esa-sceva/satcom-chunk-collection huggingface dataset:
# The Clique-Width of Minimal Series-Parallel Digraphs
## 1 Introduction  
The clique-width of graphs and digraphs has been defined by [PERSON] and [PERSON] [1]. Clique-width is an important graph parameter due to its algorithmic and structural properties. If the clique-width of a graph class or digraph class is bounded by a constant, a wide range of problems that are NP-complete in general have been shown to be polynomial time solvable on this class [2, 3, 4, 5]. While there are a lot of results on the clique-width of undirected graph classes [6, 7, 8], there are only a few known digraph class of bounded directed clique-width. Among these are directed co-graphs [5], specific orientations of distance hereditary graphs [9], and orientations of trees [5].  
In this paper we consider a directed version of undirected series-parallel graphs, which are graphs with two distinguished vertices called terminals, formed recursively by parallel and series composition ([10], Section 11.2). These graphs are interesting from a practical point of view due to their applications in modeling certain types of electric networks. Furthermore, they also play an important role in theoretical computer science, since they have tree-width at most 2 and are \(K_{4}\)-minor free graphs [11]. Further, a number of graph problems are solvable in linear time on series-parallel graphs [12]. Recently, it was proven by [PERSON] et al. that the clique-width of undirected series-parallel graphs on \(n\) vertices is at most 5, and an \(\mathcal{O}(n^{2})\) algorithm was proposed to construct a clique-width 5-expression for this type of graph [13].  
We consider minimal series-parallel digraphs (MSP DAGs) which are defined in [14] from the single vertex graph by applying the parallel composition and series composition. By ([15], Section 11.1) these graphs are useful for modeling flow diagrams and dependency charts and they have applications to scheduling under constraints. In [16] it was shown that the directed clique-width of an MSP DAG is at most 7. We prove a lower upper bound of 6 for the directed clique-width of MSP DAGs and show how a directed clique-width 6-expression can be found in linear time. Our given expression implies that every MSP DAG \(G\) can be constructed from its binary decomposition tree \(T(G)\) in linear time. We apply the shown bound on the directed clique-width to conclude a couple of algorithmicconsequences for MSP DAGs. Since the directed clique-width occurs in the exponent of the runtimes of algorithms for interesting problems our result improves the runtime of several algorithms on MSP DAGs.
# Research on Lifespan Prediction Methods Using Ultrasonic Microimaging for Electronic Packaging
## 3 Ultrasonic Image Data Processing and Feature Extraction
### Dynamic Normalization of Flip-Chip Ultrasonic Images  
In acoustic microimaging, as the number of thermal cycling experiments increases, the degradation of solder joints leads to overexposure in the images. To optimize contrast, it is necessary to adjust the gain of the acoustic microscopy system. However, even with constant gain settings, differences in the dynamic range of images persist. Therefore, a method for normalizing the dynamic range of solder joint images is proposed, with the following steps.  
Step 1. Data Acquisition and Extremum Determination:  
Initiate by scanning all pixels in the original ultrasonic image, denoted as \(x\). Extract the highest \(x_{max}\) and lowest \(x_{min}\) pixel values from this image.  
Step 2. Dynamic Range Specification:  
Define the original dynamic range \(R_{o}\) as the difference between the maximum and minimum values identified in the image, calculated by \(R_{o}=x_{max}-x_{min}\).  
Step 3. New Dynamic Range Setting:  
Set a new desired dynamic range \(\mathrm{R_{n}}\), typically 256 for standard 8-bit images. Calculate a midpoint value \(x_{mid}\) based on the original extremum values, defined as \(x_{mid}=x_{min}+\frac{R_{o}}{2}\).  
Step 4. Intensity Adjustment:  
Figure 3: Result of median filtering denoising on flip-chip and solder joint images. (**a**) Flip-chip C-scan image; (**b**) original image of solder joint with noise, derived from the solder joint marked by the red box in (**a**); (**c**) denoising results using \(3\times 3\), step size 1 square window.  
