esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 2 Methods
### TransUnet  
TransUnet is a network proposed by [PERSON] et al., in February 2021 [35]; unlike the SwinUnet network structure based entirely on the transformer, the encoder in the TransUnet network does not use a pure transformer but instead uses a hybrid CNN-transformer model. The network structure is shown in Figure 7. TransUnet is a network proposed by [PERSON] et al., in February 2021 [35]; unlike the SwinUnet network structure based entirely on the transformer, the encoder in the TransUnet network does not use a pure transformer but instead uses a hybrid CNN-transformer model. The network structure is shown in Figure 7. TransUnet first uses CNN as a feature extractor to generate the input feature maps. This is when the patch-embedding module extracts 1 \(\times\) 1 patches from the feature map generated by the CNN, rather than from the original map. The transformer encoder in the TransUnet network consists of the L-layer MSA and MLP modules (as shown in Figure 8), so the output of the \(l\)th layer can be expressed as follows:  
\[z^{\prime}_{\ell}=MSA(LN(z_{\ell-1}))+z_{\ell-1} \tag{12}\]  
\[z^{\ell}=MLP\big{(}LN\big{(}z^{\prime}_{\ell}\big{)}\big{)}+z^{\prime}_{\ell} \tag{13}\]  
where \(LN(\cdot)\) denotes the layer normalization operator and \(z^{\ell}\) indicates the encoded image.  
Figure 6: Swin transformer block.  
By combining the CNN and transformer into the encoder, TransUnet not only avoids the limitations of the CNN method in acquiring remote relational features, thanks to convolutional operations, but also avoids the problem of coarse segmentation results, thanks to the transformer's excessive focus on modeling between global contexts.
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 3 Results  
Our proposed method was tested on a Sentinel-1 image with a flood event and a GF-3 image for the river extraction. The results were compared to other pixel-based and superpixel-based methods such as RF and XGBoost [40].
## 4 Results
### Cluster Analysis
#### 4.2.2 Central Ionian Islands Area  
The two main shocks of sequence \(I1\) (Figure 6b) with \(M=6.1\) and \(M=6.0\) occupy the southern and the central part of the onshore area of Kefalonia Island. The 2014 Kefalonia earthquake sequence (\(I1\) Figure 6) started on 19 January with the first main shock occurring on 26 January (\(M=6.1\)), and aftershock activity extending over 35 km [58], part of which hosted the second main shock (\(M=6.0\)) that occurred on 3 February and the compound aftershock activity. A sub-cluster is also detected offshore to the southwest of Kefalonia Island (\(I2\) in Figure 6b) that is deployed concurrently with the main sequence (Figure S8). In addition, two distinct clusters, \(I3\) and \(I4\) (Figure 6b), are revealed, which occurred between November and December 2014 (Figure S9), across the edges of the double rupture. They might be triggered by the stress transfer of the main ruptures, indicating activation of adjacent fault segments. The seismic activity of cluster \(I5\) (Figure 6b) retains the most interest because it is essentially two seismic excitations evolving at the same time. The first initiated in the Myrtos Gulf and the second offshore the south part of Kefalonia Island. It comprises 164 earthquakes in about 100 days (Figure S9). The activity of the \(I7\) cluster (Figures 6b and S10) spreads along the western coastline of Lefkada and Kefalonia Islands, far beyond both sides of the 2015 Lefkada main rupture. To the south the aftershock activity is sparse, probably due to the large amount of stress released in the main rupture, revealing that the main slip is associated with a fault of about 17 km in length [59]. Apart from cluster \(I4\), two additional clusters (\(I6\) and \(I9\) in Figure S9 and Figure S11, respectively) are detected in the area between Lefkada and Kefalonia, extending to about 15 km, which is considered as a transition zone encompassing step-over structures [58]. All of them relate to the E-W-oriented, parallel step-over faults, similar to the ones detected in the microseismicity cluster analysis between September 2016 and December 2019 in the study area [34].  
Figure 5: (a) Spatial distribution of the centroids of the identified clusters for the eastern subarea of the Corinth Gulf along with the major faults (yellow lines). The size of the circles is proportional to the earthquake number in each cluster whereas the duration is represented by the color scale. (b) Spatial distribution of the clusters with \(N\geq 30\) events. The index of each cluster is provided in the inset box.
