esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Algorithm
### Removing Redundant Vertices  
The results produced by our greedy algorithm may contain redundant vertices. A redundant vertex is a vertex that can be removed from a solution without making the solution invalid. Formally, a vertex \(v\in S\) is redundant if each vertex \(u\) from its closed neighborhood \(N(v)\) has strictly more than half of its neighbors in \(S\). In other words, \(v\in S\) is redundant if and only if \(\forall u\in N(v):h_{S}(u)<0\). That is, each vertex in \(S\) is checked in sequence to find whether it is redundant. If it is the case, this vertex may safely be removed from \(S\) and all values \(h_{S}(.)\) of its neighbors will be decreased by one. A pseudo-code of the complete removal-function is presented in Algorithm 5. This function has a time complexity of \(O(n+m)\). Note that our algorithm, by default, makes use of this function. However, we will also test our algorithm without removing redundant vertices. The resulting algorithm variant is henceforth labelled IGA-PIDS\({}^{-}\).  
```
0: a valid solution \(S\) that may contain redundant vertices
0: a valid solution \(S\) without redundant vertices
1:for each vertex \(v\in S\)do
2:if\(h_{S}(u)<0\) for all \(u\in N(v)\)then
3:\(S\gets S\setminus\{v\}\)
4:for all \(u\in N(v)\)do
5:\(h_{S}(u)\gets h_{S}(u)-1\)
6:endfor
7:endif
8:endfor
9:return\(S\)
```  
**Algorithm 5** Function Reduce(S)  
Another way to implement this procedure, which is not considered here, would be to adopt the same removal strategy as presented in [23] for the minimum weight dominating set problem. For the latter, all redundant vertices are initially grouped in a unique set called \(S^{\prime}\). Then, a vertex from \(S^{\prime}\) is iteratively chosen to be removed according to a particular greedy function. In the case of the MPIDS problems, we could select the vertex with the smallest degree, for example. This iterative process stops once \(S^{\prime}\) becomes empty and all redundant vertices are removed.
# Experiment on Flexural Fatigue Performance of Precast Bridge Deck Joints with Loop Connections
## 2 Typical Application of Loop Connection Joint  
In highway bridge engineering, the loop connection can be applied in the longitudinal joints and transverse joints of prefabricated structures. According to the different size of the joint, the application of the loop connection joint can be divided into the following typical cases.  
1. Transverse joint of precast bridge deck.  
At present, the representative application situation of transverse joints in bridge structures mainly include steel-concrete composite girder bridges and pile-plate soilless subgrade structures [21; 22]. In these structures, the loop connection joints are mainly applied to the connection of adjacent precast bridge decks, as shown in Figure 1a,b.  
The thickness of the deck in these kinds of bridge structures are generally in the range of 220 mm to 260 mm. The width of deck joints is commonly about 200 mm to 250 mm. With the improvement of industrial construction technology, the width of the joint can generally be optimized to 300 mm. In these kinds of deck joint, the diameter of longitudinal loop bars can be 18 mm to 25 mm.  
2. Longitudinal joint of T-shape girder bridge.  
For the small- and medium-span assembled bridges with multi-girders, the girders in the same span need to be connected by certain longitudinal joints [23]. In the precast T-beam bridge structure, the application of longitudinal joints with a loop connection is more typical, as presented in Figure 1c.  
In the commonly adopted standard diagram of a T-shape girder bridge, the width of the longitudinal joint is generally between 300 mm and 800 mm, of which the joint with a width of 500 mm is a typical representative. The thickness of such longitudinal joints usually depends on the thickness of flange plate of the T-shape girder, which is usually 160 mm. The diameter of the loop bar in this type of joint is generally 16 mm.
3. Longitudinal joint of small box girder bridge.  
Another typical application of a longitudinal joint in a multi-girder assembled bridge is in small box girder bridges [24], as shown in Figure 1d.  
