esa-sceva/satcom-chunk-collection huggingface dataset:
# Triple-Attention Mixed-Link Network for Single-Image Super-Resolution
## 5 Discussion  
Many recent approaches have proposed very deep networks for handling the single-image super-resolution task. However, these very large models have relatively low practical value since most personal computers and smart terminals do not have enough GPU memory for these models. Even though stacking very deep networks could bring performance gain, from a practical point of view improving model efficiency is a more important research point. Compared with the previous methods, we propose an efficient network topology fusion, which enables the model to absorb the advantages of ResNet [18] and DenseNet [19]. This enhances the flow of gradient and information in the network. Compared to the asynchronous fusion in RDN [7] and MemNet [8], our proposed model combines the structure of ResNet and DenseNet synchronously at each layer. This enable our TAN to reduce the growth rate of parameters by 50%. The previous methods did not make full use of the attention mechanism, and our TAN proposed three kinds of different attention structures, which enable the network to self-learn the feature selection and fusion among channels, convolution kernels, and spatial pixels. Under these attention mechanisms, our model has higher performance with fewer parameters. Although the model we proposed has many improvements over the previous method, there are still some limitations. Compared with the adversarial training methods [29; 30], although current TAN can achieve higher peak signal-to-noise ratio and structural similarity, the visual quality is slightly smoothed, and not as sharp as the Generative Adversarial Networks (GAN) [31] results. This is also a trade-off in visual effects and reconstruction accuracy. However, our proposed network framework can be easily ported to the GAN generator. In addition, our proposed TAN is a data-driven supervisory training method. Although acceptable reconstruction effects can still be achieved for a new task, if a better performance is required, new datasets need to be collected for fine-tuning and migration.
## 4 Sensing-Oriented Issues and Solutions
### Defect Types and Antenna Topologies
#### 4.1.2 Crack Detection and Characterization  
Despite the fact that engineering components and structures are carefully designed against fatigue failures, more than 50% of mechanical failures are due to the formation of fatigue cracks. The severity of the failure depends on both the crack length and orientation with respect to the loading direction. Transverse cracks are the most common and dangerous cracks because they can reduce a structure's cross section and therefore lower its structural capacity/integrity. The traditional crack sensing techniques make use of lead wiring for data extraction, the placement and maintenance of large lengths of which is cumbersome and expensive [35]. The development of crack detection and characterization based on passive antenna sensors are summarized in Table 2.  
The detection of cracks using coil antenna was studied in early 2003 [108]. Benefiting from low profile and low cost, patch antennas are frequently used for crack sensing. From cavity theory, the sensitive part of such type of antenna can cover its underneath area. Based on the current techniques, most works are focusing on detection of both crack length and orientation, where dual-mode [33] or 2D grid [32; 35] was utilized to complete this task.  
With a spatial division using multi-patch, a multiplexing antenna sensor was designed to detect a multi-site crack [34]. However, this sensor system is incompatible with the Gen2 standard. It is worthy to mention that the backscattered phase can function as a sensing variable and a sub-mm resolution was achieved in crack width detection using mutual-coupling between two patch antennas [37; 100]. The response of backscattered phase is dependent on the wireless channel, making it limited in the in-site monitoring.
## 2 Materials and Methods
### Preprocessing
More precisely,  
\[r=\#samples=\frac{length(signal)}{l}-N+1 \tag{1}\]  
\[c=\#features=34\cdot N \tag{2}\]  
The proposed approach allows the processing of signal information of relatively longer lengths, for instance 30 min, while avoiding an increase in the time required to compute the features within windows of such lengths. Moreover, this process also permits each sample to include information from the past. To compare the computing time required for features extraction between the previous and the present approach, a plant electrophysiological recording of 5-days length stored at 500 Hz was taken and processed by both methodologies. To approximate the new methodology to the previous one, it was assumed that \(N=7\) windows of length \(l=5\) min. The number of windows \(N\), and their length \(l\), are the two main parameters varied in the proposed method in order to optimize the outcome of the classification model. The tested values for both parameters \(N\) and \(l\) were chosen to provide an approach that would be computationally efficient and, at same time, would not require long recording durations. Hence, relatively small window lengths \(l\) were chosen, namely: 30 s, 1 min, 2 min and 5 min, which were tested for three \(N\) values: 5, 10, and 15, respectively. Such design allows the approach to be easily deployed in commercial agricultural practice. Additionally, since the number of samples varies for different window lengths when the length of recordings is fixed, to perform a proper comparison between the window lengths, the same number of samples corresponding to \(r\) (Equation (1)) for 15 windows with length of 5 min was chosen for each case i.e., 274 samples for 24 h of recordings. For the window lengths shorter than 5 min, the 274 samples were randomly selected among all of the samples corresponding to a recording of 24 h.  
