esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Studies  
### Test System and Data  
The proposed bidding strategy of GFUs in the presence of P2G conversion technology and WPU is modeled as a mixed-integer linear programming (MILP) problem. Therefore, this optimization problem was solved by utilizing CPLEX 12.9 solver in GAMS 27.3 on a laptop with an Intel Core i7 processor with 2.50 GHz and 8 GB of RAM. As indicated before, the presented hybrid energy system consists of a GFU, a WPU, P2G conversion technology, and GSS facilities; the limitation of daily gas fuel consumption is considered, as well. All required parameters of the given equipment are reported in Table 2. The electricity day-ahead market price is shown in Figure 3, and the price of natural gas is about 2.9 $/MBtu. The operation costs of the P2G facility and GSS unit are taken from References [15; 26]. The maximum operating capacity of the transmission line between the hybrid energy system and the energy market is 150 MW, and the maximum allowed daily gas fuel consumption by GFUs is 13,955.640 MBtu [12; 52]. The nominal capacity of WPU is precisely 180 MW; the other technical parameters are depicted in Table 3. The forecasted WPU, along with dispatchable power and analysis of them, is provided in Section 4.2.
## 3 The Time Source Techniques
### Low Earth Orbit
#### 3.2.4 Reliability and Security  
PNT service by LEO is designed to be immune to certain GPS vulnerabilities [50]. Efforts are underway to address robustness and enhance services.  
LEO satellites are designed with a focus on precision, with plans to introduce signal encryption to enhance resilience against malicious attacks like spoofing. Additionally, overlapping beam patterns formed by signals from multiple satellites reduce the risk of signal loss due to spoofing, as these patterns are challenging to replicate [55].  
The raw signal power received from LEO satellites on Earth is approximately 300 to 2400 times stronger than GPS signals, which makes LEO less susceptible to jamming and interference. In contrast, GPS signals suffer significant attenuation due to distance-related path losses during their transmission from satellites to the ground [52]. This increased signal strength also allows LEO signals to be received indoors, making them suitable for use with indoor antennas.
###### Abstract  
The \(\mathrm{HO_{2}}\) nanoparticles and the nanocomposites of \(\mathrm{HO_{2}}\)-graphene (10, 30, and 50 wr%) were prepared via precipitation and simple mixing method. The XRD pattern confirmed the presence of monoclinic \(\mathrm{HO_{2}}\) and hexagonal graphene in the nanocomposite. Raman spectroscopy studies revealed the formation of \(\mathrm{HO_{2}}\)-graphene nanocomposite. According to SEM and TEM images the \(\mathrm{HO_{2}}\), NPs are spherical, and their size is less than 10 nm, anchored on the surface of the graphene sheets. The EDX spectrum shows carbon, oxygen, and \(\mathrm{HO_{2}}\) and reveals the formation of the \(\mathrm{HO_{2}}\)-graphene nanocomposite. The UV-vis absorption spectra show the optical properties of synthesized \(\mathrm{HO_{2}}\)-graphene nanocomposite. The study examines the influence of different ratios of the addition of graphene on the photocatalytic activity of \(\mathrm{HO_{2}}\)-graphene. It was found that the \(\mathrm{HO_{2}}\)-graphene (50 wr%) 40 mg nanocomposite exhibits enhanced photocatalytic activity than the bare \(\mathrm{HO_{2}}\) towards the methylene blue photodegradation, an aromatic pollutant in water under UV light irradiation, which can be applied optimally for individually wastewater management system. The \(\mathrm{HO_{2}}\)-graphene (50 wr%) photocatalyst degrades 81 \(\pm\) 2% of tetracycline in 180 min, implying that tetracycline can be degraded more efficiently under UV light. The enhancement in photocatalytic activity under UV light illumination can be attributed to the effective separation of photogenerated electrons, inhibiting recombination in the \(\mathrm{HO_{2}}\)-graphene composite.  
Keywords:\(\mathrm{HO_{2}}\); graphene; photodegradation; nanocomposites; tetracycline +
Footnote †: journal: Applied Sciences
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
### Resource Management Layer
#### 5.4.4 Performance Management  
It evaluates and reports on the behavior of telecommunications devices and the overall network, measuring their effectiveness. It additionally gathers and examines statistical data to both monitor and optimize the performance of the network and its individual components.
