esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This article proposes a new algorithm to optimize public urban transportation and encourage its use among the population. It enhances the efficiency of public urban transport systems by integrating advanced technological components, such as statistical data analysis and real-time computer vision services.  
The PUTSS algorithm integrates the following components to identify congestion levels on public transport routes: a Statistical Data Component (SDC) and a Real-Time Computer Vision Component (RTCVC).  
The SDC leverages historical and real-time statistical data to predict passenger numbers at various stations, allowing for preemptive adjustments in fleet frequency to manage expected congestion.  
The RTCVC employs Microsoft Azure Computer Vision services to monitor and count passengers at each station in real time. This component enables the system to address sudden changes in passenger numbers, such as those caused by heavy rain, concerts, or other unexpected events.  
This article includes a case study evaluating the accuracy of the Microsoft Azure Computer Vision service. Analyzing 100 images, the recognition accuracy was 73%, correctly identifying the number of people in only 73 out of 100 tests. The global accuracy rate was computed at 89.81%.  
Using a proposed mathematical tool, the PUTSS algorithm computes the congestion factor across the route, assigning numeric values based on traffic status from Google Maps Service. The algorithm adjusts public urban transport schedules to avoid empty runs and excessive passenger wait times by setting a maximum waiting time of 15 min. It makes decisions based on the _SDCF_ and _RTCVCF_ values, ensuring that buses depart when they meet specific congestion criteria.  
The five scenarios underline the dynamic decision-making capabilities of the PUTSS algorithm in real-time urban transportation management. The PUTSS algorithm analyzes the congestion levels and passenger counts to adjust bus schedules, optimizing passenger service while minimizing operational costs for the transport provider.  
Implemented in C#, the algorithm was tested using a simulator that generates various scenarios. However, the empirically set reserve value of five for the passenger number margin and the potential inefficiency of buses waiting at terminals when trips are skipped highlight areas for refinement.  
Conceptualization, C.-M.R.; methodology, C.-M.R. and A.S.; software, C.-M.R.; validation, C.-M.R. and A.S.; formal analysis, C.-M.R. and A.S.; investigation, C.-F.N. and I.-A.G.; resources, C.-F.N. and I.-A.G.; data curation, C.-M.R.; writing--original draft preparation, A.S., C.-F.N. and I.-A.G.; writing--review and editing, A.S., C.-F.N. and I.-A.G.; visualization, C.-M.R. and A.S.; supervision, C.-M.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Petroleum-Gas University of Ploiesti, Romania.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Performance Tuning for Power Electronic Interfaces Under VSG Control
## 5 Parameter Tuning for Frequency Response and Regulation
### Parameter Tuning for Performance Indices  
In some countries (such as Germany, the USA, and the UK), the system operators have developed a time frame for renewable penetrated grids. For example, the prescribed limit for ROCOF is 0.125 Hz/s [38]. Also, in this material, the maximum ROCOF and PFR delivery time in its case study are set to be 0.5 Hz/s and 10s, respectively. In another research, the maximum frequency deviation \(\Delta f_{max}\) is set as 0.2 Hz [24]. It is noted that if the ROCOF exceeds 1 Hz/s, the frequency relay would be tripped and incur a large disturbance to a power system [39]. Generally, the delivery of PFR is required to be within 10-30s, and the time for SFR delivery is from 30 s to several minutes. The PFR capacity should always limit the frequency between 49.5-50.5 Hz. The typical standards (nominal frequency is 50 Hz) are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Indices** & **Standard** \\ \hline Maximum ROCOF magnitude & 0.125–0.5 Hz/s \\ Frequency nadir/peak magnitude & 0.2–0.5 Hz \\ Settling frequency & 49.5–50.5 Hz \\ PFR delivery time & 10–30 s \\ SFR delivery time & 30 s–minutes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance indices for IR and PFR.  
Figure 13: Composite parameter influences on performance indices in the SFR stages: (**a**) SFR settling time versus \(H\) and \(R\), (**b**) SFR overshoot versus \(H\) and \(K_{I}\), and (**c**) overshoot versus \(R\) and \(K_{I}\).  