Once the optimal window length was chosen, additional tests were performed to investigate the effect of number of windows \(N\) while increasing it from 1 to 30. To equalize the number of samples per plant, for each of these tests the number of rows r of the matrix data corresponded to \(N=30\). _Labelling._ The extracted samples of the signal represent either the normal, pre-stimulus, state or the stressed state of the plant. Accordingly, the samples were labelled as '0' when the corresponding segment finished in the normal state, whereas label '1' was assigned to the samples in the stressed state. The distribution between the labels was equal in each performed test. _Normalization._ To compensate for the eventual variation in the electrical response between the plants, a min-max normalization was performed to each feature vector of each plant before concatenating the corresponding matrices. This normalization brought the feature values to the interval [0, 1].
## 5 Introducing the Multi-Linear Model to Advanced Control System Design
### Gain Scheduling Proportional Integral Derivative Control  
The multi-linear model is of high accuracy over the full operating range, thus the parameters of the proportional integral derivative (PID) controller can be tuned for each local linear model to reduce the influence of process nonlinearity. Furthermore, the parameters of the PID can be dynamically adjusted according to the change of the CO\({}_{2}\) capture rate, which results in the gain scheduling PID control. The gain scheduling law is generally designed using fuzzy logic [51], which can have a similar expression to the scheduling function of the multi-linear model. The gain scheduling PID can be conveniently applied to the real process without too much modification to the original PID control algorithm, and thus it can achieve a more robust control performance than nonlinear controllers. The control scheme of a gain scheduling PID is presented in Figure 16.  
Figure 16: Control structure of gain scheduling control. PID: proportional integral derivative.  
Figure 15: Control structure of multi-model predictive control (MMPC); linear model predictive control (LMPC). (**a**) Controller weighting method; (**b**) Model weighting method.
## 3 Results and Discussion  
### Ligand Modeling  
Drugs, through structural modification of a parent compound, aim to develop new derivatives with better activity and reduced side effects. Two primary strategies are commonly used in drug discovery: ligand-based drug design (LBDD) and structure-based drug design (SBDD) [52]. LBDD uses information from known active small molecules by analyzing their molecular structure and biological activity data [53,54]. In contrast, SBDD focuses on the three-dimensional structure of the target protein, using techniques such as X-ray crystallography or homology-based molecular modeling to identify active binding sites and guide the design of molecules that can interact specifically with the target [55,56].  
In this study, novel pyrazoline benzenesulfonamide derivatives (PBD) were designed by substituting the hydroxy group at the benzene ring A of the Modifina structure with various substituents. These included tertiary aliphatic amine (N,N-dimethylethanamine and triethylamine), aliphatic heterocyclic (piperidine, piperazine, pyrrolidine, tetrahydrofuran, and 1,3-dioxolane), aromatic heterocyclic (pyridine, pyrrole,furan, thiophene, and imidazole), and benzene. The substituents were added at positions 2, 3, or 4. The goal of these substitution was to increase the lipophilicity of the PBD compounds, thereby improving gastrointestinal absorption and anticancer activity. Based on this modification, a total of forty-five PBD compounds were selected for virtual screening against ER\(\alpha\) (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound** & **Position of** & **R** & **Compound** & **Position of** & **R** \\ \hline PBD-1 & 2 & & & PBD-24 & 4 \\ \hline PBD-2 & 3 & & & PBD-25 & 2 \\ \hline PBD-3 & 4 & & & PBD-26 & 3 \\ \hline PBD-4 & 2 & & & PBD-27 & 4 \\ \hline PBD-5 & 3 & & & PBD-28 & 2 \\ \hline PBD-6 & 4 & & & PBD-29 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure of designed novel pyrazoline benzenesulfonamide derivatives.
