esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Future Projects of the Production of Green Hydrogen on an Industrial Scale
### GH2 Projects--Future Facilities (2025-2050)
#### 6.1.7 H1 Magallanes--Magallanes Region  
The H1 Magallanes on-shore wind farm is a facility that will be located in the northeast of Punta Arenas, Magallanes region. It will have 367 wind turbines, the owner of which will be CWP Global. Furthermore, the height of the towers from the ground level to the hub will be in the range of 110 m. Each wind turbine will have an installed capacity of 6.0 MW, and considering all the wind turbines, the wind farm will have a total installed capacity equivalent to 2200 MW, with an estimated capacity factor of 0.57. Figure 16 shows a panoramic image of the landscape of the project site, and Table 9 provides some key technical data for this GH2 production facility.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Units** \\ \hline Location & Northeast of Punta Arenas, Magallanes Region & - \\ \hline Concession Owner & CWP Global & - \\ \hline Phase of the Project & Industrial Plant & - \\ \hline Type of Wind Farm & On-shore & - \\ \hline Number of Turbines & 367 & - \\ \hline Diameter of Turbines & 145 & m \\ \hline Hub Height & 110 & m \\ \hline Installed Capacity per Turbine & 6.0 & MW \\ \hline Total Installed Capacity & 2200 & MW \\ \hline Capacity Factor & 0.57 & - \\ \hline Equivalent Operating Hours (EOHs) & 5000 & Hours \\ \hline Source of Water & Sea Water—Desalination Plant & - \\ \hline Water Supply & 2,288,000 & m\({}^{3}\)/year \\ \hline Electrolyzer Capacity & 1760 & MW \\ \hline GH2 Production & 202,400 & Tons/year \\ \hline NH\({}_{3}\) Production & 1,135,200 & Tons/year \\ \hline CO\({}_{2}\) Avoided & 2,024,000 & Tons/year \\ \hline \end{tabular}
\end{table}
Table 9: H1 Magallanes GH2 plant main specifications.  
Figure 16: Panoramic view of the landscape in the H1 Magallanes project.  
Considering Figure 16, it is evident that the H1 Magallanes on-shore wind farm is situated near the desert part of Patagonia, where no contiguous human settlements are present.  
According to Table 9, this GH2 production plant will use seawater from a desalination plant, considering a water supply of 2,288,000 m\({}^{3}\)/year. Also, the plant will have an electrolyzer with 1760 MW of installed capacity. In this sense, the GH2 production plant will generate 202,400 tons/year of GH2, and 1,135,200 tons/year of NH\({}_{3}\). Finally, this facility will save 2,024,000 tons/year of CO\({}_{2}\) from being emitted into the atmosphere.
## 3 Design Approach and Methodology
### Application Design and Development
#### 3.2.3. Deployment Device Specifications  
The application was designed, built, and deployed for an android mobile device that supports a minimum API of 26 (Android 8.0 'Oreo') to maximum API level 30 (Android 11), using Vufforia marker-less ground plane detection. The application size is approximately 300 MB and it is internet-independent. During the development period, it was determined that the optimum (suitable) mobile screen aspect ratio is 16:9. However, the application is still functional on other screen aspect ratios.