It is noted that the stages of IR, PFR, and SFR activate in chronological order. Therefore, based on the composite effects and the standards, a flowchart of parameter design for VSG-PEIs is shown in Figure 14. In step A, the standards for ROCOF and frequency nadir are input. The maximum ROCOF only relates to inertia constant, so the range of \(H\) could be determined according to the ROCOF standard. The frequency nadir relates to both \(H\) and \(R\), then according to the \(H\) range and the nadir requirement, the range of \(R\) could be determined. Therefore, in step B, the satisfying ranges of \(H\) and \(R\) are obtained. In step C, the desired \(H\) and \(R\) are determined by checking the settling frequency and the settling time. If the VSG-PEI does not participate in the following SFR, the design comes to an end. Otherwise, the parameter design goes on for the SFR stage. If the parameters of \(H\) and \(R\) are inherited from the previous, it jumps to step F. If not, in step D and E, the standard of settling time is input, and the satisfying ranges of \(H\) and \(R\) are obtained. In step F, \(K_{I}\), and \(H\), \(R\) are determined by checking the overshoot.
## 4 SEM Scanning Test Results and Analysis
### SEM Image Results and Processing
#### 4.1.2 Binary Segmentation Method  
Image processing and quantitative analysis are carried out by image analysis software. The depth of a pixel in an image can be regarded as the linear distance from the point to the lens. After processing the image with filters, a certain gray value \(Z_{i}\) (0-255) is taken as the threshold, the area with gray value higher than the threshold is taken as soil particles, and the area below the threshold is taken as pores, and then the two-dimensional image is binarized, and a binary image under this threshold condition can be obtained.  
To ensure accurate quantification of microstructural parameters, we established a grayscale threshold selection protocol. The threshold was determined by calibrating binarized SEM image porosity against experimentally measured values. Through systematic analysis of multiple SEM images from the same sample, we confirmed the consistency and reproducibility of our threshold selection approach (Figure 13). For instance, in the sample with 2.5% NaCl concentration, where initial porosity measured 0.4136, a threshold value of \(Z_{i}\) = 68 produced a calculated porosity of 0.4138-representing less than 1% deviation (Figure 13). This validated threshold was subsequently applied for all microstructural parameter analyses. Notably, due to inherent variations in SEM imaging conditions across specimens, we implemented sample-specific threshold determination to maintain measurement accuracy.  
Figure 12: SEM images (1000\(\times\) magnification) of marine samples at varying NaCl concentrations. (**a**) 0% NaCl; (**b**) 1% NaCl; (**c**) 2.5% NaCl; (**d**) 3.5% NaCl; (**e**) 5% NaCl.
# Integrated Module Antenna for Automotive UWB Application
## 2 Proposed Antenna Design  
### Considerations for Antenna Design  
Before designing the antenna, the available space of the module substrate, antenna case, and the antenna's actual implementation on the vehicle must be considered. For UWB localization, an antenna with wide coverage and low fluctuation in the horizontal plane is required. In addition, a sufficient bandwidth for the automotive UWB band is required. The target frequency range is 6.25-8.25 GHz, covering channels 5 and 9 of the automotive UWB band. However, an antenna with a relatively narrower beamwidth and greater gain is required for rear passenger detection sensor. The target frequency band is 7.75-8.25 GHz, covering channel 9 of the automotive UWB band.  
The proposed antenna was designed in a compact single multi-layered substrate that is primarily used for UWB modules, as shown in Figure 1. The substrate comprised four layers of 4-copper and three layers of FR-4 dielectric (\(\varepsilon_{\rm r}=4.3,\ \tan\delta=0.025\)). The thickness of the substrate was 1.156 mm. The area available for antenna design measured 35 \(\times\) 20 mm\({}^{2}\).  
The geometry of the antenna case is shown in Figure 2. The dielectric constant of the antenna case material is 2.8. The distance between the top of the case and the 1 st layer of substrate is 7 mm, and the distance between its bottom and the 4 th layer is 5 mm.  
For the localization antenna, four antennas were mounted at the corners of the vehicle. On the front end, two antennas were mounted under the headlamp in the engine room, and the others were mounted behind the rear bumper of the rear part of the vehicle, as shown in Figure 3. Meanwhile, the rear passenger detection sensor antennas were mounted on the upper ceiling of the rear headrest in the vehicle, as shown in Figure 4. Considering the mounting axis of the antenna in the vehicle, a wide coverage in the yz-plane with low fluctuation was required for the localization antenna. For the rear passenger detection sensor antenna, a directive radiation pattern in xz-plane with 90\({}^{\circ}\) beamwidth for sufficient coverage was required.  