Figure 2: Schematic diagram of data processing method for solder joint images.  
Remap each pixel \(x_{ij}\) in the original image using a normalized scaling function. This is achieved by first shifting the image intensities to a zero-based range, followed by scaling to the new range, and then recentering on the median. The transformation is defined by:  
\[x^{\prime}_{ij}=\left(\frac{x_{ij}-x_{min}}{R_{o}}\right)R_{n}+\left(x_{mid}- \frac{R_{n}}{2}\right) \tag{1}\]  
where \(x^{\prime}_{ij}\) is the adjusted pixel intensity.
## 3 Proposed Receiver  
### Channel Estimation  
The reduced number of pilots in a single OFDM symbol and the fact that they experience ICI complicate the task of estimating the time-varying CIR. To counteract this, the observation model of the channel estimator is extended to a sliding window that includes adjacent OFDM symbols as follows:  
\[ \begin{bmatrix}\mathbf{w}^{k-1}\\ \mathbf{u}^{k}\\ \mathbf{w}^{k+1}\end{bmatrix} = \begin{bmatrix}\mathbf{G}^{k-1}\mathbf{s}^{k-1} \\ \mathbf{G}^{k}\mathbf{s}^{k}\\ \mathbf{G}^{k+1}\mathbf{s}^{k+1}\end{bmatrix} + \begin{bmatrix}\mathbf{z}^{k-1} \\ \mathbf{z}^{k}\\ \mathbf{z}^{k+1}\end{bmatrix} , \tag{7}\]  
where the superscript of each variable represents the position relative to the current \(k\) symbol. In order to carry out the channel estimation with this observation model, the algorithm proposed in [23] is used, where BEM is applied so as to obtain a compact representation of the CIR in the interval of the three OFDM symbols as follows:  
\[h[n,I]=\sum_{r=0}^{M_{\mathrm{T}}-1}\sum_{q=0}^{M_{\mathrm{D}}-1}\rho_{q,r} \phi_{q}^{I}[n]\phi_{r}^{II}[I]+\varepsilon[n,I], \tag{8}\]  
where \(\rho_{q,r}\) are the coefficients of the expression, \(m=\{0,...,3N_{b}-1\}\), \(M_{\mathrm{T}}\) and \(M_{D}\) are the number of functions used to expand the delay time domain and the time domain, respectively, and \(\{\phi_{q}^{I}[n],\forall q\in[0,M_{D}-1]\}\), \(\{\phi_{r}^{II}[I],\forall r\in[0,M_{\mathrm{T}}-1]\}\) are the functions that expand the time domain and the delay time, respectively. Given that in V2V scenarios the Doppler and delay dispersion presents statistics within a very diverse set, the discrete prolate spheroidal sequences (DPSS) are used as base functions since they optimally concentrate the energy in a finite time and bandwidth window. The modeling error for this representation in subspace is concentrated in the term \(\varepsilon[n,I]\). To determine the number of functions needed in each of the CIR domains, we use the approach proposed in [24]:  
\[M_{\mathrm{T}} =\lceil F_{\mathrm{s}}\tau_{max}\rceil+1, \tag{9}\] \[M_{D} =\lceil 2f_{D}3N_{b}/F_{\mathrm{s}}\rceil+1. \tag{10}\]  
Figure 2: Example of the quasi-band structure of channel frequency matrix \(\|\mathbf{G}\|^{2}\) in dB, for a V2V scenario with RMS delay spread of 0.4 μs and frequency Doppler spread of 1 kHz, modeling a [PERSON] fading NLOS scenario. where \(\lceil\cdot\rceil\) denotes the upward rounding operator, \(F_{S}\) is the system's bandwidth, \(\tau_{max}\) is the maximum time delay spread and \(f_{D}\) is the maximum frequency Doppler spread.
