\({}_{1}\)** & **A\({}_{2}\)**  
energy dissipation effect. (3) Along the TBM advance direction, that is, from I to V in the stress calculation and from A\({}_{1}\) to A\({}_{4}\) in the energy calculation, the sensitivity of various borehole parameters to the stress release effect showed a decreasing trend. In other words, when the ASRB parameters changed, the stress release effect of the rock mass close to the surrounding rock area was greater, while for the area far away from the rock wall, the change in borehole parameters had a smaller impact on the stress release effect.
# Scattering or Pushing for Object Singulation in Cluttered Environment: Case Study with Soma Cube
## 8 Conclusions  
In this paper, both prehensile and non-prehensile manipulations are used for object singulation in cluttered environment. Non-prehensile manipulation is divided into quasi-static and dynamic manipulation. We proposed a hybrid singulation approach combining the merits of both dynamic scattering and pushing approach. The Soma cube was employed to corroborate the effectiveness of the proposed algorithm. The proposed hybrid singulation approach achieves desired singulation in less time when compared to pure pushing approach. It is expected that the proposed hybrid object singulation algorithm could be applied to general application areas. For the future work, we will consider the following, First, to assemble the Soma cube in a given formulation, the blocks need to be removed and assembled in a specific, not a random order, which is not considered in the paper. Moreover, the collision with other obstacles in the environment and target based hybrid singulation need to be considered.  
Conceptualization: B.-J.Y., M.U.A.K. and J.Y.L.; methodology: M.U.A.K., A.I. and B.-J.Y.; software: M.U.A.K., H.H., A.I. and S.K.; writing--original draft preparation: M.U.A.K.; writing--review and editing: M.U.A.K., J.Y.L. and B.-J.Y. supervision: B.-J.Y., J.Y.L. and S.L.; experimentation: S.K. and M.U.A.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Technology Innovation Program (or Industrial Strategic Technology Development Program) (20001856, Development of robotic Work Control Technology Capable of Grasping and Manipulating various Objects in Everyday Life Environment Based on Multimodal Recognition and Using tools) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).  
The authors declare no conflict of interest.
# A Survey on Change Detection and Time Series Analysis with Applications
## 2 Decomposition Methods into Frequency Domain
### Recent Methods of Mitigating Spectral Leakages in Spectrum Beyond Aliasing
#### 2.4.1 Interpolation by Matching Pursuit (MIMAP)  
The MIMAP is a spectral analysis method that uses the gradients of a coarsely sampled time series, presenting severe aliasing to estimate the true frequency spectrum of the time series [25]. For each frequency, MIMAP simultaneously minimizes the L2 norm of the residual series and gradients to estimate the coefficients of cosine and sine functions. The rest of the process is similar to IMAP, i.e., a frequency \(\omega_{1}\) corresponding to the largest peak in the spectrum is selected, and the contributions of the sinusoidal basis functions of frequency \(\omega_{1}\) to the time series and its gradients are subtracted to obtain the residual series and gradients. In the next step, the residual series and residual gradients are treated as new inputs to choose a frequency \(\omega_{2}\) corresponding to the largest peak in the new residual spectrum, and this procedure continues until the desired frequencies are estimated.  
Like IMAP, MIMAP estimates the frequencies and corresponding amplitudes one at a time (out-of-context), ignoring the correlations between the sinusoids of different frequencies. Therefore, the estimated frequencies and amplitudes may not be accurate, resulting in a large number of iterations and less accuracy compared to when these correlations are considered [19; 27].
## 3 Proposed Technique
### Phase Bias Detection
A higher threshold may result in missed detection, while a lower threshold may result in false alarms that cause compensation to be triggered frequently, increasing the risk of the tracking loop losing lock. The recommended principle is to set a relatively high threshold to reduce false alarms. Based on our testing, the above thresholds work well in most situations. To increase the detection probability, the designed indicators can also be extended to combine more advanced techniques, such as interference direction estimation and interference source localization. More details are beyond the scope of this paper.