# Pansharpening by Complementing Compressed Sensing with Spectral Correction
## 5 Conclusions  
In this paper, we proposed a method for pansharpening based on CS theory. In the proposed method, the intensity obtained from the component substitution method and the intensity obtained via the method based on CS theory are fused to reproduce the intensity close to the original. We introduced SCMP as the intensity substitution method and used the tradeoff process for image fusion. Experimental results showed that the proposed method outperformed existing methods in terms of numerical and visual evaluation. The proposed method was also effective for satellites with panchromatic sensors (observed areas are visible and NIR regions) and multispectral sensors (observed areas are red, blue, green, and NIR bands) like IKONOS.  
Generally, the intensity image generated by a CS-based method is blurrier than the intensity image generated by the component substitution method, because component substitution captures the intensity of the PAN image. On the other hand, it is expected that the spectral distortion of the intensity image generated by the CS-based method will be lower than that of the image generated by the component substitution method. Since complete restoration is not guaranteed, in order to get the image close to a complete reproduction, back-projection methods can be used. However, they may cause ringing artifacts. Based on these considerations, our proposed method combines the intensities generated by the CS-based method and the component-substitution-based method via the tradeoff process instead of the back-projection to achieve both an improvement of spatial resolution and a reduction of spectral distortion. Experimental results show that the tradeoff process was more effective than the back-projection in generating a pansharpened image of which the spatial resolution was equivalent to that of the PAN image and reducing spectral distortion. Improvement of the accuracy by parameter tuning is important future work.  
Methodology, N.T. and Y.S.; Software, N.T.; Validation, Y.S.; Writing--Original Draft Preparation, N.T.; Writing--Review and Editing, Y.S. and S.O.; Supervision, S.O.; Project Administration, S.O.; Funding Acquisition, S.O. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the JSPS KAKENHI Grant Number 18K19772 and the Yotta Informatics Project by MEXT, Japan.  
The authors thank the Japan Space Imaging Corporation and Space Imaging, LLC for providing the images.  
The authors declare no conflict of interest.
# Sigma Routing Metric for RPL Protocol
## 7 Discussion  
LLN networks have a big growth and applicability potential in IoT; this explains why they have attracted the attention of many manufacturers to invest in different technologies that facilitate the interaction between different devices (nodes) at the data link level. These technologies take maximum advantage of IPv6 addresses (which are a plus for LLN), each node having its own IP address. This means LLNs will be exponentially scalable, and the RPL protocol will be a central piece in their development.  
One of the challenges of RPL in LLN is unquestionably security. Many of the vulnerabilities of sensor networks will no doubt make the transition to IoT, particularly with RPL attacks like Sinkhole, HELLO Flood, Wormhole and Clone ID, which occur using intruder nodes. In any case, care must be taken, as many IoT devices (nodes) may already have operating systems that are vulnerable to other types of attacks, such as Androids.  
On the other hand, many manufacturers (Cisco, Microchip, Atmel, among others) have implemented and tested RPL in their products, obtaining good results. They have made RPL available to the public, which is why it is much easier to perform tests in real IoT scenarios with the RPL protocol. Additionally, some of these manufacturers have taken the risk and used ContikiOS as the operating system for their products (devices), including thus the RPL protocol, considering there is also an implementation of RPL for other open source operating systems (e.g, Linux).  
However, one must admit that the RPL implementation in the Contiki operating system has some room for improvement. The implementation has some issues and is not complete according to  
Figure 16: Network lifetime.  
the description found in the RFC 6550, which is why we believe there are still many things to improve and test to bring RPL in the IoT to its full potential.
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 3 Interpretation of Mining-Induced Microseismicity  
### Microsesismic Focal Mechanism  
The microseismic focal mechanism is correlated to the fracturing behavior of rocks, which can be used for the inversion of the rock fracture type [67]. The ratio of the S-wave energy component \(E_{S}\) to the P-wave energy component \(E_{P}\left(E_{s}/E_{p}\right)\) is found to be an indicator for characterizing the focal mechanism of microseismicities [68]. It is generally accepted that if \(E_{s}/E_{p}>10\), the tensile component is dominant in the rock-fracturing process [69; 70]. If \(E_{s}/E_{p}<20\) to \(30\), the shear component dominates the rock-fracturing process [71]. [PERSON] et al. [72] studied the focal mechanism of mining-induced seismicies near an intrusive dyke via analysis of the \(E_{s}/E_{p}\) ratios (Figure 4), and the large-magnitude microseismic events characterized by high \(E_{s}/E_{p}\) ratios revealed the shear slip rock fracture process near the geological discontinuity in an Australia coal mine. [PERSON] et al. [73] employed \(E_{s}/E_{p}\) ratios to characterize the failure of a grouted floor and found that the grouting slurry significantly influences the failure mechanism of the grouted floor and the proportion of tensile fracture decreases with the tensile strength enhancement by grouted cement. [PERSON] et al. [74] found that the focal mechanisms of 26 tremors induced by destress blasting in a Polish coal mine differ from that of tremors induced by coal mining, and the former exhibits a decreased \(E_{S}/E_{p}\) ratio. In 1970, [PERSON] first proposed the concept of a moment tensor to study the earthquake mechanism and total seismic moment based on the normal mode theory [75]. Since then, many researchers have used different moment tensor inversion methods to understand the focal mechanisms of seismicities [67]. The moment tensor inversion has been extensively employed in Chinese coal mines to analyze the fracture behavior of coal/rock masses.