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 4 Conclusions  
The Fuzzy-FTA method represents an improvement over the conventional FTA, as it can accurately estimate the probability of each basic event. Through the fuzzy interpretation of data obtained by interviewing the on-site monitoring personnel with a rich management experience, the probability interval of the hardware reliability of a gas monitoring system based on different confidence levels can be obtained. It should be pointed out that the use of fuzzy mathematics to describe the probability of event discovery can not only reduce the difficulty of obtaining the accurate value of the probability of large and complex system events. At the same time, it can also combine field data with the experience of engineers and technicians. This method has greater advantages in flexibility and adaptability than the traditional FTA. In the future, the difficulty of big data calculation of the Fuzzy-FTA method can be reduced by software programming, in order for the method to be applied to more scenarios.  
Investigation, M.Y.; writing--original draft preparation, J.X.; writing--review and editing, S.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
## 2 System Model
### Key Generation and Reception  
A dynamic key is generated for the proposed embedding algorithm based on the pattern that gives the least MSE for that block. A 1920 \(\times\) 1080 color image is divided into three planes. Each plane  
Figure 5: Flowchart representation of the embedding algorithm.  
HH subband is divided into 4 \(\times\) 4 blocks. Therefore, the total number of blocks for one plane is 32,400. For each block, the pattern with the least MSE is applied, and a 3-bit key for that pattern is obtained from Table 1. The key size for one plane is given as 32,400 \(\times\) 3 = 97,200 bits. Three different keys for three planes of the color image are generated using the steps given below.  
Step 1: Start the embedding algorithm for Red plane  
Step 2: Select the pattern with the least MSE for the selected block. Let this be U.  
Step 3: Look for the 3-bit key from Table 1 based on U.  
Step 4: Shift the selected key to left by three times.  
Step 5: Repeat Step 2 and Step 3 for the next selected block.  
Step 6: Replace the three LSBs of the key with the selected 3-bit key from Step 3.  
Step 7: Repeat Step 4.  
Step 8: Repeat from Step 5 to Step 7 until the key is generated for every4 \(\times\) 4 block of the red plane.  
Step 9: Repeat from Step 1 to Step 8 for green and blue planes based on V and W respectively and generate keys for the green plane and blue plane.  
The schematic representation of the key generation for a color image is given in Figure 6a-c. The keys are generated at the control center and are communicated to the object showcase room. There, the key bit stream is split into three bits and given to the proposed extraction algorithm.  
Figure 6: Key generation and reception (**a**) Red plane, (**b**) Blue Plane, (**c**) Green Plane.
## 5 Result and Discussion
### Reliability of the Generator Model Based on FFT Frequency Analysis  
Using FFT forgery scores, we confirmed that our tuned cGAN model learned numerous valid RTI brushstroke variabilities, enabling the generation of realistic RTI brushstroke images. This confirmation was achieved by calculating F-Score for every single image patch with a resolution of 256 \(\times\) 256, and the results are reported and visualized. The distribution of F-Score for image patches of painting A, B, and C is illustrated in Figure 5. The higher F-Score per patch, which was mostly associated with the forged paintings B and C, indicated the uniqueness of the original brushstrokes that the forger could not successfully imitate. The reported results justified the observed extreme variance and lower mean of the forgery score for the forged painting C, see Figure 5. This can be attributed to the forger's freedom in choosing content that they were more comfortable faking. The mean F-score and their standard error of mean for all patch-level images is shown in Table 1.  
Figure 4: (**a**) Patch-level forgery scores calculation using FFT frequency analysis. (**b**) Patch-level forgery detection using trained discriminator.  
In this approach, the forgery score or F-score is not interpreted as the threshold to detect forgeries but is only for comparison and visualization of authentication of each patch of the whole painting. Furthermore, to mimic a real-world scenario, we did not provide the forger with any information regarding the brush size, or the canvas used by the original artist. The forger was allowed to replicate painting B from painting A and create painting C, claiming it was part of the original artist's collection. Regardless of the brushstroke size, the Euclidean norm, based on frequency features extracted from each image, was used to compare the cGAN-generated brushstroke image patches with the corresponding GT RTI image patches.