# A New Calibration Method for Broadband Complex Resistivity Measurement System of Rocks and Ores
## 2 Measurement System
### System Overview
The system transmits data and commands to an embedded control board via a serial peripheral interface (SPI) and supports visualization and remote communication through a mobile application.
## 9 Conclusions  
In this paper the splitting iterative schemes were reformulated in terms of descent and residual vectors. In the new formulation for the new splitting iterative scheme (NSIS) the extrapolation technique becomes easy to follow. The NSIS can be obtained from the original splitting iterative scheme by either multiplying the descent vector by a parameter \(\alpha\), or multiplying the right-hand side of the governing equation for the descent vector by a parameter \(\alpha\). Different splitting iterative schemes can be unified to have the same iterative form, but they are different in the governing equations for the descent vector.  
We proved that the spectral radius of the NSIS is smaller than the original one if \(\alpha>1\). In addition to a constant value of \(\alpha\), varying values of \(\alpha\) were examined by preserving the orthogonality and maximizing the residual vectors' decreasing norm. A varying orthogonalization factor was introduced in the second NSIS to enhance the stability, and the property of absolute convergence was proven.  
Main points for the novel contributions are summarized as follows.  
* The splitting iterative schemes were unified in terms of descent and residual vectors.
* An extrapolation parameter \(\alpha>1\) in the splitting iterative scheme can improve the convergence speed.
* The NSISs were developed to maximally decrease the residual and to preserve the orthogonal property.
* The second method by adding a stepwise varying factor \(\eta_{k}\) can stabilize the splitting iterative scheme, even the original one is unstable.
* The acceleration parameter \(r\) can be obtained readily by a maximization technique.
* The improvement of convergence speed was observed by adopting the proposed NSIS together with the optimization technique for determining the optima value of parameter.  
**Author Contributions:** Methodology, C.-S.L.; software, C.-S.L.; validation, C.-S.L.; formal analysis, C.-S.L.; writing--original draft preparation, C.-S.L.; writing--review and editing, B.L.; funding acquisition, C.-S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Taiwan's National Science and Technology Council project NSTC 113-2221-E-019-043-MY3 granted to the first author is highly appreciated.  
**Data Availability Statement:** All data that support the findings of this study are included within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(n\) & **20** & **40** & **60** & **80** & **100** \\ \hline \(w\) & 0.156725 & 0.111880 & 0.082611 & 0.073455 & 0.063021 \\ NI & 76 & 248 & 299 & 298 & 294 \\ ME & \(1.31\times 10^{-1}\) & \(1.11\times 10^{-1}\) & \(9.59\times 10^{-2}\) & \(9.78\times 10^{-2}\) & \(1.00\times 10^{-1}\) \\ RMSE & \(5.91\times 10^{-2}\) & \(4.43\times 10^{-2}\) & \(3.42\times 10^{-2}\) & \(3.57\times 10^{-2}\) & \(3.79\times 10^{-2}\) \\ \hline \end{tabular}
\end{table}
Table 20: Example 5, NI, ME and RMSE obtained by the SOR with different \(n\).
## 5 Experimental Results and Evaluation
### Evaluation Indicators  
What is sought in image coloring is not a fixed color, but as realistic a color as possible, because, in reality, objects will inherently have multiple colors, and it is reasonable for a car to be red or black, as long as it looks realistic. Therefore, the evaluation of image coloring, in general, is subjective, and there is no uniform quantitative standard to evaluate colorized images [17]. In this paper, two metrics were selected to quantitatively analyze the experiment; peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are commonly used image evaluation metrics.  
(1) PSNR  
PSNR is based on the error between pixel points in dB, and the value of PSNR is negatively correlated with the distortion of the image. Its calculation expression is shown in Equations (5) and (6) and represents the mean square error between two images.  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\|I(i,j)-k(i,j)\|^{2} \tag{5}\]  
\[PSNR=10\log_{10}\left(\frac{MAX_{L}^{2}}{MSE}\right)=20\log_{10}\left(\frac{ MAX_{I}}{MSE}\right) \tag{6}\]  
where \(m\) is the total number of pixels of image \(I\); \(n\) is the total number of pixels of image \(K\); and \(I\) (\(i,j\)) is the pixel value of image \(I\) at \((i,j)\). \(k\) (\(i,j\)) is the pixel value of image \(K\) at \((i,j)\); \(MAX_{I}\) is the maximum value of the image \(I\) color.  