## 3 Results and Discussion
### Isotherms of Water vapour sorption of Fruit Fibres and Pomace
The highest capacity of the monolayer was shown by chokeberry pomace, which had 0.072 g water/g d.s. A lower water content was found in apple pomace (0.065 g water/g d.m.) and currant pomace (0.059 g water/g d.m.). An evaluation of the suitability of the equations to describe the obtained sorption isotherms was carried out using parameters such as the coefficient of determination (\(R^{2}\)) and the mean deviation square (\(RMS\)). The values of the coefficient of determination (0.976-0.983) and the mean square deviation not exceeding 10% testify to the good agreement of the model fit to the experimental data (Table 7). A study by [PERSON] (1977) showed that the structure and composition of the chemical composition affected the sorption properties. The material studied was pomace from black and red currant, chokeberry, gooseberry and rowan. Chokeberry pomace, which contained the most carbohydrates, showed the best hygroscopic abilities. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Fibre**} \\ \cline{2-4}  & **Chokeberry** & **Apple** & **Currant** \\ \hline \multirow{3}{*}{**Equation coefficient**} & \(u_{m}=0.048\) & \(u_{m}=0.056\) & \(u_{m}=0.043\) \\ \cline{2-4}  & \(C=55.960\) & \(C=8.047\) & \(C=18.816\) \\ \cline{2-4}  & \(k=0.783\) & \(k=0.804\) & \(k=0.756\) \\ \hline \(R^{2}\) & 0.995 & 0.990 & 0.989 \\ \hline \multicolumn{2}{c}{MRE(\%)} & 3.091 & 4.534 & 3.259 \\ \hline \multicolumn{2}{c}{RMS} & 3.739 & 9.163 & 4.842 \\ \hline \multicolumn{2}{c}{RSS} & 0.013 & 0.105 & 0.036 \\ \hline \multicolumn{2}{c}{_SEE_} & 0.112 & 0.324 & 0.190 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fitting parameters of the GAB model of the water vapour sorption isotherms of the studied fibres and pomace. Figure 5: Water vapour sorption isotherms of the studied fruit fibre and pomace.
## 3 Fast Handover Algorithm
### Process of the Fast Handover Algorithm
#### 3.4.2 Location Recognition of UE  
In the preliminary study, a UE's positioning algorithm, which can obtain the real-time location information in the case that the GPS and BDS signals are missing, was proposed based on a fuzzy clustering fingerprint database [30]. Let (\(\theta_{c}\), \(\varphi_{c}\)) be the coordinates of UE's estimated location, and (\(\theta_{z}\), \(\varphi_{z}\)) be the coordinates of NCell, and the distance between UE and NCell is  
\[\begin{array}{l}\Delta D\ =\ R\times\arccos(\sin\varphi_{c}\sin\varphi_{z}+\\ \cos\varphi_{c}\cos\varphi_{z}\cos(\theta_{c}-\theta_{z}))\times\pi/180^{\circ} \end{array} \tag{12}\]  
If the distance \(\Delta D\) between the UE and the NCell is not less than \(D_{\text{thr}}\), that is to say, Equation (13) is satisfied, then the location information of UE is continuously updated; if \(\Delta D\) is less than \(D_{\text{thr}}\), that is, Equation (14) is satisfied, then the weight of the handover is calculated.  
\[\Delta D\geq D_{\text{thr}} \tag{13}\]  
\[\Delta D<D_{\text{thr}} \tag{14}\]As shown in Figure 11, suppose the positions of two eNodeBs are \(A\) and \(B\), namely, the distance \(d_{1}=AB\) between eNodeBs, and the distance threshold \(D_{\text{thr}}=d/2\) between UE and the NCell. \(AA^{\prime}\) is the vertical distance between gNodeB and rail, and \(AA^{\prime}=BB^{\prime}\). \(U\) and \(U^{\prime}\) are the locations of UE respectively. \(BU\) and \(BU^{\prime}\) are the distance \(\Delta D\) from UE to NCell. \(C\) is the intersection point of \(AB\) and the rail.
