esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The dynamic performances of the proposed detailed and the proposed average models of a grid-connected MMC-controlled permanent magnet wind turbine generator have been investigated. Comparative analyses between the detailed and the average models have been performed under steady-state and transient conditions, and it has been confirmed that both models have almost the same dynamic responses and have a good controllability. Although both models can be used in the analysis of grid-connected wind farms, each model can have different purposes in applications.  
The proposed detailed model requires a small discrete time step because power electronics IGBTs and their switching phenomena are considered. The detailed model is suited to analyzing the dynamic performance in the control system design of an individual wind turbine generator in a short time simulation. The dynamic performance of an MMC's sub-modules and harmonics analyses can be handled by the detailed model.  
In the proposed average model, power electronics IGBTs converter and switching phenomena are omitted, and thus the simulation time step can be much larger than that of the detailed model. The model can be used as an individual or an aggregated model of wind turbine generators and wind farms. The model is suited to analyzing a power system with many wind generators, which cannot be analyzed using the detailed model.  
In the future, the development of control strategies to improve the dynamic performance of the MMC in controlling the power flow from the generator to the grid system will be performed. Improvement of the gain control by adopting linear or nonlinear methods would be one of the key points in the next research. Optimization methods based on Artificial Intelligence (AI) will also be considered in future research.  
Figure 23: Power output and voltage performances of the wind farm at Bus B33 under the 3 LG case: (a) Active power output of wind farm; (b) Voltage profile at Bus B33.  
Figure 24: Power output and voltage performances of the wind farm at Bus B33 under the 2 LG case: (a) Active power output of wind farm; (b) Voltage profile at Bus B33.  
**Author Contributions:** Conceptualization, M.R. and J.T.; software, M.R.; supervision, A.U., R.T. and J.T.; validation, A.U., R.T. and J.T.; writing--original draft, M.R. and J.T. All authors have read and agreed to the published version of the manuscript.  
This research has not received any external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design of Encoding Algorithm for Underwater Wireless Optical Communication Based on Spinal Code
## 3 Superposition UEP-Spinal Code Algorithm Encoding Principle
Then, \(m_{c}\) and \(m_{l}\) are rearranged into \(\widetilde{m}=\left(m_{1}^{c}m_{1}^{l},m_{2}^{c}m_{2}^{l},\ldots,m_{d}^{c}m_{ d}^{l}\right)\), where \(m_{d}^{c}m_{d}^{l}\) represents a \((k_{2}+k_{3})\)-bit sequence containing information bits from \(m_{d}^{c}\) and \(m_{d}^{l}\). Subsequently, the rearranged message \(\widetilde{m}\) is fed into a Spinal encoder with an initial hash state \(s_{0}^{l}\), following the equation  
\[s_{i}^{l}=h^{l}(s_{i-1}^{l},m_{i}^{c}m_{i}^{l}),1\leq i\leq d \tag{7}\]  
Sequentially generating \(d\) new hash states of length \(v\), \(s_{i}^{l}\) refers to the hash state of length \(v\) generated by the \(m_{i}^{c}m_{i}^{l}\) information blocks. Figure 2: Schematic diagram of the Superposition UEP-Spinal code. Step 4: The encoder generates the final encoded symbols according to the following equation:  
\[X_{i}=s_{i}^{m}\oplus(P_{i}\circ s_{i}^{l}),1\leq i\leq d \tag{8}\]  
where \(\oplus\) denotes modulo 2 addition operation; \(\circ\) denotes Hadamard product operation; \(P\) is a \(d\times v\) binary matrix containing 0 s and 1 s. \(P_{i}\) is the weight factor matrix of \(s_{i}^{l}\), and the values 0 and 1 in \(P_{i}\) indicate whether \(X_{i}\) contains the information of \(s_{i}^{l}\). The more 0s in \(P\), the higher the proportion of \(m_{m}\) information in \(X\). \(X_{i}\) is the final encoding generated by the MIB and LIB information blocks of the \(i\)-th part. The numerical sequence generated in the \(j\)-th pass is represented as  
\[X_{i,j}=s_{i,j}^{m}\oplus(P_{i,j}\circ s_{i,j}^{l}),1\leq i\leq d \tag{9}\]  
After the generation of channel input symbols is completed, the transmitter continuously sends \(x=(x_{1},x_{2},\ldots,x_{d})\) until it receives a successful decoding message from the decoder. Each channel consists of \(d\) symbols, and during the \(l\)-th channel transmission, the transmitter sends the symbols \(\left(x_{1,j},x_{2,j},\ldots,x_{d,j}\right)\).