In the recommended standard diagram of the small box girder bridge, the width of the longitudinal joint is usually less than 1000 mm, and the commonly adopted width is from 500 mm to 800 mm. Due to the large width of this type of joint, the loop bars in the joint are generally arranged in the form of a double loop overlap. The thickness of the longitudinal joint in the small box girder bridge is about 180 mm, and the diameter of the loop bar in this type of joint is generally 16 mm as well.  
Figure 1: Typical application situation of a loop connection joint: (**a**) transverse joint of concrete–steel composite bridge deck; (**b**) transverse joint of pile–plate soilless subgrade structures; (**c**) longitudinal joint of T-shape girder bridge; and (**d**) longitudinal joint of small girder bridge.
## 4 Future Prospects for China's Weather Routing Services
### Expanding Arctic Weather Routing Service  
Recent geopolitical shifts have heightened uncertainties in global shipping networks, with escalating tensions along traditional chokepoints such as the Strait of Malacca, Suez Canal, and Panama Canal amplifying systemic vulnerabilities. Concurrently, accelerated climate change has triggered a faster-than-expected retreat of Arctic Sea ice, with melting rates exceeding IPCC model projections by 15-20 years [55], significantly extending the navigable window for Arctic routes. These environmental and geopolitical dynamics are catalyzing a new era of polar shipping, where BeiDou-3/GNSS high-precision positioning and ice-strengthened vessel technologies have enabled large-scale container ships and LNG carriers to operate in the Arctic.  
However, the Arctic's extreme and volatile conditions--including sudden storms, ice coverage, subzero temperatures, and dense fog--pose unprecedented challenges to navigational safety, demanding higher precision and adaptability from weather routing services. To address these challenges, Arctic-specific weather routing systems must deliver hyper-accurate ice zone hydrometeorological forecasts, encompassing multidimensional parameters such as wind speed, wave height, ice thickness, ice concentration, ice age, ice drift, and temperature. Data from various sources, including satellite SAR imagery, buoy data, model outputs, and potentially observations from vessels of convenience, will be critical [56]. AI techniques can play a crucial role here by integrating diverse real-time data sources (e.g., satellite SAR imagery, buoy data, model outputs) to predict complex ice dynamics and identify optimal paths through or around hazardous ice features [57]. By integrating satellite imagery, real-time ice condition data, and dynamic ice forecasts, these systems can generate tailored route guidance based on vessel type and ice breaking ability, mitigating risks from unpredictable ice obstacles and ensuring safe Arctic transit. Significant challenges remain, including the sparse observation network in the Arctic, the difficulty of accurately modeling sea ice dynamics, the harsh operating environment for sensors, and the high cost of specialized services and ice-strengthened platforms, alongside geopolitical sensitivities.
# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 6 Conclusions  
Visual examining long-term EEG recordings to detect epileptic seizures is a costly and time-consuming method. This study proposed a new seizure detection algorithm using EEG signals to address these issues. The main strength of this study is the analysis of the available samples using both a multilayer perceptron neural network classifier and a wavelet transform function.  
Future studies with larger sample sizes are needed to confirm and expand upon these findings. In this study, the diagnostic value of physiological signals measured using EEG was evaluated, and features such as FMD, FMN, FR, and WL were extracted. FMD and FMN, which are the median and mean frequencies, were obtained through the Fourier transform of the desired signal. In individuals with epilepsy, these two characteristics increase due to higher brain frequency and were compared to normal individuals. FR, or frequency rate, refers to the high-to-low frequency ratio of the brain, and in individuals with epilepsy, the frequency rate decreases due to higher brain frequency compared to normal individuals. WL, or waveform length, is a temporal feature that measures the changes and complexity of the signal by subtracting the absolute magnitude of subsequent samples from the previous samples and adding them together. In individuals with epilepsy, WL is reduced due to decreased entropy and similar fluctuations in the brain. Calculating these features provides useful information for physicians to diagnose and treat patients with epilepsy.  