## 2 Full-Scale Measurement  
### Sensor Arrangement and Data Obtained Onboard  
The target ship of our full-scale measurement is an 8600 TEU container ship with overall length of approximately 335 m, breadth of 45.6 m, depth of 24.4 m, and maximum service speed of 24.5 kt, as listed in Table 1. The stresses were measured at 12 points, as shown in Figure 1, and the details are included in Table 2. In this study, we employed the stresses measured at the midship four corners (shown in blue points) for further analysis. The sampling frequency of the stress measurement was 20 Hz. In addition to the stress data, the ship's speed and direction were recorded in the onboard monitoring data.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 6 Enablers for 6G and Relevant Technical Preparation and Implementation
### Holographic Beamforming  
Beamforming [63] is a technique that utilizes a focused narrow beam with a very high gain for transmitting and receiving utilizing antenna arrays. It does this by concentrating the power in a small angular range. Beamforming improves coverage and throughput while also increasing the signal-to-interference-plus-noise ratio (SINR), which may be used to monitor people. HBF, as shown in Figure 6, is a more advanced beamforming technique that makes use of a software-defined antenna (SDA). RF signals from the radio are routed through an optical hologram to the back of the antenna (a holographic plate) before the beam's shape and direction are adjusted to reflect these tiny elements [64]. Using HBF allows 6G systems and IoT devices to share radio frequencies in a flexible and efficient manner [65; 66]. Moreover, HBF increases the position accuracy in 6G networks and IoT up to centimeters [67].
## 2 Related Works
### Evolutionary Power Spectral Density (EPSD) Estimation of the Non-Stationary Turbulence  
The wavelet transform, which can capture both the low-frequency and high-frequency information using different scale windows, was selected to calculate the EPSDs of the measured turbulence in Figure 2[23]. In addition, the filtered harmonic wavelet (FHW), which is the updated version of the generalized harmonic wavelet, was selected to estimate the EPSD herein [24]. The EPSD \(S\big{(}\omega_{{}_{P}t}\big{)}\) for FHW scheme can be given by:  
\[S\big{(}\omega_{{}_{P}t}\big{)}=\frac{4\mathbb{E}\bigg{[}\bigg{|}_{F(m,n),k} \bigg{|}^{2}\bigg{]}}{n-m} \tag{1}\]  
where \(c_{F(m,n),k}\) is the FHW transform coefficients. Equation (1) defines the local spectrum in the time and frequency regions.  
\[m2\pi\leq\omega\leq n2\pi\;and\;\frac{k}{n-m}\leq t\leq\frac{k+1}{n-m} \tag{2}\]  
The EPSDs of the measured turbulence in Figure 1(b,d) were estimated using the FHW with a total 2000 of frequency segmentations [25], as shown in Figure 3. The reliability of the estimated EPSDs was validated by comparing the averaging EPSDs with the power spectral density (PSDs) calculated by the Pwelch method [26]. Here, the averaging EPSD can be obtained by averaging the EPSD over time.  
Figure 2: Field measured wind data during Typhoon Wipha.  
The estimated EPSDs in along-wind and vertical directions are shown in Figure 3a,c, respectively. For clarity, the EPSDs with frequency over 2 Hz are omitted due to the relatively small values in Figure 3a,c. It is seen that the non-stationary characteristics of the measured turbulence are captured properly by the FHW scheme. Meanwhile, the averaging EPSDs agree quite well with the PSDs in Figure 3b,d, which indicates that the estimated EPSDs are accurate and consistent with the analytical theory [27].  
The Updated Non-Negative Matrix Factorization (NMF)-Based Fast Fourier Transform (FFT)-Aided Spectral Representation Method (SRM)
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 7 Simulations and Results
### Variable Threshold SINRs of CUEs  
By varying the SINR threshold, we look at the V2P link capacity under a variable vehicular speed. At high speeds the V2P Capacity decreases. An SINR of 5 dBs is therefore chosen as the base threshold, since the capacity is higher compared to other SINR thresholds as shown in Figure 7.