## 2 Related Work
In their methodology, they analyzed the series of 161 patients, each with nearly 180 nights of data; from each series, they extracted the average number of hours, level, slope, variance, and autocorrelation of the series. These features are then fed into a clustering process based on dynamic analysis [23]. As a result, they identified four clusters, which they named Great Users, Good Users, Low Users, and Slow Decliners. An immediate limitation of this work, and of the work of [24] (who identified seven clusters over only 71 patients), is the need to analyze time series of similar sizes. Since patients usually have different trajectories, just a few will satisfy similar temporal constraints, severely reducing the cohort cardinality. By contrast, we identify recurrent 30-day patterns observed in the realm of a much larger cohort with over 2000 patients; this approach allowed us to work with over 20,000 pattern instances, robust support for our analysis. Another problem is that the methods of [22,24] disregard the dynamic aspect of the patients when exposed to the CPAP therapy. By contrast, we assume that the efficacy of the therapy is subject to variations over time: at the beginning of the therapy, after years of use, during a period of stress, possibly accompanied by insomnia [25]; while treating another infirmity; and so on. Accordingly, our method is designed to characterize shorter periods of 30 days or any other size as the analyst desires. Lastly, using a sliding-window method, we summarize a given patient's time series to simplify the process of inspecting the entire period of therapy. In the state of the art, we found that many works have sought to characterize OSA patients into groups, or clusters, of individuals with similar clinical signals or therapy outcomes. Since this is an unsupervised task, the various works provide sets of groups with different cardinalities and characterizations. This variability is explained by factors such as cohort peculiarities, methodology specificities, and result interpretation. Although the discrepancy is not desirable, these many works still point to an intersection of relevant aspects: (i) the patients indeed behave in a stratified manner, (ii) the characterized behaviors are interpretable from heterogeneous perspectives, and (iii) there is a meaningful aspect in characterizing patients, which has motivated the very investigation of the topic. In this work, we touch upon the same investigative line, but from the prospect of the time of CPAP use, a straight indicator of treatment adherence. We innovate by assuming that the different periods of CPAP therapy deserve, each one, a distinguished analysis; in contrast to former works that aggregate the whole therapeutic history of a given patient into a single average. With this course of action, we introduce a method able to summarize long-term CPAP use series without resorting to aggregation methods but, instead, introducing a scale of discrete numbers that hold, as referential, the most usual 30-day-long periods observed in a real-world database.
# Bifunctional Heterogeneous Catalyst for Biodiesel Production from Waste Vegetable Oil
###### Abstract  
Bifunctional solid catalysts facilitate the esterification of free fatty acids (FFA) into alkyl esters alongside the transesterification reaction, which allows for the use of waste vegetable oils with high water and FFA contents for biodiesel production. This makes the process economically viable and greener, as the waste fats and oils are readily available. The concurrent esterification and transesterification of waste palm oil (WPO) and waste sunflower oil (WSO) with methanol was investigated in the presence of calcium oxide on alumina catalyst in a conventional batch process. The catalyst characterization showed the existence of calcium oxide aluminates (calcined at 750 \({}^{\circ}\)C), which exhibited crystalline phases with porous/spongy-like particles. The high concentration of CaO in CaO/Al\({}_{2}\)O\({}_{3}\) was a favorable support material in the heterogeneously-catalyzed transesterification reactions. The optimum catalyst parameters for the production of fatty acid methyl esters (FAMEs) were observed at 65 \({}^{\circ}\)C for 4 h with a methanol-to-oil ratio of 9:1, 60% (waste palm oil, or WPO) and 80% (waste sunflower oil, or WSO), CaO/Al\({}_{2}\)O\({}_{3}\) (% wt/wt) catalyst ratio as well as 4% CaO/Al\({}_{2}\)O\({}_{3}\) concentration (% wt.) for WSO and WPO. The simultaneous esterification/transesterification reactions at optimum conditions on WPO and WSO led to high yield of FAMEs of 89, 61 and 55% for WPO and 54, 75 and 98% for WSO at catalyst ratios (wt %) of 60, 70 and 80% respectively. The use of bifunctional heterogeneous catalyst (CaO/Al\({}_{2}\)O\({}_{3}\)) with waste vegetable oil can result in high performance and the upscaling of biodiesel production.  