According to the studies conducted in this paper, there is not a significant difference between the two methods used, but the experiment using the multilayer perceptron neural network (MLP) achieved slightly higher accuracy than the experiment using the wavelet transform function, as shown in Table 3. In recent years, new methods for automatic epilepsy detection have been emerging. Improving the speed and accuracy of epilepsy detection models will contribute to the development of clinical diagnosis techniques and portable, integrated epilepsy detection equipment. Therefore, a concise and efficient epilepsy detection model will be a necessary trend in the future.  
Conceptualization, M.R.Y.; Methodology, M.R.Y., S.G., M.M.H. and A.D.; Investigation, S.G.; Writing--original draft, S.G., M.M.H. and A.D.; Supervision, M.R.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The details of the database are summarized in Table 1. The database is open-access and available on the website of Bonn University.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Modeling Methods  
Partial least squares (PLS) is the most widely used regression modeling method in spectral data analysis due to its flexibility and reliability in dealing with redundant spectral data. In the PLS model, the spectral matrix is decomposed first, and the main principal component variables are obtained; then, the contribution rate of each principal component is calculated. The flexibility of PLS makes it possible to establish a regression model in the case where the number of samples is less than the number of variables. In this study, the PLS model was established with the spectral data as \(X\) and the measured soil water-solution content as \(Y\), whose best principal factor was determined by the root mean square error of cross validation (RMSECV) [25].  
Principal component analysis (PCA) is a statistical method based on the principle of maximum variance, which adopts low dimensional variables instead of the original high dimensional variables so as to achieve the purpose of reducing data dimension. The principal components of the PCA are independent of each other, which effectively eliminates the redundant information in the original data. In general, the first few principal component variables existed with large deviations, which can be used to sample and discriminate between clusters of species instead of all the variables [26].  
Least squares support vector machines (LS-SVM) is a supervised learning model, whose main purpose is to find an optimal classification hyperplane to satisfy the classification requirements. The LS-SVM first maps the nonlinear training data to a higher dimensional feature space (Hilbert space), and then finds a hyperplane between positive and negative values in the high dimensional feature space. The application of LS-SVM can effectively solve the problems of a traditional neural network, such as the local minimum and overfitting. It provides an impressive performance for small samples, nonlinear high dimensional data such as machine learning problems, which has been widely used in pattern recognition; data mining, and other fields [27].  
All data analysis was based on MATAB R2014a (The Math-Works, Natick, MA, USA) and OMNIC v8.2 (Infrared spectrum processing software, Thermo Fisher Scientific, Waltham, MA, USA).
## 3 The Proposed Satellite Images DSM Generation Method
### Alignment and Fusion  
Since the proposed method adopts the stereo-pair-based multi-view stereo method for DSM generation, multiple 3D point clouds of AOI will be obtained when it performs DSM generation on multi-date satellite images. In order to improve the accuracy of the 3D models, multiple 3D point clouds need to be aligned and fused. In this process, we project the 3D point clouds on a geographic grid and align the 3D point clouds according to the geographic coordinate, then we average the pixels at the same location.
### Literature Review  
The following algorithms have been successfully applied to optimize various OPF problems: manta ray foraging optimizer (MRFO) [3], grey wolf optimization (GWO) [16; 17], a parallel genetic algorithm (GA) (EPGA) [18], multi-objective electromagnetism-like algorithm (MOELA) [19], a distributionally robust approach for OPF [20], a combination firefly-bat algorithm (HFBA-COFS) [21], social spider optimization (SSO) [22], solving OPF by GA and teaching-learning-based optimization (TLBO) (G-TLBO) [23], a bacterial foraging algorithm (BFA) [24], various differential evolution (DE) algorithms [25; 26; 27; 28; 29; 30; 31], Harris hawks optimization (HHO) [32], cuckoo search algorithm (ECSA) [33; 34], chaotic invasive weed optimization algorithms (CIWOs) [35], multi-objective dynamic OPF (MOD-OPF) [36], salp swarm algorithm (SSA) [37], TLBO with Levy mutation (LTLBO) [38], voltage stability constrained OPF (VSC-OFF) [39], considering effects of solar position [40], the hybridization of PSO with GWO, namely a PSO-GWO algorithm [41], symbiotic organisms search (SOS) [42], artificial bee colony (ABC) algorithms [43; 44; 45], group search optimization (GSO) [46; 47], a new combine algorithm, SFLA-PSO [48], a colliding bodies optimization (CBO) [49], tunicate swarm algorithm (TSA) [50], a modified hybrid PSO [51] and GSA with chaotic maps (CPSOGSA) [52], sine-cosine algorithms (SCAs) [53; 54], chaotic bonobo optimizer (CBO) [55], a honey bee mating optimization (HBMO) [56], a heap-based optimization (HBO) [57], slime mold algorithm (SMA) [58], mayfly algorithm (MA) [59], BAT search algorithm [60], moth swarm algorithms (MSA) [61; 62], bird swarm algorithm (BSA) [63], a new evolutionary algorithm (EA) [64], etc.
