esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Reduction of Oscillations Caused by Sea Waves
At 0.08 Hz, the values of \(k_{TMD}\) and \(d_{TMD}\) for the frequency-based TMD are identical to those of the optimized passive TMD, leading to similar peak magnitudes for both strategies at this frequency. Conversely, at 0.54 Hz, the frequency-based semi-active control demonstrates superior performance, yielding the lowest peak in the PSD, indicating the most effective damping. Several conclusions can be drawn from these results. Firstly, as expected, the oscillation suppression rates are significantly lower when the system is excited by wave forces compared to the free decay test simulations, due to the consistent excitation introduced by the external force. Although the TMD effectively reduces the TTD, it cannot damp it out entirely, which was expected given the persistent external excitation applied to the system. Additionally, the optimized passive TMD performs worse than the reference TMD at the 0.54 Hz frequency, contrary to the results observed in simulations where the dominant frequency was 0.08 Hz. This discrepancy may be attributed to the fact that the reference TMD was optimized for an average frequency of 0.3 Hz, making it a more effective strategy for handling frequencies that vary between the two dominant modes of the system. Figure 14: Power spectral density comparison for semi-active and passive strategies with induced external frequency. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency f2** & **Passive** & **Amplitude-Based Semiactive** & **Frequency-Based Semiactive** \\ \hline
0.75 \(\times\) 0.54 Hz & 63.02\% & 44.97\% & 63.05\% \\
0.54 Hz & 2.56\% & 6.96\% & 24.75\% \\
1.25 \(\times\) 0.54 Hz & 62.83\% & 44.88\% & 63.35\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of suppression for semiactive and passive strategies. In conclusion, the proposed frequency-based semi-active control methodology demonstrates good performance, achieving nearly a 25% reduction in vibrations compared to the scenario where the FOWT nacelle is not equipped with a TMD. However, it is important to emphasize that the effectiveness of this control strategy is significantly influenced by the periods of frequency fluctuation. The suppression rates achieved by the semi-active control would be higher if the high-frequency excitation occurred during the transient phase or the initial moments of the simulation, and vice versa.
## 2 C-POD Method and Modal Sorting Method
### C-POD Method
Each \(\mathbf{\mu}_{i}^{(t)}\) can be interpreted as a local average attractor in the trajectory space of the flow field--analogous to a mean coherent structure in physical space. (5) Repeat steps (6) and (7) until the following convergence criterion is met: \[\left\|\mathbf{u}_{i}^{(t)}-\mathbf{\mu}_{i}^{(t-1)}\right\|^{2}<\varepsilon,\forall i\in \{1,2,\ldots,K\}\] (8) (6) To obtain the modal coefficients, project the original data matrix \(\mathbf{D}\) onto the cluster bases \(\mathbf{\mu}\) by solving the following least squares problem [24]: \[\mathbf{a}_{r}={(\mathbf{\mu}^{T}\mathbf{\mu})}^{-1}\mathbf{\mu}^{T}\mathbf{a}_{r}\ (r=1,2,\ldots,n)\] (9) Here, \(\mathbf{a}_{r}\in R^{k}\) is the modal coefficient vector corresponding to the \(r\)-th snapshot. Physically, solving the least squares problem in Equation (9) provides a projection of each snapshot \(\mathbf{d}_{r}\) onto the low-dimensional basis formed by cluster centers \(\{\mathbf{\mu_{1}}\mathbf{\mu_{2}}\mathbf{\ldots}\mathbf{\mu_{K}}\}\). These