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 4 Experiments
### Results
#### 4.4.1 \(n\)-Gram Comparison  
It is evident from Figure 6 that bigram provides a good stable performance compared with a unigram and trigram regarding the ability to capture the sentiment expressed. On the other hand, the bigram signifies an optimal result to carry out the sentiment analysis of tweets concerning extreme/radical identification. It is obvious from Figure 6 that naive Bayes' and decision tree vary with the value of '\(n\)' as compared to SVM, bagging, boosting and KNN, etc.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Classification** & \multicolumn{4}{c}{**Unigram**} & \multicolumn{4}{c}{**Bigram**} & \multicolumn{4}{c}{**Reduced Feature Set (Using PCA)**} \\ \cline{2-13}
**Algorithms** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline Naive Bayes’ & **83.16** & **82.81** & **80.2** & 81.48 & 81.05 & 79.73 & 80.84 & 80.28 & 68.06 & 67.87 & 67.70 & 67.78 \\ SVM & 80.73 & 80.65 & 77.21 & 78.89 & **84.71** & **85.18** & **83.15** & 84.15 & 74.10 & 75.00 & 73.21 & 74.09 \\ Decision Tree & 76.06 & 79.56 & 69.28 & 74.06 & 77.43 & 80.22 & 71.34 & 75.52 & 70.70 & 76.17 & 68.77 & 72.28 \\ Random Forest & 70.7 & 74.04 & 70.04 & 71.98 & 69.56 & 84.70 & 50.87 & 63.56 & **76.00** & **78.77** & **74.69** & 76.68 \\ KNN & 74.78 & 73.05 & 72.82 & 72.93 & 73.87 & 70.34 & 66.29 & 68.25 & 72.55 & 73.85 & 73.33 & 73.59 \\ Bagging & 83.12 & 82.21 & 81.01 & 81.61 & 83.66 & 81.89 & 80.01 & 80.94 & 70.17 & 73.91 & 68.44 & 71.07 \\ Boosting & 81.81 & 80.30 & 78.56 & 79.42 & 84.43 & 86.29 & 82.76 & 84.49 & 72.52 & 73.39 & 71.43 & 72.40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average score on unigram, bigram feature set and Principal Component Analysis (PCA)-based reduced feature set.
## 2 Data and Methods
### Methods  
#### 2.2.1 Correlation Analysis  
The Pearson correlation coefficient [31] is used to indicate the degree of linear correlation between two variables, with an absolute value closer to 1 indicating stronger correlation. In this study, a total of 211 data points were used to test the correlation. Considering the beneficial effect of ratios and normalized values of different features on water quality prediction [42; 43], and for the convenience of extracting effective features, this study adopts ratio and normalization methods to preprocess the spectral features. After preprocessing the features, a correlation analysis is conducted to select the ten features with the strongest correlation with total phosphorus for subsequent input into the multilayer denoising autoencoder. Effective information is extracted from these features using the multilayer denoising autoencoder, which is then utilized to train subsequent machine learning models. The water quality inversion performance of these models can also indicate the suitability of the selected features and the efficiency of the multilayer denoising autoencoder in information extraction. The superior water quality inversion performance of these models can verify whether the features selected in this study have a strong correlation with total phosphorus.  
The F-test is used to evaluate the explanatory power of each feature on the target variable. If a feature has a significant relationship with the target variable, the result of the F-test will indicate that the feature contributes to the model's prediction and can be selected as an important feature. Generally, if the \(p\)-value is less than 0.05, it means that the correlation between the feature and the target variable is significant. Therefore, this paper conducts an F-hypothesis test on the features selected by the correlation coefficient to further determine the validity of the input data for the multilayer denoising autoencoder model.