# Analysis of BIM-Based Quantity Take-Off in Simplification of the Length of Processed Rebar
## 3 Methodology & Results
### Selection of Calibration Lengths and Quantity Take-Off
#### 3.2.3 Beams  
The beam consists of main bars, arranged axially to resist bending moments, and stirrups surrounding the main bars, in the lateral direction to resist shear forces. The main bars consist of a combination of top, bottom, and side bars (skin reinforcement), depending on the direction of the bending moment acting on the beam. In that case, the bending moment changes and is distributed by sections; thus, the beam is designed to be divided into the external, center, and internal sections. Figure 4 below shows an example of the rebar arrangement of beam members and the name of the rebar for each part.  
The lengths of the beam member's processed rebar were calibrated by segregating them into the cases of unrefined length and simplified lengths A, B, and C. Tables 5 and 6 depict the results of the rebar arrangements and QTOs, as follows. The result values of QTO for the buildings of the project were summed, and the differential for each case was calculated for each project. In the case of Project H, the increase rate in the amounts of rebar (+4.67%) and the decrease rate in the number of LPRS (\(-41.24\%\)) were the largest based on Case C. The QTO results and differentials for each building of each project are included in Tables A6 and A7 in Appendix A. Table A6 shows the calculated amount of rebar by building for beam members by case. The \"Diff.\" specified in each case means the differential with the unrefined case. Table A7 shows the calculated number of LPRS in the same way.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{1}{c}{**Building No.**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Amount of** \\ **Rebar Material** \\ **(Ton)** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Types of** \\ **Rebar Lengths** \\ **(Units)** \\ \end{tabular} } \\ \hline \multirow{2}{*}{**Project B**} & Apartment \&Office & 101 & 213.09 & 2395 \\  & & 102 & 212.89 & 2374 \\ \cline{2-5}  & Underground Parking Lot & 337.75 & 838 \\ \hline \multirow{6}{*}{**Project C**} & \multirow{3}{*}{Apartment} & 101 & 172.03 & 1281 \\  & & 102 & 165.43 & 1267 \\  & & 103 & 172.77 & 1188 \\  & & 104 & 202.60 & 2060 \\  & & 105 & 125.04 & 1217 \\ \cline{2-5}  & Security Office & 0.89 & 22 \\ \cline{2-5}  & Underground Parking Lot & 82.82 & 264 \\ \hline \multirow{6}{*}{**Project G**} & \multirow{3}{*}{Apartment} & 101 & 128.51 & 1431 \\  & & 102 & 175.89 & 1994 \\  & & 103 & 214.92 & 1629 \\  & & 104 & 201.37 & 1784 \\  & & 105 & 289.74 & 2931 \\  & & 106 & 198.93 & 2426 \\ \cline{2-5}  & Security Office & 1.89 & 13 \\ \cline{2-5}  & Underground Parking Lot & 80.49 & 163 \\ \hline \multirow{2}{*}{**Project H**} & \multirow{2}{*}{Mixed-useApartment} & 1 BL & 1120.48 & 6057 \\  & & 2 BL & 1077.46 & 5933 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 4: Example of the rebar arrangement of beam members and the name of the rebar for each part (RF, Building #101 of construction project A).