cluster centers are not arbitrary vectors but statistically representative patterns extracted from the dataset. As such, the projection yields modal coefficients \(\mathbf{a}_{r}\) that quantify how much each typical flow pattern (i.e., cluster mode) contributes to the reconstruction of the current state. The full data reconstruction expression is: \[\mathbf{D}=\{d_{1},d_{2},\ldots,d_{n}\}\in R^{m\times n}\]0 (10) where \(\mathbf{A}=[a_{1},a_{2},\ldots,a_{n}]\in R^{\text{k}xn}\) is the modal coefficient matrix, and \(\hat{\mathbf{D}}\) is the reconstructed data matrix. Through the C-POD method, the database matrix \(\mathbf{D}\) is decomposed into low-dimensional clustering bases \(\mathbf{\mu}\) and the modal coefficient matrix \(\mathbf{A}\). The construction of the C-POD method is illustrated in Algorithm 1, which presents the pseudocode of the entire procedure. ```
Input: \(\mathbf{D}=\{d_{1},d_{2},\ldots,d_{n}\}\in R^{m\times n}\)//Snapshot matrix \(K\)//Number of clusters Output: \(\mathbf{\mu}=\{\mathbf{\mu_{1}}\mathbf{\mu_{2}}\mathbf{\ldots}\mathbf{\mu_{K}}\}\)//Cluster basis \(\mathbf{A}=[\mathbf{a_{1}}\ \mathbf{a_{2}}\ \ldots\ \mathbf{a_{n}}]\)//Modal coefficients Steps: 1. POD Initialization - Solving the eigenvalue problem: \(\mathbf{C}\mathbf{\varphi}_{i}=\mathbf{\lambda}_{i}\mathbf{\varphi}_{i}\) (\(i=1,2,\ldots m\)) - Select first \(K\) eigenvectors as the initial cluster centers: \(\mathbf{\mu}_{i}^{(0)}=\mathbf{\varphi}_{i}(i=1,2,\ldots K)\) 2. Clustering - Assign each snapshot vector to its closest cluster center - Compute cluster centroids: \(\mathbf{\mu}_{i}^{(t)}=(\frac{1}{\left|\mathbf{S}_{i}^{(t)}\right|})\sum_{x\in\mathbf{S}_ {i}^{(t)}}x(i=1,2,\ldots,K)\) 3. Compute modal coefficients - Modal coefficient vector \(\mathbf{a}_{r}\in R^{k}\): \(\mathbf{a}_{r}={(\mathbf{\mu}^{T}\mathbf{\mu})}^{-1}\mathbf{\mu}^{T}\mathbf{d}_{r}\ (r=1,2,\ldots,n)\) 4. Return \(\mathbf{\mu}\), \(\mathbf{A}\)
```  
**Algorithm 1**C-POD Method
# Carbon Nanoparticles and Their Biomedical Applications
## 6 Photodynamic Therapy  
Photodynamic therapy (PDT) is a treatment method that can destroy cells, which is why it is used in the treatment of cancer.  
PDT is based on the toxic effects of reactive oxygen species (ROS; OCI\({}^{-}\), OH\({}^{-}\), H\({}_{2}\)O\({}_{2}\), O2, \({}^{1}\)O\({}_{2}\), -OH, \({}^{0}\)\({}^{-}\), \({}^{0}\)\({}^{-}\)) whose local production is induced by the presence of CNPs. The formed ROS display toxic effects, especially toward cancer cells. Distinct CNPs generate different types of ROS. Light-independent, single-walled carbon nanotubes generate superoxide anions (-O\({}_{2}\)\({}^{-}\)), while graphene quantum dots generate singlet oxygen (\({}^{1}\)O\({}_{2}\)) [55, 56]. This method is non-invasive and very effective with low side effects and minimal systemic toxicity. To enhance the effect of PDT, the application of CNPs is combined with light (light-mediated PDT), anticancer drugs, or photothermal therapy. CNPs are most often activated with NIR, which penetrates deeper into the tissue than visible light or ultraviolet light [57, 58].  
This type of treatment can also be used to treat bacterial and fungal infections. [PERSON] et al. produced nanocomposites based on GO and MWCNTs. MWCNTs eliminated _Escherichia coli_, _Staphylococcus aureus_, and _Candida albicans_ in vitro, while GO only eliminated bacteria [59]. [PERSON] et al. showed that indocyanine green-loaded GO effectively eliminated methicillin-resistant S. aureus [60].