## 4 Modeling and Implementations of Software-Defined Vehicular Networks for V2X
### Modeling and Implementations of SDN for Vehicular Networks
After system analysis, the proposed solution provides a model that includes the integration of SDN controller, eNB infrastructures, RSU controller and 5G to design 5G based SDN concept. The full transformation of VANET into SDVN requires to model SDVN solutions not only based on system architectures of SDVN but also based on mathematical analysis. Since the SDVN integrates the use of the SDN concept on the VANETS, a mathematically-based model is the natural modeling language to break up complexity problems and make VANETs' and SDVNs' challenges tractable. Mathematical-based theory applied to SDVN should bring further improvements and variations for allowing SDN to fully enhance VANETs, consequently, minimizing latency and cost, safety message delivery using heterogeneous communication interfaces [41]. A thorough mathematical model theory for all the above-analyzed articles that would lead to a new proposed concept that along with its implementation shall be addressed in future work.
## 4 Applications of the Transient Regime
### Analysis of Products Using Transient Cuffp Signals
#### 4.2.3 Paramagnetic Complexes  
CWFP sequences have also been used in the study of paramagnetic ions complexes. T\({}_{1}\) and T\({}_{2}\) relaxation profiles were used to study Ethylenediamine tetraacetic acid-paramagnetic (Cu\({}^{2+}\), Fe\({}^{3+}\), and Mn\({}^{2+}\)) ion complexes as function of pH. Figure 13 shows the T\({}_{1}\) (stars) and T\({}_{2}\) (open circle) profiles of the EDTA-Cu\({}^{2+}\) complex measured with CWFP and CP-CWFP sequences (Figure 13a), and with IR and CPMG (Figure 13b). These results show the similarity between the relaxation profiles measured by classical and CWFP methods. The dotted line in Figure 13a is the relaxation profile of T\({}_{1}\)\(\approx\) T\({}_{2}\) for the Cu\({}^{2+}\) solution without EDTA. This relaxation profile is completely different from complex relaxation profiles.  
Figure 13: (**a**) Variation in maximum absorbance at 730 nm (\(\blacksquare\)), T\({}_{1}\) (\(\ast\)) and T\({}_{2}\) (\(\odot\)) measured simultaneously with CP-CWFP pulse sequence for the aqueous solution of complex Cu(II)-Ethylenediamine tetraacetic acid and T\({}_{2}\) measured with CPMG (\(\square\)) to the aqueous solution of Cu\({}^{2+}\), in function of pH. In all experiments the Cu\({}^{2+}\) concentration was 3.3 \(\times\) 10\({}^{-3}\) mol/L. The solid line for EDTA complexes is the sixth-order polynomial fitting of the data. The dashed line for Cu\({}^{2+}\) solution is a sigmoidal fit. (**a**) Variation in T\({}_{1}\) (\(\ast\)) and T\({}_{2}\) (\(\odot\)) of the same solution in (**a**), obtained with standard inversion-recovery (IR) and Carr–Purcell–Meiboom–Gill (CPMG) pulse sequences, respectively. Adapted from Publication [46], Copyright (2015), with permission from Elsevier.  
Figure 13a demonstrates that the relaxation profiles show similar variations in the absorbance measured in 730 nm. The relaxation profiles can also be measured in a colorless complex, such as EDTA-Mn\({}^{2+}\), which is not detected by spectrophotometry [46]. Spectrophotometry is a standard method used to study this complex. The CP-CWFPx-x sequence has been used in the complexation of biopolymers chitosan (CS) with paramagnetic ions (Cu\({}^{2+}\), Fe\({}^{3}\), and Mn\({}^{2+}\)) as a function of pH [46]. The results indicated that CS strongly interacts with these ions and the complex CS-ion can be easily removed from the solution by centrifugation.