## 2 Parametric Study
### Monopile Lateral Ultimate Capacity  
The monopile lateral ultimate capacity may be governed by either the soil resistance or the pile's structural capacity. Hence, it was important to model the correct behavior of the soil and pile components. In Plaxis 3D, the pile can be modelled using either plate elements or embedded beam elements. However, both are assumed to behave elastically. While volume elements do not provide information on structural forces, embedded beam elements do provide structural forces and can be used to check whether the yield strength of the structural elements is reached. Ref. [36] indicated that by incorporating a solid zone around the pile, the embedded beam elements within solid elements behave like volume piles. Correspondingly, embedded beam elements within solid elements were utilized to simulate the piles, to enable proper evaluation of its ultimate capacity. The combined rigidity of both the solid elements and embedded beam elements was equivalent to that of the open-ended pile. To ensure beam elements would deflect with the solid elements, their Young's modulus E was divided by \(10^{6}\). The resulting bending moment was then  
\begin{table}
\begin{tabular}{c c c c} \hline
**L, m** & **L/D/(L\({}_{\text{P}}\)/W)** & **Foundation System** & **e/D\({}_{\text{t}}\)** & **V, kN** \\ \hline
20 & 3.33 & & 6.83 \\
30 & 5 & & 6.83 \\
40 & 6.67 & & 6.83 \\
50 & 8.33 & Monopile & 6.83 \\
60 & 10 & & 6.83 \\
70 & 11.67 & & 6.83 \\
80 & 13.33 & & 6.83 \\ \hline \end{tabular}
\end{table}
Table 3: Range of parameter analyses.  
Figure 9: The considered monopile-foundation-system geometric notations (not to scale).  
multiplied by \(10^{6}\) to calculate the correct bending-moment value. Failure was determined to be either geotechnical or structural after examining output data and checking whether the applied load causes yielding conditions at the mudline. The lateral ultimate capacity is taken as the linear average between the two values on either side of the value that caused structural-capacity failure.
## 3 Maximum Power Point Estimator
### Evaluation of the Estimator Characteristics for the Double-Stator Generator  
Figure 8 shows the characteristics of the double-stator generator for each rotation speed. If the rotation speed increases, the generating power also increases. This characteristic indicates that there are maximum power points for each rotation speed of the generator. When the internal resistance \(R_{M}\) equals the load impedance \(Z\), the condition is such where the power for each rotation speed is at maximum. From the measurement data the approximate equation at each speed is plotted. From the plot at each speed the maximum power point is traced as a power line for the controller to be tuned.  
Figure 8: Characteristics of the double-stator generator for each rotation speed.
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 4 Evaluation  
To assess the efficacy of our proposed method integrating optimized DBSCAN with an improved Newman-Girvan clustering approach (GraphDBSCAN), we conducted extensive experiments on real-world and synthetic datasets. Real-world datasets include DBLP, Amazon, and Cora, sourced from public repositories such as SNAP (Stanford Network Analysis Project), alongside a synthetic scale-free network generated using the Barabasi-Albert model. These datasets vary in size, density, and community structure, providing a robust testbed to evaluate our method's adaptability and noise-handling capabilities. We compared GraphDBSCAN against state-of-the-art methods: MDBNG [29], Newman-Girvan, and Louvain, with Before Noise Removal (using Louvain on the initial graph) as the baseline. Label Propagation was excluded from comparisons to focus on modularity-driven methods, aligning with our evaluation goals.  
Evaluation metrics include the number of communities, graph density (ratio of actual to possible edges), modularity (quality of community partitions), average betweenness centrality (node bridging importance), average degree centrality (node connectivity), and average closeness centrality (node proximity to others). For each dataset, we sampled 1000 nodes to ensure computational feasibility while preserving structural integrity, reflecting the latest code implementation. The results demonstrate that GraphDBSCAN consistently enhances modularity and reduces noise across datasets, outperforming baselines due to its automated DBSCAN parameter tuning via KNN, proximity-based feature extraction, and refined edge removal strategy.