## 1 Introduction
In the case of the multi-beam satellite, the system should aim at designs to maximize the efficiency of resources in terms of frequency reuse, power allocation and especially the quality of the beam layout [18]. Great attention is drawn to the antenna design and optimization to further increase the total throughput of the satellite by using a limited number of beams [19]. Conventionally, the allocation of beam spots was carried out based on the supposed service requirements of ground and flight applications [20]. It is common for design tools to provide regular coverage based on the assumption of traffic demand partition [21]. Therefore, the solution is often suboptimal with inappropriate beam widths, overprovisioned or unprofitable beams. Several optimization algorithms were taken into consideration to build a non-uniform layout and further improve the systematic performance by the local search method or simple enumeration [22; 23]. Previously, in the study of the communication system, there were studies focusing on dealing with the problem of uniformity, which usually takes several factors, such as energy availability, interest, physical ties and so on, into consideration to carry out the coalition and selection process. To implement the dynamic practice, the individual device is associated with a holistic utility function, which appropriately represents its degree of satisfaction with respect to the Quality of Service (QoS) prerequisites fulfilment [24]. This kind of process is also applied among a variety of wireless access technologies supporting IP connectivity as the handover function, which is a key enabler for seamless mobility and service continuity [25]. However, these frameworks are all used in the scenario with fixed hubs, and the aim is to lay out the behavior of mobile terminals and chase the optimization of utility function. In this work, the task is to allocate the beam spot centers with a predictable distribution of dynamic terminals without the prerequisite of the centroid positions. In this work, the service objects are LEO satellite constellations, which have predictable configuration and specific movement characteristics. Different from the ground and flight applications, the data requirement assumption of LEO objects is relatively realistic [26]. It offers the opportunity to globally optimize the beam layout, relying on truthful data. Figure 1 illustrates the schematic of a GEO data relay satellite covering the target LEO satellite constellation with spot beams. The LEO satellites with altitude lower than 1500 km are located within a field of view (FOV) of approximately \(\pm 11^{\circ}\). The number in the center of each circle indicates the objective LEO satellites in the coverage of the certain beam. For any algorithm, the challenge is to appropriately place the spot centers and choose the beam widths in line with the density of the traffic demands. This paper aims at proposing a mathematical model to automatically realize the global optimization of beam layout based on a modified K-means approach.
## 3 Methods
### Gaussian Process Regression
#### 3.4.2 Model Performance  
The model performance was assessed using the \(R^{2}\)-value and the root-mean-squared error (RMSE). Model performance was determined by five-fold cross-validation. Cross-validation is particularly suitable for small data sets, allowing all available data to be used iteratively without the risk of overfitting [66]. The data were divided into five equal partitions. Four groups were used to train the model while a held-out group was used to validate the model. The process was repeated for all combinations. The RMSE score calculated during validation was averaged across all partitions and presented with confidence intervals.
# Biogas Production Enhancement through Chicken Manure Co-Digestion with Pig Fat
## 3 Results and Discussion
### Fat Influence on Digestated Substrate Changes
#### 3.5.1 Digestate pH and TAN  
While the composition of the digested substrate impacts microbe population growth and nourishment for biogas production, the pH of the digestate supports acclimatization of the bacteria population and facilitates the uptake of specific nutrients for organisms. Furthermore, the pH of the anaerobic digester affects the mass transfer rate, thus impacting digestion performance. The carbohydrates, lipids, and proteins supply nutrients to microorganisms, maintaining a carbon-to-nitrogen ratio (C/N). The proteins are the primary source of nitrogen upon degradation into ammonia. The free ammonia (NH\({}_{3}\)) and ammonium ions (NH\({}_{4}\)+) are the degradation end-products of the protein: amino acids and urea. For example, a high level of proteins can come from meat products, leading to a lower C/N ratio and process instability. In the substrates containing a high concentration of ammoniaical nitrogen, pH determines the free ammonia (NH\({}_{3}\)) and ionized ammonia (NH\({}_{4}\)+) ratio [72] and free hydrogen sulphide (H\({}_{2}\)S) and ionized sulphide (HS\({}^{-}\)) ratio as well. When pH goes down, the inhibition of H\({}_{2}\)S can occur; when it goes up, the inhibition of NH\({}_{3}\) can occur. H\({}_{2}\)S plays a role in digestate and biogas. Ammoniacal nitrogen (N-NH\({}_{4}\)+), as total ammoniacal nitrogen (TAN), acts in digestate. In the biogas, the gaseous NH\({}_{3}\) presents only in low concentrations [25]. Such concentrations do not make an essential impact on biogas quality and do not restrict biogas from being used. Additionally, TAN does not change significantly even at higher OLRs and can remain relatively stable throughout the entire biogas production period.  
According to a study [73], the anaerobic digestion process can proceed well around TAN of 6000 mg\(\cdot\)L\({}^{-1}\), but it is also reported in another study [74] that TAN concentration can increase to 8000 mg\(\cdot\)L\({}^{-1}\) because methanogens are able to acclimatize to increasing TAN with time. In our experiments, TAN was at the level of 3500-4600 mg\(\cdot\)L\({}^{-1}\) and did not play any role in inhibiting the digestion process. Some literature [65] suggested a pH of 6.8-7.5 as suitable for the methanogen population in manure co-digestion. However, through our experiment, the pH value was in the range of 7.8-8.0, and the digestion process seemed to be stable with no inhibitions. The highest pH value of 8.0 \(\pm\) 0.1 on average was observed in the fourth stage at the highest OLR when biogas yield rate and C\({}_{\rm m}\) value were the highest too. The pH value of 7.8 \(\pm\) 0.1 on average was the same in the first three stages.