## 5 Diversity Reception in Malaga-\(\mathcal{M}\) Fading
We define the weighting function  
\[w(s)=\frac{A}{2}\ \left(\frac{b\,m}{z_{q}}\right)^{\alpha}\ \frac{\Gamma(s+\alpha)}{s!}\ \sum_{j=1}^{\beta}\ \frac{a_{j}\,\Gamma(s+j)}{b^{j}}\,\Psi\left(s+\alpha,1-j+ \alpha;\frac{b^{2}\,m}{z_{q}}\right)\,, \tag{25}\]  
and the average PER becomes  
\[ \begin{split}\overline{P_{e}}&=\frac{Q}{2\left(Q-1 \right)}\ \sum_{q=1}^{Q-1}\ \binom{Q-1}{q}\ (-1)^{q+1}\ \sum_{n=0}^{q\ (N-1)}\ \frac{c_{n}^{q}}{(1+q)^{n+N}}\sum_{i=0}^{n}\ \binom{n+N-1}{i+N-1}\ \frac{1}{q^{i}}\\ &\times\sum_{i_{1}+\ldots+i_{L}=i}w(i_{1})\ \ldots\ w(i_{L})\\ &=\frac{Q}{2\left(Q-1\right)}\ \sum_{q=1}^{Q-1}\ \binom{Q-1}{q}\ (-1)^{q+1}\ \sum_{n=0}^{q\ (N-1)}\ \frac{c_{n}^{q}}{(1+q)^{n+N}}\sum_{i=0}^{n}\ \binom{n+N-1}{i+N-1}\ \frac{1}{q^{i}}\ \binom{L}{i}_{w}\\ &=\frac{Q}{2\left(Q-1\right)}\ \sum_{q=1}^{Q-1}\ \binom{Q-1}{q}\ (-1)^{q+1}\ \sum_{n=0}^{q\ (N-1)}\ \sum_{i=n}^{(N-1)}\ \binom{i+N-1}{n+N-1}\ \frac{c_{i}^{q}}{(1+q)^{i+N}}\ \frac{1}{q^{n}}\ \binom{L}{n}_{w}\end{split}  \tag{26}\]  
where \(\binom{L}{n}_{w}\) stands for the extended binomial coefficient and is calculated using the recursion formula ([33], Equation (4))  
\[ \begin{split}\binom{L}{n}_{w}&=\sum_{s=0}^{n}\ w(s) \ \binom{L-1}{n-s}_{w}\,,\\ \binom{0}{n}_{w}&=\delta_{n}\,.\end{split}  \tag{27}\]  
Equation (26) is plotted in Figure 4 for weak fading and an increasing number of diversity branches. The figure shows that diversity may not always improve the average PER in weak fading, and that the most significant improvement is introduced with the second branch. Any further increase in the number of diversity branches may actually have little to no effect, since the fading intensity is already limited and additional noise from the amplifiers is added, especially if a large number of noise modes are allowed in the receiver. The modulation order, on the other hand, has a more pronounced
# Visible-Light CDMA Communications Using Inverted Spread Sequences
## 4 Average Light Intensity and Normalized Fluctuation  
In this section, we investigate the light intensity and the normalized fluctuation of multiplexed optical signals by computer simulation. We assume an ideal link in which the noise can be considered negligible. MPSCs and inverted MPSCs with lengths of 16 (\(q=4\)) and 64 (\(q=8\)) are employed for the six MUI cancellation schemes introduced in Sections 2 and 3. Although the maximum number of users \(N_{\text{max}}\) differs depending on the cancellation scheme, we set up \(N_{\text{L}}=8\) for \(q=4\) and \(N_{\text{L}}=32\) for \(q=8\) in all schemes. The number of active users \(N\) is set to be \(1\leq N\leq N_{\text{L}}\). In each slot, \(N\) active users are selected randomly among the zeroth to (\(N_{\text{L}}-1\))th users.  
To evaluate fluctuations in light intensity, we define a normalized fluctuation as the ratio of the standard deviation to the average intensity of multiplexed signals. Figures 5 and 6 show comparisons of the average light intensity and the normalized fluctuation, respectively. We assume that the intensity of each user's positive chip is 1, that the intensity of each user's null chip is 0, and that the probability of transmitting data \(I=0\) is equal to the probability of transmitting \(I=1\) for each user.  
Because the positive chips in the non-inverted MPSC codewords are sparse, the average intensity is low and the normalized fluctuation is high for the conventional schemes, as shown in Figures 5 and 6. In contrast, the schemes using an inverted MPSC provide higher average intensity and lower normalized fluctuation because the number of positive chips in an inverted codeword is much greater than that in a non-inverted codeword. Among the three schemes with an inverted MPSC, the EWO scheme has lower average intensity than the other two schemes. The reason is that, in the EWO scheme, the transmitted sequence is a codeword even if \(I=0\). These results show that our initial goal of the average light intensity more than half of the maximum light intensity of the light sources is achieved by adopting inverted MPSCs.