## 4 Results  
### Multi-Temporal Detecting Geological Hazards  
The 2008, 2009, and 2011 secondary geologic hazard areas, as determined by the above analysis, are shown in Table 3. This table shows that during the first year after the earthquake, the secondary geologic hazard development differed greatly among sites, with five sites experiencing an increased area and ten sites experiencing a decreased area. In 2011, however, almost all of the sites, except No. 14 and No. 15, experienced a decrease in hazard area, although at varying rates. The changes of geological hazards between 2008 and 2011 of several hazards are shown in Figure 4. From the monitoring results, the variations of geological hazards could be classified into four cases. The first case was for a continuous decrease in hazard area from 2008 to 2011 that included No. 2, No. 4, No. 5, No. 6, No. 7, No. 8, No. 12 and No. 13, accounting for 53.3% of all sites. The second case was for an increase in hazard area from 2008 to 2009, and a decreased hazard area in the next two years, such as the No. 3, No. 10, No. 11 and No. 15 sites. The third case was for a decreased area from 2008 to 2009 and an increase in hazard area in the next two years, such as the No. 1 and No. 9 sites. The last case was for a continuous increase in hazard area from 2008 to 2011, which pertained only to site No. 14. Nonetheless, from 2008 to 2011, the overall geologic hazard area decreased as vegetation growth rate exceed that of debris flow development, and the geologic hazards variation rate between 2008 and 2011 was approximately 12%, which was higher than the rate of the first year after the earthquake. Figure 3: Photos taken at geologic hazard sites during the field investigation in May 2012. (**a**) Vegetation recovery in Yingxiu Town, Wenchuan County (site No. 7); (**b**) Old Beichuan Town, Beichuan County (site No. 10); (**c**) The huge failure in Tangjiashan, Beichuan County (site No. 10); (**d**) Vegetation recovery on the failure slope in Qingping Town, Mianzhu City (site No. 1). Figure 4: Airborne remote sensing images of several hazards in 2008, 2009, and 2011, respectively (from left to right). (**a**) The big landslide and debris flow of Wenjiagou, Qingping Town, Mianzhu City (site No. 1); (**b**) The big landslide group happened at Yingxiu Town, Wenchuan County (site No. 7); (**c**) The change of hazard happened at Tangjiashan, Beichuan County (site No. 10); (**d**) The change of hazard happened at Changheba, Mianzhu City (site No. 14); (**e**) The change of Daguangba hazard group, An County (site No. 15).
## 2 Materials and Methods
### Performance Metrics  
A rigorous statistical framework was adopted to compare machine learning models for predicting innovation outcomes, encompassing careful selection of performance metrics, cross-validation procedures, and a structured evaluation method. By incorporating multiple quantitative indicators, the analysis aimed to capture the multidimensional nature of model performance and support robust, reproducible findings [32; 33].  
Performance was assessed through well-established metrics, each emphasizing a particular aspect of predictive accuracy and practical utility. Accuracy, defined as the proportion of correctly classified instances, provided a baseline measure but could obscure issues arising from class imbalance. Precision, the fraction of true positives among predicted positives, was integral for scenarios where false alarms or misclassifications imposed high costs. Recall (sensitivity) quantified the proportion of actual positives correctly identified, a key consideration when failing to detect important signals--such as potential breakthroughs--can be costly. The F1 score, as the harmonic mean of precision and recall, underscored the trade-offs inherent in imbalanced data. Finally, the area under the receiver operating characteristic curve (ROC-AUC) served as an overarching indicator of a model's discriminatory power by aggregating its performance across varying thresholds [34]. Employing these metrics in tandem ensured that the comparative analysis of machine learning algorithms provided a holistic perspective on predictive efficacy in the context of innovation outcomes.