## 7 Conclusions  
Epileptic seizure detection is crucial for providing timely intervention, improving patient safety, and enhancing the quality of life for individuals living with epilepsy. This paper introduced an advanced epileptic seizure detection model that addresses key challenges in existing methodologies, including data imbalance and feature selection. Traditional approaches often suffer from misdiagnosis and delayed detection, affecting patient outcomes. By integrating SMOTE for dataset balancing and a hybrid feature selection technique (PCA + DWT), our proposed model enhances the reliability of seizure classification using EEG signals. Experimental results demonstrated significant performance gains, with the SVM classifier achieving 97.30% accuracy, 99.62% AUC, and 93.08% F1 score, surpassing existing methods. The proposed approach can serve as a robust decision-support tool for neurologists, reducing the risk of delayed or inaccurate diagnoses. A comparative evaluation with prior studies highlights the model's effectiveness in optimizing feature extraction, dimensionality reduction, and classification performance. Unlike patient-specific approaches, our proposed model enhances generalization while mitigating computational inefficiencies. While the results are promising, future work will focus on enhancing real-time implementation, optimizing computational efficiency, and exploring deep learning architectures to improve generalization across diverse datasets.  
Conceptualization, H.F.A. and G.E.A.; methodology, H.F.A.; software, G.E.A.; validation, H.F.A., G.E.A. and M.S.N.; formal analysis, H.F.A.; investigation, G.E.A.; resources, M.S.N.; data curation, G.E.A.; writing--original draft preparation, H.F.A. and G.E.A.; writing--review and editing, M.S.N.; visualization, M.S.N.; supervision, H.F.A.; project administration, H.F.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original data presented in the study are openly available in [[https://www.ukbonn.de/epileptictopologie/arbeitsgruppen/ag-lehnertz-neurophysik/downloads/](https://www.ukbonn.de/epileptictopologie/arbeitsgruppen/ag-lehnertz-neurophysik/downloads/)]([https://www.ukbonn.de/epileptictopologie/arbeitsgruppen/ag-lehnertz-neurophysik/downloads/](https://www.ukbonn.de/epileptictopologie/arbeitsgruppen/ag-lehnertz-neurophysik/downloads/)) (accessed 28 March 2025).  
The authors declare no conflicts of interest.
# Biocompatible Dispersants for Carbon Nanomaterials
## 4 The \(\eta\) and \(\eta^{*}\) Metrics  
The efficiency of a surfactant can be described in terms of \(C_{s,max}\), which is the surfactant concentration needed to reach maximum nanoparticle dispersion. Dispersion effectiveness (\(\eta\); Equation (1)) is a measure of the ability of a surfactant to disperse a given amount of hydrophobic material, where \(C_{CNM,in}\) is the initial undispersed carbon nanomaterial (CNM) concentration and \(C_{CNM,max}\) is the highest concentration of a dispersed CNM. The \(\eta\) and \(\eta^{*}\) metrics:  
\[\eta=\frac{C_{CNM,max}}{C_{CNM,in}}\times 100 \tag{1}\]  
Dispersion efficiency (\(\eta^{*}\); Equation (2)) is a more thorough representation of dispersants' abilities, as an efficient dispersant may still have a high \(C_{s,max}\).  
\[\eta^{*}=\frac{\eta}{C_{s,max}} \tag{2}\]  
Table 1 displays a range of \(\eta\) and \(\eta^{*}\) values, obtained from the supplementary information section of a paper written by [PERSON] et al. [27]. The authors of this study utilised a range of surfactants, including sodium dodecylbenzene sulphonate (SDBS), sodium dodecyl sulphate (SDS), and hexadecyltrimethylammonium bromide (CTAB), to disperse single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) in water.  
A high \(\eta^{*}\) value suggests that either (a) the repulsion forces between surfactant functionalised CNMs is high, (b) a large portion of the CNM surface area is covered in surfactant, (c) a high percentage of surfactant is adsorbed onto the surface of the CNM, or (d) some synergistic combination of these factors. It should be noted that the \(\eta^{*}\) metric is not a comprehensive description of a dispersant's abilities, as there are many factors it does not account for. For example, the stability of the final dispersion is not considered; neither is the molecular mass of the surfactant molecule itself. Different CNM types will yield different values due to the varying mass-to-surface area ratios of different nanomaterial forms. As seen in Table 1, it was found by [PERSON] et al. that MWCNT systems displayed higher \(\eta^{*}\) values than their SWCNT counterparts due to the different mass-to-surface area ratios of the two CNMs [27].