CaO/Al\({}_{2}\)O\({}_{3}\); biodiesel; waste palm oil; waste sunflower oil +
Footnote †: journal: Applied Physics Letters
# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
## 5 Mis-Configuration Detection Model
### Social Relationship Embedding  
Apart from the features of paper title and citations, we also leverage the co-author relationship to get the social feature of each scholar considering its sustainability [24]. The co-author information reflects social competence of different authors, which also indicates the reputation. The credit of the collaborators can enhance the reliability of the scholar. In this part, we try to obtain a social graph embedding of the scholars based on the co-author relationship.  
In order to characterize the social feature of each scholar, we build a co-author graph \(G=(V,E)\) with a node \(v\in V\) denoting a scholar and an edge \(e\in E\) representing the collaboration between two scholars. The weight of an edge represents the number of papers co-authored by the two collaborators.  
The co-author graph is constructed based on the paper lists of all the scholars in our dataset. We scan all the scholars and count the authors of the distinct 6,202,775 articles. In order to deal with duplication of paper's title, we use the tuple (title, authors and publication information) to identify a paper and the hash technique is used to accelerate the counting process. Then, we add an edge between every two authors of a paper. Finally, we obtain a graph with 170,483 nodes and 1,756,688 edges. We utilize node2 vec to get the embedding representation of each node, which is defined as the social features of a scholar. Node2 vec is a framework to learn the feature representation for nodes in the graph. It is leveraged because of its remarkable ability to deal with large-scale graphs and preserve the network neighbors of nodes. It is worth mentioning that other node embedding algorithms can also be used here.
## 5 Specific Application of Topic Identification Models and Algorithms  
Using the statistics shown in Table 1 in Appendix A, classification has been conducted to find the most frequently used topic identification models and algorithms. The Dycharts tool is used for analyzing and visualizing data (see Figure 4).  
Figure 3: Co-occurrence of keywords.  
Figure 3 shows each algorithm model and the number of times it has been applied. Topic recognition models and algorithms can be generally divided into the following four categories, denoted with different colors in the figure: (1) LDA theme model and related derivative models (accounting for 53%); (2) machine learning and deep learning models (accounting for 26%), mainly including K-means, Word2 Vec, Bert and TF-IDF; (3) text mining (accounting for 14%); (4) citation analysis (accounting for 7%). The application frequencies of each method are as follows: LDA theme model and related derivative models (46 times), machine learning and deep learning (22 times), text mining (12 times) and citation analysis (6 times). It should be noted that although LDA can also be regarded as one of the classical algorithms of machine learning, due to its unique role in the field of topic recognition, this paper classifies studies using only the LDA model into category 1. These four kinds of topic identification models and algorithms are specifically discussed in Sections 5.1-5.4.
# OpticalTrust: A Sensor-to-Blockchain Framework Using Free-Space Optical Communication
## 4 Results and Discussion
### Evaluate FSOs Design
#### 4.2.1 Analysis of Q Factor  
In optical communication networks employing EDFAs, elevated Q-Factor measurements are observed, as demonstrated in Tables 2 and 3, indicating superior signal integrity and enhanced overall system efficacy. For instance, at 10 km with 8 Gbps, the Q-Factor improves from 64.5921 to 201.041 with EDFA implementation. The presence of elevated Q-Factor values in these EDFA-based systems correlates with improved transmission fidelity, as evidenced by the BER reduction to zero across multiple distances (5-25 km) for both 8 and 12 Gbps configurations.  