# Evaluation Model of Rice Seeding Production Line Seeding Quality Based on Deep Learning
## 4 Discussion
### Limitation of Our Work  
In this study, the proposed rice sowing quality detection model effectively identifies missed sowing areas in seedling trays, but it also presents several critical issues for further investigation.  
1. The virtual grid-style image segmentation preprocessing method, while boosting model efficiency and precision, adversely affects the recall rate in certain situations. This challenge is exemplified when large unsown areas exist between adjacent grid images with seed presence at their periphery, leading to these areas being undetected. The problem arises as the model treats each grid image independently, failing to recognize the larger missed sowing area formed by the combination of two grids. One potential solution explored was replacing grid segmentation with grid convolution to improve recall. However, this method substantially extends the detection time, which is impractical for the fast-paced environment of rice seedling production lines. Therefore, balancing the enhancement of recall rate while maintaining rapid detection speed will be a primary focus for future research endeavors.
2. The assessment of seeding quality in rice seedling production lines involves multiple factors. While this research model predominantly addresses missed seeding during rice seed sowing, it is crucial to note that the issue of seed overlap due to excessive sowing also warrants attention. Excessive seed overlap can intensify competition during seedling cultivation, thereby impacting seed growth and development. Consequently, future research endeavors will prioritize addressing the problem of excessive seed overlap while identifying areas of missed seeding.
## 3 Conventional Detection Scheme
### Half-Complexity Joint Detection (HCJD) Scheme  
A low-complexity estimation scheme exploiting inherent central-symmetric feature of the ZC sequences is commonly utilized in the LTE-MTC system [15; 16; 17]. In this approach, the metric is defined as the correlation from the symmetric-conjugate differential PSS and local differential reference PSS  
\[\Phi_{a}(m,n)=\sum_{k\in\mathcal{D}^{+}}\left\{\tilde{Y}_{l}(k+m)+\tilde{Y}_ {l}^{*}(-k+m+1)\right\}D_{n}^{*}(k), \tag{7}\]where \(m\) refers to the trial value of a true IFO, \(\mathcal{D}^{+}=\{k|2\leq k\leq N_{p}/2\}\) whose cardinality is \(N_{p}/2-1\), and \(\Phi_{a}(m,n)\) represents the joint metric of the HCJD scheme. The objective is to find the joint estimate of the IFO and SID by maximizing \(\Phi_{a}(m,n)\) with respect to two-dimensional search space \((m,n)\)[17]  
\[(\vartheta,\hat{n})=\underset{|m|\leq M,n\in\{0,1,2\}}{\arg\max} \Phi_{a}^{I}(m,n), \tag{8}\]  
with  
\[\Phi_{a}^{I}(m,n)=\sum_{k\in\mathcal{D}^{+}}\hat{Y}_{l}^{I}(k+m)D _{n}^{I}(k)+\sum_{k\in\mathcal{D}^{+}}\hat{Y}_{l}^{Q}(k+m)D_{n}^{Q}(k), \tag{9}\]  
where \(\hat{Y}_{l}(k+m)=\hat{Y}_{l}(k+m)+\hat{Y}_{l}^{*}(-k+m+1)\), and \((\cdot)^{I}\) and \((\cdot)^{Q}\) denote the operation taking the real and imaginary part of the enclosed complex quantity, respectively.
###### Abstract  
While the training of professional sports athletes and the factors determining sports success are based on well-established scientific research, esports training and markers of esports performance are not yet fully recognized or explored. Knowledge of the psychophysiological responses of the athlete's body to esports competition is the necessary foundation for rational training management. The aim of the present study was to evaluate physiological stress while playing Counter-Strike: Global Offensive (CS:GO). Selected cardiovascular, hormonal, and biochemical indices were monitored to assess differences in stress responses between winners and losers. Twenty-two male players participated in the study (age 22.0 \(\pm\) 2.0 years, CS:GO training experience 7.0 \(\pm\) 2.2 years, training load 24.6 \(\pm\) 11.5 h per week). Each player played two games during the CS:GO competition. The CS:GO tournament induced an increase in heart rate (HR), systolic blood pressure, and blood cortisol levels (C), and a decrease in the nonlinear heart rate variability (HRV) index based on the fractal correlation properties, called alpha1, of detrended fluctuation analysis (DFA-alpha1). In contrast, no changes were observed in blood levels of testosterone (T) and lactate (BLA). It was found that changes in physiological indices in players while playing CS:GO did not differentiate between winners and losers. The changes in the physiological parameters recorded during play indicate that CS:GO tournaments induce significant physiological arousal and can be considered a stressor.  