## 3 Results
### Health Risk Assessment  
#### 3.2.1 Non-Carcinogenic Analysis  
The values of exposure dose (D) and hazard quotient (HQ) are presented in Table 4. The ascendent trend of exposure dose, based on potentially toxic elements concentrations, is as following Cd > Cr > Sb > Co > Mn > Pb > Cu > Ni > Li > Zn > Ba > Fe. In case of HQ, the values decrease in order Cd > Co > Cr > Sb > Zn > Mn > Ni > Ba > Li > Pb > Cu > Fe. The interpretation and description of HQ and HI is presented in Table 5.  
Except Fe, the estimated levels of hazard quotient were lower than 1. This means that repeated exposure of children does not cause side effects. However, in case of Fe, only one sample had the value lower than 1. This implies the appearance of some non-carcinogenic side effects, after repeated exposure to tested samples.  
Regarding the hazard index values, obtained for hazard quotient of all analyzed elements, ranged between 9.10 \(\times\) 10\({}^{-1}\) and 2.42 \(\times\) 10\({}^{1}\), with an average value of 7.10. Of the total of 19 samples, the sample that had the value HQ < 1 also had the value HI < 1. The rest of samples recorded HI values greater than 1. This means that after repeated exposure, some non-carcinogenic side effects can appear.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **RfD\({}_{\text{oral}}\) (mg/kg)** & **CSF\({}_{\text{oral}}\) (mg/kg/day)** \\ \hline Ba & 7.0 \(\times\) 10\({}^{-2}\) a & - \\ Co & 2.0 \(\times\) 10\({}^{-2}\) b & - \\ Cu & 3.7 \(\times\) 10\({}^{-2}\) b & - \\ Zn & 3.0 \(\times\) 10\({}^{-1}\) b & - \\ Mn & 4.7 \(\times\) 10\({}^{-2}\) c & - \\ Ni & 2.0 \(\times\) 10\({}^{-2}\) b & 8.4 \(\times\) 10\({}^{-1}\) a \\ Li & 2.8 \(\times\) 10\({}^{-2}\) d & - \\ Fe & 7.0 \(\times\) 10\({}^{-2}\) e & - \\ Pb & 3.6 \(\times\) 10\({}^{-3}\) b & 8.50 a  \\ Cd & 5.0 \(\times\) 10\({}^{-4}\) b & 6.10 a  \\ Cr & 3.0 \(\times\) 10\({}^{-3}\) b & 4.1 \(\times\) 10\({}^{1}\) a  \\ Sb & 3.5 \(\times\) 10\({}^{-4}\) f & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: RfD and CSF values for oral ingestion.
# Selected Aspects of Designing Modular PEMFC Stacks as Power Sources for Unmanned Aerial Vehicles
## 3 Results
### Electrical Tests of the 500-W PEMFC Stack
During electrical loads, given certain faults in the module, the energy management system is capable of reducing power; alternatively, the faulty stack section can be shut down. Assumptions have been developed for technical supervision procedures and the flight safety of hydrogen-powered drones. Redundant fuel cell power sources were tested in order to supply electrical devices. Simultaneous parallel operation of the fuel cell modules and the electrochemical battery connected passively (by means of blocking diodes) increases the operational reliability of electrochemical power sources [33; 48]. In a passive parallel connection, electricity is initially drawn from a higher voltage source. The increase in load, and thus the voltage drop, equalizes the voltages of the connected sources and starts their simultaneous operation. The load distribution between individual sources working in parallel is automatic and does not require an additional control and control system or additional DC/DC converters, which would increase losses, demand for auxiliary power and increase the weight of the energy source. The waveform of electrical power (P) vs. time recorded during the operation of single servomechanism powered directly from the electric power source involving a 500-W modular PEMFC fuel cell stack is presented in Figure 22.  