## 2 Taxonomy and Design Drivers
### Multi-Replication  
Multi-replication reduces the data loss that happens due to node failure or node mobility from one rendezvous zone to another. Furthermore, multi-replication may facilitate data fusion and aggregation. In the literature, most of the DCS schemes fail to meet the key issue of recovering lost data during data replication.
## 4 Edge Computing-Based IoT
### AI in the Realm of EC-IoT  
With current advancements in AI and the hardware that supports AI applications, EC-IoT has seen new technologies and concepts that improve and expand the current capabilities. A few of the major concepts that have been introduced with AI into EC-IoT systems are listed below:  
Edge Intelligence (EI): EI represents a significant advancement in EC-IoT systems, where AI computations are performed locally on edge devices. This reduces the dependency on centralized cloud servers, thereby minimizing latency and bandwidth usage while enhancing real-time processing capabilities [42,87]. By enabling local AI processing, EI allows IoT devices to handle complex tasks independently, making systems more responsive and efficient [88].  
Real-time data processing: Real-time data processing is a core benefit of integrating AI into EC-IoT systems. By processing data at the network edge, latency is significantly reduced, which is crucial for applications requiring immediate responses such as autonomous vehicles and industrial automation [42,89]. This decentralized approach also distributes AI tasks across edge devices, improving system robustness and scalability by avoiding single points of failure [87].  
Enhanced security and privacy: Keeping data at the edge enhances security and privacy by reducing the risk of data breaches during transmission to central servers [42,87]. AI-based anomaly detection systems at the edge can monitor and detect unusual patterns or behaviors, providing early warnings for potential security threats, thus bolstering overall system security [89].  
Advanced, scalable, and flexible AI architectures: AI on the edge supports advanced applications like real-time image and video processing, which are essential for smart surveillance, augmented reality, and natural language processing, enhancing real-time voice recognition and user interaction with IoT devices [89]. Integrating AI into EC-IoT systems introduces automation and intelligence, significantly enhancing the quality of service (QoS) and user experience [42]. This synergy enables smart and automated systems to deliver superior performance and reliability. Furthermore, federated learning in EC-IoT systems allows AI models to be trained across multiple edge devices without transferring data to a central server. This approach not only enhances data privacy and reduces transmission costs but also fosters secure and efficient AI deployment in IoT environments [88]. These advancements collectively drive the evolution of EC-IoT systems, making them more responsive, efficient, and secure.
## 2 Materials and Methods  
### Study Sites and Sampling  
For the purposes of the study, untreated and treated wastewater samples were collected from thirteen wastewater treatment plants of various capacity, hydraulic retention time (HRT), and characteristics of the untreated wastewater, located in the Warminsko-Mazurskie District in Poland. All of the samples were collected after the secondary treatment process. No additional wastewater disinfection was performed in the analysed WWTPs. Wastewater treatment plants were divided into four categories according to the applied modification of the wastewater treatment system: A--WWTPs (No. I, II) with an anaerobic-anoxic-aerobic (A\({}_{2}\)O) bio-reactor system; B--WWTPs (No. III, IV, V, VI, VII) with a mechanical and biological system; C--WWTPs (No. VIII, IX, X) with SBRs (Sequencing Batch Reactors); and, D--WWTPs (No. XI, XII, XIII) with an increased nutrient removal mechanical and biological system (see Supplementary Material, Table S1). Wastewater samples were collected in the winter (February) to sterile bottles, transported to the laboratory at a temperature of 4 \({}^{\circ}\)C, and subjected to analysis on the day of collection [29]. The sample collection period was selected to correspond with an increased morbidity incidence and, thus, with higher antibiotic consumption.