EDFA is pivotal for reducing signal deterioration, particularly at longer distances. This is clearly demonstrated at 25 km, where the 16 Gbps system's Q-Factor improves from 4.68169 to 16.9683 with EDFA integration. Conversely, Q-Factor values of FSOs systems without EDFAs show significant deterioration, indicating a decline in signal quality as transmission ranges increase. For example, at 20 Gbps, the Q-Factor decreases from 6.909782 at 5 km to 3.68705 at 25 km without an EDFA, while with an EDFA, it maintains higher values ranging from 6.97469 to 6.78159 across the same distance range.
## 3 Methods
### Data Collection and Preprocessing
#### 3.1.2 Data Preprocessing  
In this study, we meticulously preprocessed the collected data from coal-fired power plant units to ensure the accuracy and reliability of subsequent analyses. The preprocessing steps mainly included checking for missing values, detecting outliers, confirming data types, and checking for duplicates, followed by data normalization before inputting them into the model.  
We first checked for missing values, as data completeness is crucial for ensuring the reliability of analysis results. Through a comprehensive examination of the dataset, we confirmed that all key fields, including power generation, power supply, heating supply, and unit emissions, had no missing values. This check ensured that each piece of data in the 372 unit records was complete, providing a solid foundation for further analysis. However, we found 45 records of unit emissions with zero values and, upon closer inspection, determined that these units did not engage in production activities during the monthly statistical period. Therefore, these data were considered irrelevant and were excluded. Based on statistical descriptive analysis, we evaluated potential outliers in the data. Specifically, we focused on the maximum and minimum values of numerical fields such as power generation, power supply, heating supply, and unit emissions to identify possible extreme cases in the data. While some extreme values were present, considering the significant differences in the collected unit data volumes, we chose to retain these records to avoid mistakenly deleting valid data points. We checked the data types of columns in the dataset to ensure they were suitable for subsequent analysis. Major numerical variables such as installed capacity, power generation, power supply, heating supply, and unit emissions were stored in appropriate numerical types, while categorical variables like unit type, cooling method, and product type were represented as object types, requiring no additional data type conversions. Finally, we checked the dataset for duplicate records. The absence of duplicates confirmed the uniqueness of the data.  
After completing the above steps, the data were normalized to ensure that numerical variables were on the same scale during the analysis, reducing the impact of differences in dimensions among variables on the analysis results. The study applied \(Z\)-score normalization to key numerical variables in the dataset, including power generation, power supply, heating supply, and unit emissions. \(Z\)-score normalization is a common data preprocessing technique designed to transform raw data into a dataset with zero mean and unit variance. The calculation of the \(Z\)-score is as shown in Equation (1).  
\[Z=\frac{(X-\mu)}{\sigma} \tag{1}\]
## 3 Methodology
### Gateway Optimization  
#### 3.5.1 Gateway Optimization Purpose Function  
The objective function defined in this study for EPSO is multi-objective, as it searches to minimize the uncovered area (or maximize the covered area) and the number of gateways to reduce implementation costs. The minimization of the uncovered area is given by \(Obj1\), as follows:  
\[Obj1=(At-Pc) \tag{7}\]  
In (7), \(At\) is the total area to be covered (all 957 points that make up the route to be served) in percentage (100%) and \(Pc\) is the coverage index in percentage (the number of points served, that is, with the RSSI value greater than or equal to the established threshold of \(-108\) dBm).  
\(Obj2\) minimizes the number of gateways required, as follows:  
\[Obj2=\ \frac{N_{g}}{N_{gt}} \tag{8}\]  
where \(N_{g}\) represents the number of gateways defined as optimal by EPSOand \(N_{gt}\) is the total number of candidate gateways.  
The development of the multi-objective function was based on the concept known as Pareto optimal front [26]. This concept seeks a balance between two or more goals.  
Figure 7: Statistical analysis of coverage radius.  
Equations (9) and (10) represent the calculations for defining the optimal Pareto fronts, where \(W\) is the average value of the \(N\) Pareto solution for \(i\) iterations of the algorithm and \(Z\) is the final equation of the multi-objective function.  
\[W=\frac{i}{N} \tag{9}\]  
\[Z=Obj1*W+(1-W)*Obj2 \tag{10}\]  
It is worth mentioning that the objective function seeks to minimize the number of gateways and the area not covered, with a Pareto front ranging from 1 to 10. As the Pareto front increases, coverage is prioritized over minimizing the number of gateways. The higher the \(W\) value, the more gateways will be used to reduce the area not covered. Conversely, smaller values of \(W\) result in fewer gateways, reducing deployment costs without guaranteeing the maximum possible coverage.