esports; CS:GO; physiological stress; competition Article
## 5 Results and Discussion
In comparison with previous studies, the hybrid models of the present research outperformed the integration of least squares support vector machine (LSSVM) and the cuckoo search optimization (CSO), LSSVM-CSO, used by [PERSON] et al. (2019) for the same aim. More clearly, according to the obtained values of \(R^{2}\), the prediction accuracy of both SFLA-ANN (0.89) and WDO-ANN (0.92) was higher than 0.885 in their study. However, the LSSVM was more successful in understanding the relationship between the shear strength and input factors (\(R^{2}=0.922>0.90\)). Similarly, the PSO-SVM used by [PERSON] et al. (2017) predicted the shear strength less accurately (\(R^{2}=0.888\)) than both hybrid models of our study. More than 90% generalization accuracy of the WDO-ANN shows that it is a capable model to analyze and predict the shear strength for unseen soil conditions. In other words, it can be introduced as a promising alternative to laboratory and traditional shear strength evaluative methods. Hence, the ruling formulation of this model was extracted, and it is presented in Equation (18) for use as an efficient neural-mathematical solution to the problem of shear strength modeling. This formula, as well as Equation (19), was derived using the weights and biases of the MLP which were optimized by the WDO algorithms. More specifically, Equation (18) was extracted from the output neuron of the MLP network, which was fed by \(A\), \(B\),..., \(E\). These middle parameters represent the outputs of the hidden neurons. As can be seen, the activation function of Tansig was applied to these outputs. \[\text{\emph{S}}\text{\emph{S}}\text{\emph{S}}\text{\emph{S}}\text{\emph{S}} \text  
alternatives for simulating the shear strength. In this regard, the neural-metaheuristic formula of the most successful model (i.e., the WDO) was extracted and presented. [PERSON] and [PERSON] wrote the manuscript, discussed the results, and analyzed the data. [PERSON] and [PERSON] edited, restructured, and professionally optimized the manuscript. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflicts of interest.
## 4 Results and Discussion
### Numerical Study on the Performance of Three-Layer Covers with Capillary Barrier  
Previous tests emphasized the mid-layer's impact on the capillary barrier effect within a three-layer cover. However, due to constraints related to test equipment and geometric dimensions, the physical tests could not assess the cover's performance at the engineering scale. Numerical modeling should be approached not as a predictive tool but as a means to gain deeper insights into the behaviors of complex systems across various scenarios ([PERSON] and [PERSON], 2019). A 15 m long cover was numerically investigated to address this, enabling comparisons of diversion length, lateral drainage, and percolation across various mid-layers. Numerical analyses were conducted using the SEEP/w (GEOSTUDIO 2023.1) software based on the finite element method.  
Unsaturated soil exhibits hysteric behavior, with significant differences observed in the drying and wetting soil water characteristic curves (SWCCs) (Stein and Stein, 2019; [PERSON] and Stein, 2019; [PERSON] and Stein, 2020). These curves also experience changes when subjected to repeated wetting and drying processes. Using appropriate soil properties, such as SWCCs, in numerical analyses is crucial to modeling soil behavior accurately under unsaturated conditions ([PERSON] and Stein, 2019; [PERSON] and Stein, 2020; [PERSON] and Stein, 2020). Back analyses were performed to calibrate the soil water characteristics and hydraulic conductivity curves based on the results of the 1D infiltration tests.  
Figure 9: Infiltration rate variation with time for all tests.
# Efficient and Sustainable Cleaning: A Comparative Analysis of Cryogenic Technology
## 4 Discussion
### Future Trends and Developments
#### 4.1.2 Development of Noise Reduction Technologies  
In the technical and safety data sheets published by the manufacturers, the sound pressure levels to which the operator is subjected during the use of the equipment are published, as well as the required personal protective equipment (PPE) to limit the risks of exposure. Most of the machines recorded in the study reach 120 dB/A, a very high value that can cause hearing damage if appropriate protective measures are not taken. For reference, the take-off of a jet plane at a distance of 100 metres, a chainsaw, or a train horn corresponds to approximately these levels. Against this background, we found studies that demonstrate the relationship between optimal nozzle configuration and noise impact reduction, offering a promising advancement in operator comfort and safety [63]. Other research aimed at automotive type mould cleaning integrates numerical and experimental methods to optimise the energy and aerodynamic efficiency of the cleaning system. Considering geometrical and operational constraints to reduce noise, they evaluate various nozzle and silencer designs to minimise noise emissions without affecting system efficiency [18].