These data were recorded during the variation of the steering angle, at which time the set servomechanism position was maintained. The servomechanisms, unlike the propulsion engine, were characterized by lower, albeit dynamic, variations in electric load. This resulted from the need to change the position as quickly as possible and to maintain the new set position. These tests confirmed the required dynamic PEMFC performance. On the other hand, in Figure 22 the waveform of electrical power (P) vs. time recorded during the operation of servomechanisms powered directly from the electric power source involving a 500-W modular PEMFC fuel cell stack. The investigation of the electrical output of 500 W PEMFC stack during step changes was also performed. Figure 22: The waveform of electrical power (P) vs. time recorded during the operation of servomechanisms powered directly from the electric power source involving a 500-W modular PEMFC fuel cell stack. In Figure 23 the waveform power P vs. time during the variable loads were also recorded under dynamic variable electrical load. In this investigation the electrical output of 500 W PEMFC stack during step changes was investigated. In the first part of the experiment, a rapid increase of electrical power from 0 to 250 W, followed by an increase of power up to 450 W, was observed from 20 to 600 s. In the last stage of the test, electrical power varied within the range 250-500 W for 2000 s. It was found that the constructed PEMFC stack was capable of supplying the required power ranging from 0-500 W in a timely fashion in accordance with the variation of dynamic loads during the performance of servomechanisms.
## 1 Introduction
[PERSON] studied two surface-active warm mixes, Evotherm M1 and Return, and found that warm mixes could improve the penetration and ductility of bitumen and reduce the softening point and bitumen viscosity [16]. [PERSON] studied the effects of different warm mixes on the properties of bitumen and found that the high- and low-temperature performance of the surfactant Evotherm was more balanced, while the adhesion and anti-aging properties were better [17]. [PERSON] used warm-mix bitumen technology to reduce the mixing and compaction temperatures of bitumen; however, the addition of a warm-mix modifier also affected the rheological response of bitumen during pavement use [18]. [PERSON] conducted a temperature scan test on bitumen mixed with Evotherm warm mix using a dynamic shear rheometer and analyzed the complex shear modulus, phase angle, and rutting factor of the modified bitumen. They found that when the temperature was between 28-52 \({}^{\circ}\)C, Evotherm could improve the rutting resistance of bitumen, while at temperatures higher than 52 \({}^{\circ}\)C, the effect of Evotherm diminished. Moreover, Evotherm could improve the elastic recovery properties of bitumen [19]. [PERSON] showed that adding Evotherm during the preparation of rubber bitumen could save energy and reduce emissions [20]. Subsequently, further studies revealed that the Sasobit modifier could improve the rutting resistance of bitumen and Evotherm could improve the fatigue resistance of bitumen [21]. [PERSON] investigated the effects of Sasobit and Evotherm on bitumen properties using the tensile strength ratio (TSR) and retained Marshall stability (RMS) tests. Evotherm-modified bitumen mixes exhibited higher TSR and RMS values than Sasobit [22]. As mentioned above, the modification of bitumen and its mixtures differs among the modifiers of different warm-mix technologies. Therefore, in this study, three warm-mix modifiers, XT-W3 for foaming technology, Evotherm for chemical additives, and Sasobit for organic additives, were separately studied for comparative analysis to investigate their effect on the road performance of bitumen and its mixtures. Virgin bitumen and SBS-modified bitumen and their mixtures were analyzed and studied as control groups.
## 5 Conclusions  
Symmetric Gaussian kernel based criteria such as the MCC and GMCC are not desirable when dealing with asymmetric heavy-tailed system noise in adaptive filtering. This paper proposed the GMACC to build the cost function of robust adaptive filtering, which was the expansion of traditional ITL-based adaptive filtering and included the original algorithms as special cases. The flexible values of the parameters brought many benefits. The different kernel widths in positive and negative axes made the GMACC suitable to deal with asymmetric noise. The shape parameter of the GMACC could balance the steady-state performance and convergence rate of the algorithm. The steady-state performance of the algorithm was analyzed theoretically and verified by experiment. The asymmetric property of a satellite network delay was discovered and described. Simulations verified the effectiveness of the GMACC-based adaptive filter in a satellite network delay prediction. The proposed GMACC-based adaptive filtering performed well under an asymmetricnon-Gaussian system noise environment, and improved the research framework of robust adaptive filtering.  