Figure 1: Configuration of the substrate of the proposed antenna.  
Figure 2: Configuration of the antenna case.
# Evaluation of the GPS Precise Orbit and Clock Corrections from MADOCA Real-Time Products
## 1 Introduction
Figure 1: The ground stations for the MADOCA real-time ephemeris generation.
## Materials and Methods
### Structures of the Three Spatial-Temporal Components
#### 3.3.2 Global Correlated Spatial Module
Thus, the input signal of the graph convolution is a feature matrix \(\mathbf{X}_{t}=[\mathbf{x}_{1,t},\mathbf{x}_{2,t},...,\mathbf{x}_{N,t}]^{T}\in\mathbb{R}^{N \times C}\) and the result of graph convolution is as follows:  
\[\mathbf{\hat{X}}_{t}=\sum_{k=0}^{K-1}\theta_{k}T_{k}(\widetilde{\mathbf{L}})\mathbf{ X}_{t} \tag{9}\]  
where \(\mathbf{\hat{X}}_{t}=[\mathbf{\hat{x}}_{1,t},\mathbf{\hat{x}}_{2,t},...,\mathbf{\hat{x}}_{N,t} ]\in\mathbb{R}^{N\times D}\), and \(D\) is the number of the output channels. It is worth noting that the convolution results contain the feature information of K-order neighbors, and only capture the local spatial correlation of the road network structure. **Global Correlated Spatial Mechanism:** To model the global spatial correlations between different nodes in the road network, a global correlated mechanism is proposed, as depicted in Figure 4. The formula for computing global correlations is as follows:  
\[\mathbf{y}_{i,t}=\sum_{\forall\varphi_{j}\
eq\varphi_{i}}s_{i,j}\cdot\phi(\mathbf{ \hat{x}}_{i,t},\mathbf{\hat{x}}_{j,t})\cdot\mathbf{\hat{x}}_{j,t}W_{g}+\mathbf{\hat{x}}_{ i,t}W_{r} \tag{10}\]  
where \(\mathbf{y}_{i,t}\in\mathbb{R}^{F}\) represents the output feature of the \(i\)th node at timestamp \(t\). Considering whether there is an edge between the \(i\)th node and the \(j\)th node in the road network, if there is an edge, \(s_{i,j}=\alpha>1\), else \(s_{i,j}\)=1. \(s_{i,j}\) represents the static global topological weights. In Equation (10), \(\phi\) is the Gaussian kernel \(\phi(\mathbf{x},\mathbf{y})=exp(\mathbf{x}^{T}W_{\phi}\mathbf{y})\), measuring the correlations between two node embedding representations, where \(W_{\phi}\in\mathbb{R}^{D\times D}\) is the learnable parameter. \(\sum_{\forall\varphi_{j}\
eq\varphi_{i}}s_{i,j}\cdot\phi(\mathbf{\hat{x}}_{i,t}, \mathbf{\hat{x}}_{j,t})\cdot\mathbf{\hat{x}}_{j,t}W_{g}\) represents the impact of all other nodes on the \(i\)th node in the spatial dimension and \"\(+\mathbf{\hat{x}}_{i,t}W_{r}\)\" denotes a residual connection with the localized output features of the \(i\)th node, with \(W_{r}\in\mathbb{R}^{D\times F}\) and \(W_{g}\in\mathbb{R}^{D\times F}\) the learnable parameters. The output of the global correlated mechanism at timestamp \(t\) is \(\mathbf{Y}_{t}=[\mathbf{y}_{1,t},\mathbf{y}_{2,t},...,\mathbf{y}_{N,t}]^{T}\in\mathbb{R}^{N \times F}\), and the final output feature matrix of the spatial module is \(Y\in\mathbb{R}^{N\times T\times F}\).
# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 4 Experimental Verification and Analysis
### Ablation Experiment  
To find the influence of each module on the performance of the model, an ablation experiment is used to evaluate the impact of the graph perception module and the domain discrimination module on the performance of fault diagnosis. Three models, the GNN, DANN, and MPGCN, are set up as controls. Results of the ablation experiment are shown in Figure 10.  
The comparison reveals that the incorporation of both the graph generation module and the graph perception module significantly enhances the diagnostic performance of the proposed method.  
Figure 9: Comparison of time consumption and diagnostic accuracy of MPGCTN with other methods.
## 3 Results
### Test 3  
The following test was designed to determine the distance between the transmitter and receiver at which the data rate drops below the required amount and when the connection is lost. In the case of a connection lost at distances under 5 km, the system was considered to be unsatisfactory for our scenario. The measurement procedure was the same as for Test 2. Some data link parameters, however, were changed: the wireless distance parameter was set to its maximal value--3000 m.  
Figure 9 presents the measured throughput results for various distances between the ground station and the mobile transmitter. As expected, the throughput decreases with distance. The minimal measured throughput value was 1.4 MB/s, which can be considered satisfactory. The second requirement--a minimum 5-km connection distance--was not met because the connection was lost at 1.1 km. This was likely due to the low height at which the transmitter and receiver were positioned (1.5 m). Therefore, a subsequent test with the transmitter and receiver positioned at more realistic heights was required.  
Figure 8: Test 2—Throughput results.  
Figure 9: Test 3—Measured throughput (transmitter and receiver at a 1.5-m height).
# Nutritional Support for Liver Diseases
## 1 Introduction  
Chronic liver disease (CLD) is the progressive deterioration of liver function lasting more than six months [1]. CLD can be divided into infectious (caused by viral infection-Hepatitis B and C) and non-infectious (i.a. alcoholic liver disease, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), autoimmune hepatitis, Wilson disease, and hereditary hemochromatosis). The causes of non-infectious liver diseases include alcoholism, metabolic disorders, toxins, and autoimmune and genetic disorders. Both infectious and non-infectious liver diseases can lead to liver fibrosis and cirrhosis. The stages of the development of chronic liver disease are hepatitis or steatosis or hepatosteatosis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC) [1].  
It is estimated that more than 844 million people worldwide suffer from chronic liver disease, resulting in approximately 2 million deaths per year [2]. The most common cause of chronic liver disease is non-alcoholic fatty liver disease (NAFLD) [3]. It has been estimated that 25% of the world and 90% of the obese population have some degree of NAFLD. By contrast, the prevalence of its active stage, non-alcoholic steatohepatitis (NASH), is around 3-5% [3].  
The liver is a key organ in which the numerous metabolic processes of proteins, carbohydrates, and fats take place; therefore, it is responsible for maintaining proper nutritional status. In the advanced stage of the disease, there is a malabsorption of fats, deficiencies in fat-soluble vitamins, a reduction in the level of water-soluble vitamins, and changes in micronutrient metabolism. Thus, the dysfunction of this organ leads to malnutrition and sarcopenia [4]. In patients with liver disease, their diet and nutritional status is an important therapeutic and prognostic factor [5].  
In general, patients with chronic liver disease are advised to eat a healthy diet that is varied in foodstuff, devoid of ultra-processed industrialized food, sugar-sweetened beverages, and high-fat food [6]. Recommendations regarding the intake of both macro- and micronutrients are dictated by supplementing the deficits in these ingredients, resultingfrom the specificity of the disease (absorption disorders, metabolic disorders, e.g., impairment of carbohydrate, protein, and lipid metabolism) or the support of liver function (e.g., the improvement of liver enzymes) [5].  
Alcohol should definitely be excluded because ethanol and/or its metabolites cause metabolic, biochemical, and molecular disorders not only in the liver but also in skeletal muscles, which, in turn, leads to impaired proteostasis [7].  
The goal of treatment is to stop the progression of the disease and its complications, which requires a multidisciplinary approach, including nutritional support [1]. This review focuses on providing key dietary recommendations to patients with NAFLD, cirrhosis, and cholangiocarcinoma, as well as liver transplantation.