(2) SSIM  
SSIM evaluates the similarity of two images in terms of brightness, contrast, and structure. The formula for calculating structural similarity is shown in Equation (7).  
\[\mu_{x}=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}X(i,j) \tag{7}\]  
\[\sigma_{x}=\left(\frac{1}{H\times W-1}\sum_{i=1}^{H}\sum_{j=1}^{W}\left(X(i,j) -{\mu_{x}}^{2}\right)^{\frac{1}{2}}\right) \tag{8}\]  
\[\sigma_{XY}=\frac{1}{H\times W-1}\sum_{iSSIM is evaluated in three aspects.  
\[l(X,Y)=\frac{2\mu_{X}\mu_{Y}+C_{1}}{\mu_{X}^{2}+\mu_{Y}^{2}+C_{1}} \tag{10}\]  
\[c(X,Y)=\frac{2\sigma_{X}\sigma_{Y}+C_{2}}{\sigma_{X}^{2}+\sigma_{Y}^{2}+C_{2}} \tag{11}\]  
\[s(X,Y)=\frac{\sigma_{XY}+C_{3}}{\sigma_{X}\sigma_{Y}+C_{3}} \tag{12}\]  
\[SSIM(X,Y)=l(X,Y)*c(X,Y)*s(X,Y) \tag{13}\]  
where \(\mu_{x}\) is the pixel mean of image \(X\), and \(\sigma_{x}\) is the variance of image \(X\).
## 4 Tests and Results  
The proposed procedure is implemented in MatLAB2015b(r) environment, considering 96 time steps of 15-minutes duration within each day. In particular, the optimization tool _intlinprog_ is exploited to solve the proposed iterative MILP. Simulations are carried out on a workstation HP Z440 equipped with an Intel Xeon 3.50 GHz processor with 16 GB RAM.  
The MILP problem-solving starts from the solution of the linear relaxed problem (without integer constraints on variables) according to the interior-point method. Then, a cut generation is used to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device** & **Installed Size** & **Installed Size** & **State of Charge** & **Converter Size** \\  & **(kW)** & **(kWh)** & **Max/Min (p.u.)** & **(kW)** \\ \hline Photovoltaic (PV) & 40 & — & — & 40 \\ Energy storage system (ESS) & 30 & 60 & 0.95/0.25 & 30 \\ Charging station (CS) & — & — & — & 10 \\ Utility grid & — & — & — & 50 \\ Electric vehicle (EV) & — & 24 & 1.0/0.2 & — \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test system. Sizes of installed devices.  
Figure 2: Grid scheme of the test direct-current (DC) microgrid.  
restrict the linear solution, and finally, the branch-and-bound technique is adopted to generate two subproblems using proper heuristics and to evaluate the most suitable solution according to the best projection on improving lower or upper bounds [53]. As for the settings, the relative tolerances of the integer and non-integer variables are set to 0.0001 and 0.001, respectively.  
Regarding non-linear problems, classical solution techniques are highly dependent on initial condition, since local solutions can be found instead of global ones. To overcome this issue, a metaheuristic approach, such as the genetic algorithm, can be exploited to generate and compare multiple solutions. In the following, the reference non-linear problem is, therefore, solved by means of the optimization tool _ga_ in MatLAB framework.  
The solution starts from the initial population in the first run of the linearized problem and further feasible populations up to 100 individuals are created in the feasible space. The fitness of individuals is, therefore, evaluated according to the Augmented Lagrangian function to account for non-linear constraints [54], and exploiting square root of rank as a fitness measure. Therefore, the next generation is given for 80% by crossover, where the selection of parents is based on Roulette heuristic, whereas 20% of individuals comes from mutation. Each process is governed by a Gaussian distribution with feasibility region adaption. The feasibility tolerance with respect to non-linear constraints is set to 0.001, and the maximum objective function tolerance is set to 0.001.