The proposed GMACC algorithm in this paper was tested in simulation environments only, but it has application prospects in satellite network delay prediction, oil price prediction, etc., where asymmetric non-Gaussian noise exists. Moreover, the adaptability of the GMACC under different types of noise needs more studying in the future.  
Conceptualization, P.Y. and H.Q.; methodology, P.Y. and M.W.; software, M.W.; validation, P.Y., H.Q. and J.Z.; formal analysis, M.W., P.Y. and J.Z.; investigation, P.Y. and M.W.; resources, M.W.; data curation, M.W.; writing--original draft preparation, P.Y.; writing--review and editing, M.W., P.Y., S.Z., T.L. and J.Z.; supervision, S.Z., T.L. and H.Q.; funding acquisition, H.Q. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key Research and Development Project under Grant 2018 YFB18003600 and National Natural Science Foundation of China under Grant 61531013.  
Not applicable.  
The authors declare no conflict of interest.
# NOMA Clustering for Improved Multicast IoT Schemes
###### Abstract  
In the context of future ultra-dense mobile networks, spectrum and energy efficiencies (SE and EE) are critical measures in designing efficient systems for the sixth-generation (6G) of wireless networks. Recognized for their benefits in increasing SE and EE, non-orthogonal multiple access (NOMA) and device-to-device (D2D) communications are combined in this work to present a new NOMA-based D2D scheme increasing the performance in terms of SE and EE. The users in the proposed scheme are split into coalitions. Coalition heads are served in NOMA directly from the base stations, while the other users within the coalitions get the service through D2D links. We investigate the system's SE and EE for different mobility patterns, and we discuss optimal system configurations with the help of Monte Carlo simulations. The obtained results show that the proposed system exhibits a better performance compared to conventional OMA and NOMA models, especially in low mobility contexts.  
6G; D2D; energy efficiency; spectrum efficiency; coalitions; NOMA; IoT 1  
[PERSON]DRESS] or [EMAIL_ADDRESS]
## 5 Implementation
### Accuracy Verifying Environmental Quality Indexes through Decision Tree  
In the third scenario, we verified the accuracy of the environmental quality indexes using decision tree analysis. The dataset was divided into two variables (\"Q1-Q7\" and \"Class ID\") to split the data for training and testing. Then, we built a decision tree model using Scikit-learn. The decision trees show the sorting method of the class ID in the internal nodes. The sorting methods for the decision trees represent the most ideal path to classify each class ID. Decision tree analysis is a method for checking classification accuracy. In Figures 9 and 10, the starting root nodes are Q5, \"How do the buildings look at your position?\", which shows that Q5 is an index for the determination of this classification. Two criteria to carry out classification in Scikit-learn are Gini and entropy. Figure 9 is a visualization of a decision tree with the Gini criterion for classification, and the highest classification rate is 72.73%, which is quite accurate. Figure 10 is a visualization of a decision tree with the entropy classification criterion, for which the classification rate is 61.36%, which is still high. The method of the data splitter in both trees is \"best\". Another splitter is \"random\". Since the quantity of the dataset was less than 1000 until we started the algorithm, the random splitter was not selected in this experiment. Since the sampling data were random, the classifying rates were not all the same. We trained the decision tree several times, as shown in Table 6, and all of the classification rates are higher than 60%, with the highest value obtained being 72.73% and the highest entropy rate being 68.82% for the Gini index. Training the decision tree verified the hypothesis that how citizens feel about the urban environment is recognizable and extractable by this system.  
Figure 8: Data classification according to the environmental quality indexes determined according to PCA: (**a**) explained variance ratio of each principal component. This statistical chart presents the proportions of principal component PC1 and PC2 to certify that they are effective. If the proportion of the sum of PC1 and PC2 is lower than 0.3 (30%), then the principal component cannot certify that the classification is accurate; (**b**) decision-distribution map of component scatter plot. This scatter plot presents the check-in marks distributed according to the four class IDs on the 2D vector graphs constituted by PC1 and PC2. If the classification is accurate, then the scatter points should come together under the same class ID. This plot presents the classification output of at least three effective blocks on the PC1-PC2 graphics.