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Model** & \multicolumn{1}{c}{**Hyperparameter Ranges**} \\ \hline LR & S: [’liblinear’,’lbfgs’,’saga’]; \text{P: [’l1’,’l2’, None]; \text{C: log($-4$,2); LR: 0-1; MI: 100–500’50} \\ \hline RF & N: 50–500’1; MD: 5–30’1; MSS: 2–10’1; MSL: 1–5’1; MF: [’sqrt’,log2’], None; OOB: [True, False]; \\  & MS: 0.5–1 \\ \hline XGB & N: 50–500’1; MD: 2–10’1; LR: 0.01–0.3; SS: 0.5–1; CT: 0.5–1; CL: 0.5–1; G: 0–1; MCW: 1–10’1; \\  & MDS: 0–10; RA: 0–1; RL: 0–2 \\ \hline LGBM & N: 50–500’1; MD: 3–15’1; LR: 0.01–0.3; SS: 0.5–1; CT: 0.5–1; MDL: 10–100’1; MSG: 0–0.1; FE: 0.5–1; \\  & RA: 0–1; RL: 0–5; Y: \(-1\) \\ \hline CB & I: 50–500’1; D: 4–10’1; LR: 0.01–0.3; SS: 0.5–1; RSM: 0.5–1; MDL: 1–100’1; L2R: 0–10; BT: 0–1; \\  & RS: 0–10; GP: [’SymmetricTree’, ‘Depthwise’,’Lossguide’]; LE: 1–10’1; EM: “Logloss” \\ \hline SVM & K: [’linear’,’poly’,’rbf’,’sigmoid’]; \text{C: log($-4$, 4); G: log($-9$, 3); D: 2–5’1; T: log($-5$,$-1); \\  & MI: 100–1000’100} \\ \hline ANN & S: ’adam’; HLS: [(10),(50),(100), (50,50),(100,50)]; AT: [’relu’,’tanh’,’logistic’]; \text{A: log($-5$,$-2); \\  & LR: log($-5$,$-2); MI: [200,300,500,100]; BS: [32,64,128,256,’auto’]; BL: 0.85–0.99; B2: 0.9–0.999; \\  & E: log($-10$,$-5$); T: log($-6$,$-3); ES: [True, False]; VE: 0.1–0.3 \\ \hline \hline \end{tabular}  
* The full names corresponding to the abbreviations for models and hyperparameters are provided in Table 1.  
\end{table}
Table 2: Hyperparameter ranges for Hyperopt tuning across selected models.
## 3 Results
For shelters, the training and testing RMSE values are 6.7791 and 7.7213, respectively, suggesting higher prediction errors compared to lodging facilities. The correlation for training data is strong (0.8593), indicating a positive relationship between predicted and actual values, but the test set shows zero correlation (-0.0835 but insignificant), suggesting low generalizability of the model for shelter predictions. Additionally, the MAPE values (79.41% for training and 107.01% for testing) further emphasize the challenges in achieving accurate relative predictions for shelters. Compared to the benchmark GLM method (Tables 4 and 5), the proposed GAM approach (Tables 6 and 7) has significantly improved predictive accuracy for both shelters and lodging facilities with respect to all performance measures. The GAM model showed superior performance in comparison with the benchmark GLM. Notably, the GAM significantly reduced RMSE and MAE across both shelters and lodging facilities. For shelters, the GAM achieved a training correlation coefficient of 0.8593, a substantial improvement over the GLM's 0.1760. Similarly, for lodging facilities, the GAM outperformed the GLM with better RMSE, MAE, and correlation metrics. The GAM's flexibility in modeling nonlinear patterns and its capacity to incorporate spatial and temporal smoothing functions resulted in more accurate and generalizable predictions.
# Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image
## 2 Materials and Methods
### Study Area  
An approximate area of 25 sq. km. in central south western region of South Korea was selected for the study (Figure 2). The area lies in Jeollabuk province and is geographically bounded by 35\({}^{\circ}\)34\({}^{\prime}\)49.38\({}^{\prime\prime}\) N to 35\({}^{\circ}\)38\({}^{\prime}\)0.47\({}^{\prime\prime}\) N and 126\({}^{\circ}\)43\({}^{\prime}\)8.41\({}^{\prime\prime}\) E to 126\({}^{\circ}\)45\({}^{\prime}\)54.48\({}^{\prime\prime}\) E. Most of the area is agricultural land with barren and vinyl house farmlands. Few areas were covered by the forest or built-up structures like roads and residential areas. A river passes through the center which is dammed to preserve water for irrigation. The selection of the area was due to its availability of data and ease in identification of diverse land cover classes.  