## 3 Results  
In this section, we present the achieved experimental results of PTG-PLM. PTG-PLM is performed on the Ngly and Kgly datasets for glycosylation/glycation site prediction. Each dataset is divided into three sets: training, validation, and testing set. The number of samples in each dataset was described in Section 2.1. The training sets are used for training the models as well as evaluating using the cross-validation method. The validation sets are used by the CNN model to adjust its hyperparameters and weights during the training process. In contrast, the testing sets are used for independent testing and comparing PTG-PLM with the other machine learning techniques as well as comparing with the existing tools of glycosylation/glycation site prediction. Six recent PLMs including ProtBERT-BFD, ProtBERT, ProtALBERT, ProtXLNet, ESM-1b and TAPE are employed to encode peptide (samples) sequences. PLMs extract features from the peptide sequences based on the tokenization and embedding strategies. The description of PLMs was clarified in Section 2.2. PTG-PLM is performed using each PLMs individually. The PLMs embedding and CNN model training are implemented using Python 3.7.13 on Google Colab Pro  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **Parameter** & **Range of Values** & **Used Value** \\ \hline \multirow{4}{*}{SVM} & kernal & linear, poly, rbf, sigmoid & rbf \\  & C & \(1\times 10^{-1}\),..., \(1\times 10^{6}\) & 1 \\  & gama & scale, auto & scale \\ \hline LR & penalty & L1, L2 & L2 \\ \hline RF & n\_estimators & 100 to 1000 & 100 \\ \hline \multirow{4}{*}{XGB} & booster & gbfree, gblinear & gbfree \\  & learning\_rate & 0.01, 0.02,..., 0.1 & 0.1 \\ \cline{1-1}  & max\_depth & 2 to 10 & 5 \\ \cline{1-1}  & min\_child\_weight & 1 to 9 & 5 \\ \cline{1-1}  & subsample & 0.2, 0.3,..., 0.9 & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters setting for the used machine learning classifiers and the used value for each parameter.  
([[https://colab.research.google.com/](https://colab.research.google.com/)]([https://colab.research.google.com/](https://colab.research.google.com/)) (accessed on 10 February 2022)) with GPUs (RAM 16g) and high RAM (28G).
# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 3 Results  
### Benchmark Dataset  
#### 3.1.1 CamVid Dataset  
Semantic segmentation models based on deep learning require a large number of datasets for model training, and the datasets used vary in the face of different semantic segmentation tasks; for instance, in the semantic segmentation task of medical images, 3D medical image datasets are often used. The CamVid dataset [28] can be used to meet the needs of this paper, which requires time series images or video datasets. This is an RGB street view dataset developed and labeled by the University of Cambridge. The CamVid raw dataset contains a total of 701 photos with a resolution of 720 \(\times\) 960, containing a total of 32 raw categories that can be grouped into 11 major categories, including vehicles, pedestrians, roads, and buildings. The dataset, consisting of a series of sequential images, was taken under different lighting conditions and was taken while the car was in motion, which is well suited to the environment of autonomous driving use. It can meet the dataset needs of this paper. We followed the official CamVid data divisions of training set, validation set, and test set. The number of images in the training set, validation set, and test set are 367, 101 and 233, respectively. Figure 6 shows an example of the comparison between the original image and the labeled image.  
Figure 6: Sample of the CamVid dataset.
## 4 Results
### Evaluation of Interpretability and Usability for Decision-Making Results  
#### 4.4.1.  
In evaluating the clarity of the methods, experts provided insights specific to each case study. For Case Study A in the logistics industry, the initial AHP-QFD matrix identified and prioritized key site selection attributes, including operating handling costs, information technology status, and proximity to airline agents and customers. These attributes were complex and required detailed analysis, making it difficult for decision-makers to interpret the results clearly. However, after integrating PCA, 75% of the experts found Method 2 to be clearer. The PCA process simplified the complexity by reducing redundancy and highlighting the most critical components, making the evaluation process more straightforward and easier to understand.  
In Case Study B for new earphone development, the initial AHP-QFD matrix outlined numerous technical requirements for the product, such as material, structure, and wireless capabilities. The detailed technical specifications made the process intricate and harder to interpret. With PCA integration, 75% of the experts found Method 2 clearer. The PCA helped simplify these detailed specifications by focusing on the most influential factors, thus enhancing the clarity of the decision-making process.