# A Hierarchical Coordinative Control Strategy for Solid State Transformer Based DC Microgrids
## 3 Hierarchical Coordinative Control Strategy Design
### Comparative Analysis of Microgrid Power Fluctuation Response of the DC Link  
From Figures 4, 5 and 9, the voltage response functions of the SST DC links to the CLLC load current \(i_{L}\) under the independent control can be given in Equation (12), where \(L_{0}\) is equal to \(L_{s}+\frac{L_{0}}{2}\) and is the equivalent inductance on the AC side of the MMC.  
\[\left\{\begin{array}{c}\frac{U_{tot}}{I_{L}}=\frac{1}{bC_{uu}-N_{CL}s}\\ \frac{U_{lk}}{I_{L}}=-\frac{bC_{uu}}{n(bC_{uu}-N_{CL}s)(sC_{Hc}+\frac{3L_{0}C _{ubL}C_{dd}}{2U_{dc}})}.\end{array}\right. \tag{12}\]  
Since the independent control is only for three-stage SST converters, the proposed microgrid layer control is not considered in the comparative analysis. From Figure 9, the corresponding disturbance response functions under the SST-layer coordinative control can be expressed as:  
\[\left\{\begin{array}{c}\frac{U_{tot}}{I_{L}}=\frac{1-k_{1}}{bC_{uu}-N_{CL}s }\\ \frac{U_{lk}}{I_{L}}=-\frac{(1-k_{1})(k_{2}C_{uu}-N_{CL}s)}{n(bC_{uu}-N_{CL}s) (sC_{Hc}+\frac{3L_{0}C_{dd}}{2U_{dc}})}\end{array}\right. \tag{13}\]  
As can be seen from Equations (12) and (13), the additional control branches change the numerator of the disturbance response functions. If the value of the control parameter \(k_{1}\) is close to 1, the dynamic voltage fluctuation problem of HVDC and LVDC buses caused by microgrid output power swing can be completely solved in theory. The coefficient \(k_{2}\) aims to improve the dynamic power tracking speed difference between the MMC and the CLLC, so as to realize the joint regulation of these two stages in the dynamic process. Although this effect is not clearly reflected in the above formulas, it can be seen from the disturbance transfer function of HVDC bus voltage to the CLLC input current \(i_{1}\) before and after the introduction of the \(k_{2}\) branch, as shown in Equation (14). The introduction of \(k_{2}\) improves the tracking ability of the MMC to current \(i_{1}\), and the whole coordinative control branch (2) does not need additional sensors.  
\[\left\{\begin{array}{c}\frac{U_{lk}}{I_{1}}=-\frac{1}{sC_{Hc}+\frac{3L_{0}C _{dd}}{2U_{dc}}}\\ \frac{U_{dc}^{\prime}}{I_{1}}=-\frac{1-k_{2}}{sC_{Hc}+\frac{3L_{0}C_{dd}}{2U_{ dc}}}\end{array}\right. \tag{14}\]  
The system simulation parameters are shown in Table 2. According to Equations (12) and (13), the Bode diagrams of the disturbance response functions can be obtained, as shown in Figure 10.  
From this figure, it can be seen that the coordinative control strategy proposed in this paper can significantly improve the low-frequency amplitude gain, indicating that this coordinative strategy has a better suppression for the DC bus voltage fluctuating during a same dynamic process. The Bode diagram of Equation (14) is similar to that of Equation (12), since the coefficient \(k_{2}\) acts on the numerator, and so it is not repeated here.
# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
## 4 Experimental Evaluation
### Experimental Case 3: Address Matching Using Machine Learning
Among the three kinds of training data, Training data 3, which shows the lowest accuracy of machine learning models, was used to test different numbers of similarity metrics and select the best machine learning model. We chose 97% as the minimum accuracy that all the three machine learning models need to exceed, which is 1% higher than the highest accuracy of similarity metrics in Training data 3. We determined different kinds of similarity metrics tested by descending order of their accuracy. As shown in Figure 10, all the trained models exceed 97% using nine similarity metrics (Jaro-Winkler Sorted, Tversky, Jaccard, Jaro, Jaro-Winkler Reversed, Hamming, Cosine, Sorensen_Dice, Jaro-Winkler). Among them, XGB shows the highest accuracy (98.29%) and is the best model with nine similarity metrics for the evaluation of geocoding. With the trained XGB model, as a result, 96.39% of input addresses are correctly matched through the address matching process using machine learning (Table 6) in Input data 3. Since some input addresses include random spelling errors and mistakes that do not abide by the systematic rules, the accuracy becomes lower than the results in Table 5. Regarding the quality of address matching, there is 1.25% of mismatched addresses and 2.36% of unmatched addresses in Input data 3. Results show that 40.94% of all the input addresses are geocoded on the corresponding parcels with the side offset, and the rest of the addresses (59.06%) are outside corresponding parcels. The percentage of the addresses geocoded on corresponding parcels is not very different in three kinds of input data with different ratios of correct matches. Figure 10: Accuracy of three machine learning models depending on the number of similarity metrics. On average, the geocoded points are 4.44 m further away from their corresponding parcels in Input data 1. The mean distance of Input data 1 is lower than the other two kinds of input data because a few addresses geocoded with long distances are unmatched, so the evaluation excludes the distances of these addresses. The 4 m distance on average with 7 m standard deviation is similar to GPS positional accuracy. GPS positional accuracy ranges from 4.4 to 10.3 m under an urban environment [48], so considering this, the developed geocoding system is applicable in some domains, like GPS-equipped car navigation.