Figure 1: Overall process adopted in this study where land cover classification of Korea Multi-Purpose Satellite (KOMPSAT)-3A image was done using four supervised methods i.e. Mahalanobis Distance (MahD), Minimum Distance (MinD), Maximum Likelihood (ML) and Support Vector Machine (SVM).  
Figure 2: \(Cont.\)
## 3 Experimental Results
### Dataset  
This study used the publicly available Sleep-EDF database [22], which was labeled and verified by experts. The database analyzed the sleep data of 20 individuals (10 men and 10 women) between the ages of 25 and 34 years old. The PSG signals in the data set include a horizontal EOG signal, a chin EMG signal, and two EEG signals: the Fpz-Cz channel and the Pz-Cz channel. This experiment adopted the Fpz-Cz location channel (Figure 2). These signals were sampled at a rate of 100 Hz and segmented into 30-s intervals for a total of 3000 sampling points. In addition, the signals in the data set are not scaled or preprocessed. They were then classified into six categories according to R&K's classification standards: W, N1, N2, N3, N4, and REM. They represent different levels of the patient's depth of sleep. The amount of data for W, N1, N2, N3, and REM are 8285, 2804, 17,799, 5703, and 7717, respectively. To meet the classification standards of AASM, this experiment combined the two sleep stages of N3 and N4. These recordings included a total of 42,308 signals divided into training data and testing data at a ratio of 7:3.  
\begin{table}
\begin{tabular}{c
# Overview on Different Types of Solar Cells: An Update
## 2 Organic Solar Cells
### Computational for Organic Solar Cells  
The process of synthesizing new materials for OSCs is time-consuming and expensive. Consequently, a computer model for predicting the PCE of a new device without physical experiments is required. The Scharber equation can predict the PCE of any OSC using a few key parameters that can be determined computationally through density functional theory (DFT) and machine learning (ML). The Harvard Clean Energy Project Database (CEPDB) contains computationally predicted PCE values for 2.3 million potential organic photovoltaic systems through machine-learning models.  
In this context, the PCE, the short-circuit current density Jsc, the open-circuit voltage Voc, the HOMO energy, the LUMO energy, and the HOMO\(-\)LUMO gap are reported as computed by the DFT methods described by [PERSON] and co-workers using the Scharber equation.  
\[\text{PCE}=\frac{\text{FF}\,V_{oc}J_{sc}}{P_{in}} \tag{4}\]  
where FF is the fill factor and Pin is the input power.  
The open-circuit voltage given by the expression below is derived by [PERSON] and co-workers.  
\[(V_{oc}=\frac{1}{e}\Big{(}\Big{|}E^{\text{donor}}\text{HOMO}\Big{|}-\big{|}E^{ \text{acceptor}}\text{LUMO}\big{|}\Big{)}-0.3 \tag{5}\]  
where \"\(e\)\" is the electron charge, \"\(E^{\text{donor}}\text{HOMO}\)\" is the energy of the highest occupied molecular orbital (HOMO) of the donor material in the cell, \"\(E^{\text{acceptor}}\text{LUMO}\)\" is the energy of the lowest unoccupied molecular orbital (LUMO) of the acceptor material in the cell, and 0.3 is an empirical correction.  
The short-circuit current density is given by  
\[J_{sc}=\text{EQE}\ \times e\ \int_{Eg}^{\infty}\mathcal{O}_{\text{ph}}(E)\ dE \tag{6}\]  
where the external quantum efficiency EQE set to 0.65 in the Scharber model, \(Eg\) is the band gap of the donor material, and \(\phi_{\text{ph}}\) is the incident solar photo flux density as a function of energy \(E\).