# Prediction of the Tensile Load of Drilled CFRP by Artificial Neural Network
## 4 Conclusions  
This study was carried out to determinate the influence of drilling parameters on the reduction of tensile load of CFRP. The following results were obtained:  
* The drilling parameters caused a significant change in the tensile load of the CFRP.
* For all drill bits, increasing of feed rate caused increase of delamination, surface roughness, thrust force, and loss in force, but increasing spindle speed caused decrease of them.
* The maximum tensile load of the CFRP was observed at low feed rate, and high spindle speed drilled with the WC drill bit and the lowest tensile load was observed at the high feed rate, and low spindle speed drilled with the Brad Spur drill bit.
* The drilling parameters showed similar effects on surface roughness, delamination, thrust force and reduction of tensile load. At the same time, it has been shown that these parameters have a linear relationship among themselves.
* If the correct drilling parameters are selected, the decrease in tensile load of CFRP can be up to 25%.
* The results of the ANN model are a match for the experimental results. The ANN model will help engineers to determine correct drilling parameters for maximizing tensile load of CFRP.  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper; [PERSON] checked the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
Moreover, friction losses in the gear train appear minimal; one study observed no significant difference in gross mechanical efficiency between a lever-driven push-pull system and a conventional pushrim system [33]. In addition, reducing the overall weight of the two-way propulsion system is expected to improve ease of attachment and detachment. Third, a comprehensive usability evaluation is needed. We cannot generalize the system's usability and effectiveness because the pilot test was conducted with only one participant. Further studies with a larger group of SCI users are required to determine whether diverse users can use the system comfortably and easily. In addition, it will be necessary to quantitatively examine whether the pulling mode of the system effectively activates the intended back muscles (e.g., via electromyography and fatigue analysis in future trials). Fourth, this study did not address the needs of users with high-level cervical SCI who have limited or no trunk control. For such individuals, performing a pulling motion may be challenging without additional postural support since, unlike pushing (where the backrest supports the user's trunk), pulling provides no inherent trunk stabilization. Prior research on wheelchair-adapted rowing exercise indicates that individuals with tetraplegia often require torso support (vests or chest pads) to safely generate pulling force [35]. Future iterations of the two-way propulsion system should consider optional trunk stabilization accessories (e.g., chest harnesses or supportive seating adaptations) to accommodate users with compromised trunk muscle function [35].
## 8 Application FVIM to Solve Structural Engineering Design Problems
### Experimental Setup
#### 8.1.2 The Welded Beam Design Problem
As can be seen in Table 19, the results presented indicate that FVIMDE achieves a mean value of 1.724948, a maximum of 1.725806, and an impressively low standard deviation of 0.000302, showcasing its strong performance consistency and reliability in converging towards optimal solutions. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Optimizer** & **Mean** & **Max** & **Std** & **Best** & **X1** & **X2** & **X3** & **X4** \\ \hline FVIMDE & 1.724948 & 1.725806 & 0.000302 & 1.724852 & 0.20573 & 3.470489 & 9.036624 & 0.20573 \\ FVIM & 1.726068 & 1.726818 & 0.00049 & 1.725337 & 0.2057  
FVIDDE's best value aligns closely with the lowest values obtained by other high-performing algorithms such as WSO, POA, AVOA, MFO, ROA, and HHO. These algorithms exhibit minimal variations in their performance metrics, especially in terms of standard deviation, which suggests a robustness similar to that of FVIDDE. In contrast, other algorithms such as ZOA and SCA show higher mean and maximum values, coupled with larger standard deviations. This indicates variability in their performance, which may lead to less reliable outcomes in similar optimization tasks. Moreover, algorithms like HOA and Chimp demonstrate poorer metrics with much higher mean and standard deviations, underscoring their unsuitability for this particular problem under the tested conditions.