# Chemical Composition and Antimicrobial Properties of Honey Bee Venom
## 2 Results and Discussion
### Chemical Composition of Extractive Compounds
#### 2.2.2 Chemical Composition of Methanol Extracts
The extracts contained nitrogen compounds \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Groups of Compouns**} & \multicolumn{2}{c}{**Dry Venom**} & \multicolumn{2}{c}{**Fresh Venom**} \\ \cline{2-5}  & **Dv-1** & **Dv-2** & **Fv-1** & **Fv-2** \\ \hline \multicolumn{5}{c}{P-containing compounds, including:} & 12.23 (2) & 7.17 (2) & 5.48 (5) & 9.29 (5) \\ \hline - H\({}_{3}\)PO\({}_{4}\) & 11.54 & 6.89 & 2.48 & 5.13 \\ \hline - \(\alpha\)-glycerophosphate & 0.69 & 0.28 & 2.33 & 3.31 \\ \hline - _myo_-inositol phosphate & - & - & - & 0.72 \\ \hline \multicolumn{5}{c}{Carohydrate \& related compounds, including:} & 27.20 (21) & 32.37 (30) & 30.34 (44) & 38.50 (35) \\ \hline - \(\alpha\)- and \(\beta\)-fructose & 1.96 & 14.26 & 4.58 & 7.23 \\ \hline - \(\alpha\)- and \(\beta\)-glucose & 0.14 & 4.11 & 12.13 & 16.51 \\ \hline - gluconic acid & 0.51 & 4.05 & 2.81 & 3.18 \\ \hline - mannitol & 0.04 & 0.59 & 0.77 & 1.34 \\ \hline - glucitol & trace & 0.24 & 2.00 & 2.76 \\ \hline - _myo_-inositol & 0.85 & 0.44 & 0.31 & 0.21 \\ \hline - sucrose & 15.85 & 2.97 & - & - \\ \hline - trehalose & 2.91 & 1.52 & 0.26 & 1.02 \\ \hline - 1-kestose & 0.25 & 0.07 & - & - \\ \hline - erlose & 1.36 & 0.02 & - & - \\ \hline - melzitose & 0.77 & trace & - & - \\ \hline \multicolumn{5}{c}{Other compounds, including:} & 3.13 (5) & 0.12 (4) & 1.14 (5) & 3.25 (7) \\ \hline - 1-O-eicosyl glycerol & 0.62 & 0.04 & - & 0.62 \\ \hline - \(\beta\)-sitosterol & - & - & 0.16 & 0.14 \\ \hline - evenasterol & - & - & 0.37 & - \\ \hline - unidentified P-containing compound & - & - & 0.30 & - \\ \hline - 9-hentriacontene & 0.22 & 0.03 & 0.10 & - \\ \hline - 7-hentriacontene & 0.14 & 0.03 & 0.21 & - \\ \hline NN & 4.24 (12) & 4.75 (26) & 5.59 (27) & 4.31 (15) \\ \hline \hline \end{tabular}  
* the number of components in this group is given in brackets; ** component not found; ** less than 0.01% TIC.  
\end{table} 
Table 3: _Cont._related to catecholamines (neurotransmitters dopamine, norepinephrine, and serotonin), purines (adenine, xanthine, hypoxanthine, and uric acid), nucleosides (guanosine, uridine, pseudouridine, inosine, and cytidine), and pyrimidines (5-methylcytosine, and uracil). All four extracts contained diamines, putrescine, and cadaverine, as well as \(N\)-acetylputrescine. Although the latter compound was found by the authors [5] in dried BV, the content of free putrescine and cadaverine in bee venom, to the best of our knowledge, has not been previously reported.
## 3 Results And Discussion  
### Calculation Results of Flow Field  
The study set the entrance boundary condition as the velocity entrance. The velocity was taken as a torpedo navigation speed of 19 m/s. The torpedo wall was set as no-slip condition, and the outer field boundary was set as no-shear condition. To provide a better initial field for the transient calculation to converge and stabilize faster, the standard k-e model was first selected for steady-state calculation. In the steady-state calculation section, the broadband noise source model was turned on to perform a preliminary estimation and analysis of the entire fluid domain sound field. The results are shown in Figure 3a.  