## 4 Results and Discussion
### Phase 2  
The proposed framework's phase 2 focuses on the classification of emotions through supervised ML techniques. We have used various ML techniques to compare their performance with each other to pick the best one. To have a fair comparison among the adopted ML techniques we have tuned the parameter of ML techniques through five-fold grid search cross-validation. Wherein grid-based search cross-validation considers all parameter combinations to select the best set among them. During cross-validation, the collection of data is divided into k folds (groups) that are roughly similar in size. The estimator (ML model) then trains on the remaining k-1 folds using the first fold as a validation set. Following are the tuned parameters for all ML techniques--SVM and LogReg (regularization parameter C = 0.01), KNN [number of neighbor K = 3], RF [number of trees = 100], DT {max_depth = 50}, MLP [learning rate = 0.001, batch size = 32, hidden layer size = 2], AdaBoost {number of the base estimator or n_estimator = 50}, and NBs {versmoothing = 0.4 and alpha = 0.7}. Table 3 shows the various ML model evaluation measures, wherein MLP outperformed all other ML techniques based on the high accuracy and recall scores, 95% each. However, logistic regression is closely competent to MLP but neural network-based techniques like MLP have the potential to train on large datasets than the traditional ML technique such as logistic regression as demonstrated by [77] as well.  
Moreover, as the ML technique's accuracy is an indicator of the successful classification of all emotion classes, this also provides us an overview of the unsuccessful prediction of emotion classes. In other words, the wrong or unsuccessful prediction rate of our optimum ML technique (i.e., MLP) is 5%. This means that in general 5% of test data were predicted or classified to the wrong emotion classes (e.g., a tweet that belongs to happy emotion was classified to sad category). Similarly, the logistic regression technique has classified 5% of tweets into wrong categories. The potential way to get optimum 100% accuracies would be to accommodate more similar data with further parameter fine-tuning (as we did).
## 3 Results  
### Phylogenetic Inference  
Preliminary analyses carried out individually with nrITS, nrSSU, nrLSU, and RPB1 revealed no incongru fluence in the topology of the single-locus trees. The combined four-markers dataset-built based on BLASTn results and of recent phylogenetic studies [30; 41]--Consisted of 42 taxa (including MUT isolates) that represented 9 genera and 20 species (Table 1). A total of 82 sequences (9 nrITS, 13 nrSSU, 13 nrLSU, 13 TEF-1\(\alpha\), 13 \(\beta\)-TUB, 11 RPB1, and 10 RPB2) were newly generated while 91 were retrieved from GenBank.  
The dataset combining nrSSU, nrITS, nrLSU and RPB1 had an aligned length of 2431 characters, of which 1530 were conserved, 263 were parsimony-uninformative and 638 parsimony informative (TL = 2192, CI = 0.550901, RI = 0.758483, RC = 0.417849, HI = 0.449099). The strains under investigation, MUT 1587, MUT 1938, MUT 1950, MUT 1954, MUT 1961, MUT 5040, MUT 5048, MUT 5049 and MUT 5082 formed a strongly supported monophyletic lineage (BYPP = 1.00; MLB = 100%) close but well set apart from Corollospora martitima (Figure 1). Within this new group, MUT 1954 and MUT 1961 seemed to form a separated clade, as well as MUT 5040 and MUT 5049. However, the relationships between the taxa were unclear.  
The additional dataset, implemented with the addition of TEF-1\(\alpha\), \(\beta\)-TUB, and RPB2 sequence data relative to the novel lineage and to Corollospora martitima only, had an aligned length of 4086 characters, of which 3693 were constant, 236 were parsimony-uninformative and 157 parsimony informative (TL = 452, CI = 0.843537, RI = 0.900217, RC = 0.759367, HI = 0.156463). Two clusters could be observed: the first grouped MUT 1954 and MUT 1961 while the second one was further split into two subclades that separated MUT 1587, MUT 1938, and MUT 5049 from MUT 5040, MUT 5048, and MUT 5082. Finally, MUT 1950, although included second cluster, was not part of any of the two subclades (Figure 2).  
Figure 2: Phylogenetic inference based on a combined nrITS, nrSSU, nrLSU, RPB1, RPB2, TEF-1\(\alpha\), and \(\beta\)TUB dataset. The tree is midrooted. Branch numbers indicate BYPP and BS values; \({}^{\rm T}\) Type Strain; Bar = expected changes per site (0.004).