# Protein Kinases in Mediating Phage-Bacteria Interactions
## 5 Bacteria HKs in Phage-Bacteria Interactions
### Phage-Encoded Anti-HKs Proteins  
Beyond the phage-encoded STKs previously discussed, recent work [96] reveals a novel phage counter-defense strategy: phage LSL4 encodes the protein PIT4, which simultaneously targets three histidine kinases (FleS, PiS, and PA2882) in _Pseudomonas aeruginosa_. PIT4 directly binds the kinase domains of these HKs, likely exploiting a conserved structural feature. This interaction downregulates expression of type IV pili and flagella--structures that serve as receptors for multiple phages [97]--thereby reducing bacterial motility. Critically, this suppression of surface receptors suggests PIT4 may exclude competing phages from infecting the host cell after LSL4 has established infection. Though not a kinase itself, PIT4 represents a functional analogue to anti-CRISPR proteins, implying that phages may widely encode such tactical inhibitors to maximize infection efficiency.
## 2 Experimental Section
### Material Characterisation  
The Fourier-transformed infrared (FTIR) spectra of the pristine samples and the loaded samples were recorded over a range of 600 cm\({}^{-1}\) to 4000 cm\({}^{-1}\) with the use of a PerkinElmer spectrum 100 FTIR spectrometer, which is also fitted in a PerkinElmer universal ATR sampling device. The Q Series-[Q5000-0140] TGA instrument was used for the thermogravimetric analysis (TGA) of the samples. The crystals were heated using platinum pans from 30 \({}^{\circ}\)C up to 600 \({}^{\circ}\)C at a 5 \({}^{\circ}\)C min\({}^{-1}\) heating rate with a continuous flow of nitrogen at a flow rate of 25 mL min\({}^{-1}\). The analysis of the data was performed using the TA Instrument's universal analysis 2000 Software. Powder X-ray diffraction (PXRD) data were recorded using a D8 Discover Bruker diffractometer with Cu K\(\alpha\)1,2-radiation (\(\lambda\) = 0.154018 nm, 1600 W) at an ambient temperature, and Origin was used for the analysis of diffraction patterns.  
Images of scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) data were collated using an FEI Quanta 400E-SEM with an Oxford Xplore 30 EDS system. Drug release studies were performed using a Jena UV-Vis spectrometer, to measure and record all the electronic absorbance spectra of the drug release study solutions.
# Link Load Correlation-Based Blocking Performance Analysis for Tree-Type Data Center Networks
## 3 Blocking Performance Analysis for Tree-Type Data Center Networks
### Link Load Correlation-Based Blocking Probability Performance Analysis
#### 3.3.1 Blocking Probability for Edge Class Connections  
In the tree DCN, all the \(L_{0}\) layer links connecting to the same edge switch can be regarded as part of a star network, due to the symmetry of the traffic distribution. The link independence assumption still applies and, so, the EC connection loads in different \(L_{0}\) layer links can be approximated by independent Poisson arrival distributions. The blocking probability of an \(L_{0}\) layer link with effective load \(\rho_{L_{0}}\) can be obtained by using the Erlang B formula [14], given as  
\[P_{L_{0}}=EB\big{(}\rho_{L_{0}},M_{0},M_{0}\big{)}=\frac{\big{(}\rho_{L_{0}} \big{)}^{M_{0}}/M_{0}!}{\sum\limits_{j=0}^{M_{0}}\big{(}\rho_{L_{0}}\big{)}^{j }/j!}. \tag{5}\]  
Then, the blocking probability for EC connections is  
\[P_{cc}=1-\big{(}1-P_{L_{0}}\big{)}^{2}. \tag{6}\]