## 3 Methodology
### Machine-Learning Methods
The decision tree algorithm, often depicted as a tree-like flowchart, presents various outcomes of sequential decisions. It aids in classification, decision making, and analysis by representing potential decisions, outcomes, or reactions. The tree structure comprises three main elements: the root node, leaf nodes, and branches. The root node serves as the starting point, with both root and leaf nodes featuring a categorization criterion. Branches link the nodes, indicating different category paths. During tree formation, each node's split tends to enhance the order and purity of information within the subset. Purity can be quantitatively measured by three mainstream methods, namely information gain-based trees (ID3 algorithm), information gain ratio-based trees (C4.5 algorithm), and Gini index-based trees (CART algorithm). The CatBoost [39] is a novel classification learning algorithm within the Boosting family. It stands alongside XGBoost [40] and LightGBM [41] as optimizations of the GBDT [42] framework. While XGBoost remains widely prevalent in general applications, LightGBM notably enhances computational efficiency. Conversely, CatBoost excels by addressing issues like gradient bias, prediction shift, and effectively preventing overfitting. This innovative solution significantly improves classification accuracy while concurrently enhancing generalization capabilities. CatBoost's prowess in mitigating these challenges positions it as a compelling choice for algorithmic comparisons, making it a pivotal consideration in this study. The multilayer perceptron (MLP), a type of artificial neural network (ANN), comprises an input layer, multiple hidden layers, and an output layer. In this architecture, the input layer vector is X, and the output of the hidden layer is calculated using \(f(w_{i}X+b_{i})\), while \(w_{i}\) signifies the weight of the fully connected layer, \(b_{i}\) denotes the bias, and \(f\) stands for the chosen activation function. Typically, nonlinear functions such as the sigmoid, tanh, or ReLU functions are preferred as activation functions. Linear functions are unsuitable as activation functions as they result in a linear combination of vectors, irrespective of the number of hidden layers. Among these, the ReLU function is commonly chosen in practice due to its effectiveness, while the output values of other activation functions have certain range limitations. To obtain optimal values, a gradient-descent approach is commonly employed. Initially, all weights and biases are initialized, followed by iterative training that stops upon reaching a maximum number of iterations or when the error becomes sufficiently small. The gradient is computed, and subsequent updates are made to the weights and biases to converge towards the optimal values.
# Performance Evaluation of LoRa Considering Scenario Conditions
## 2 Related Work
By using this methodology, we identify the most proper configuration for LoRaWAN PHY layer parameters in order to reach the best performance in each type of scenario. Besides, a comparison of the attained experimental results with a theoretical propagation model is also presented. A similar approximation was considered in [18] but, in this work, the attained coverage range was compared with the predictions given by the simple Free-Space model. This model is known to be inadequate to predict path loss in complex scenarios like those with the presence of obstacles. For that reason, in the present work, we make use of the predictions provided by a network-planning tool employing the widely used Okumura-Hata propagation model over realistic topographic maps.
###### Abstract  
This study uses Artificial Neural Networks (ANNs) and multiple linear regression (MLR) models to explore the relationship between gait dynamics and the metabolic cost. Six nonlinear metrics--Lyapunov Exponents based on [PERSON]'s algorithm (LyER), Detrended Fluctuation Analysis (DFA), the Approximate Entropy (ApEn), the correlation dimension (CD), the Sample Entropy (SpEn), and Lyapunov Exponents based on [PERSON]'s algorithm (LyEW)--were utilized to predict the metabolic cost during walking. Time series data from 10 subjects walking under 13 conditions, with and without hip exoskeletons, were analyzed. Six ANN models, each corresponding to a nonlinear metric, were trained using the Levenberg-Marquardt backpropagation algorithm and compared with MLR models. Performance was assessed based on the mean squared error (MSE) and correlation coefficients. ANN models outperformed MLR, with DFA and Lyapunov Exponent models showing higher R\({}^{2}\) values, indicating stronger predictive accuracy. The results suggest that gait's nonlinear characteristics significantly impact the metabolic cost, and ANNs are more effective for analyzing these dynamics than MLR models. The study emphasizes the potential of focusing on specific nonlinear gait variables to enhance assistive device optimization, particularly for hip exoskeletons. These findings support the development of personalized interventions that improve walking efficiency and reduce metabolic demands, offering insights into the design of advanced assistive technologies.  
nonlinearities; metabolic cost; artificial neural networks; multiple linear regression; biomechanics; gait human movement analysis +
Footnote †: journal: Applied sciences  
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