Figure 1: Diagram of the model. (**a**) Underwater vehicle model (**b**) Flow field calculation domain (**c**) Acoustic computational domain.  
Figure 2: Monitoring point location diagram. (**a**) Side view (**b**) Rear view.  
The accuracy of noise data obtained from the broadband noise source model simulation is not high because it uses a constant Reynolds averaging model to calculate the noise. However, this model has a natural advantage in quickly determining the location of noise source. The sound power level diagram shows that the noise source is mainly concentrated in the torpedo head and wake vortex area behind the torpedo tail. A comparison with the turbulent kinetic energy shows that the noise generation region highly overlaps with the turbulence concentration region, and it can be assumed that the noise mainly originates from the turbulence region.  
Based on the results of steady-state calculations, an LES with a Smagorinsky-Lilly subgrid scale model was used to carry out the transient solution process, and the calculation time step was set to \(1\times 10^{-5}\) s according to the grid size and acoustic conditions. The Q criterion was used to describe the vortex structure of transient flow field, as shown in Figure 4. It was found that the vortex structure is concentrated at the rear of the torpedo blade, which corresponds to the abovementioned sound power level distribution and turbulent kinetic energy distribution, indicating that the vortex structure is closely related to the generation of flow noise and is the cause of flow noise.
## 4 Discussion
The published studies on the biomechanical effects of laminoplasty of the cervical spine can be largely divided into FE analysis and cadaver analysis studies. In FE analysis studies, reports of laminectomy are far more common than reports of laminoplasty [25]. We did not analyze laminectomy in this study. [PERSON] reported the difference in stresses in the cervical spine after different laminoplasty surgeries including open door laminoplasty, French door laminoplasty, and double-door laminoplasty. They reported that laminoplasty was more stable than the intact model [26]. In our study, the results were similar only for LM-L. [PERSON] reported that during flexion, the percent changes in C2-T1 ROM of LM resulted in a 20% increase, and in left bending, a decrease of 20% was observed. Similarly, left axial rotation resulted in a 15% decrease in motion at C6-C7 after double-door laminoplasty [27]. In our study, LM-L showed the same trend; however, previous reports did not consider cervical alignment. In cadaver analysis studies, [PERSON] reported three-dimensional kinematic changes after double-door cervical laminoplasty. They found that laminoplasty showed no significant differences in motion compared with intact except in axial rotation [28]. [PERSON] reported that open-door laminoplasty left the spine in a significantly more stable condition than laminectomy after comparing biomechanical stability during flexion and extension [29]. These results indicated the contribution of laminoplasty to stability. Our results also showed a similar trend for LM-L. To the best of our knowledge, our study is the first to examine various sagittal alignments on the cervical spine. There are several limitations to our study. The models do not include muscles, although the effect of musculature was addressed by the follower load technique [17]. Additionally, the only cervical alignments analyzed were lordotic, straight, and kyphosis alignments. A model with increased kyphosis should also be explored. The current study does not include a spinal cord, nor does it take osteoporosis or osteoarthritis into consideration, which may alter the material properties of the bone. Kyphosis of the thoracic spine and total spine alignment were also not considered. This model simulates an immediate postoperative scenario and does not consider conditions such as fusion and non-fusion of the lamina. The study also does not fully simulate the long-term outcome of laminoplasty. Although there are several methods of laminoplasty [26], the current study only analyzes double-door laminoplasty. FE analysis is a one-time event analyses for a single model. It does not allow for subsequent analysis, such as the evaluation of muscle training and other statistical factors due to rehabilitation. Therefore, multivariate analysis cannot be performed as in clinical analysis. However, our analysis was consistent with previous biomechanical and clinical studies [24; 26; 29]. Despite these limitations, this study provides valuable insight on the biomechanical outcome of laminoplasty in different cervical sagittal alignments.