esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, current training methods have either focused solely on increasing the physiological load or on separating training to improve sprint mechanics. We reviewed a pioneering study that introduced elastic band resistance for sprint mechanical efficiency in repeated sprint training protocols for elite youth soccer players, aged 17 \(\pm\) 0.3 years [25]. This study revealed that sprint performance improved during the late acceleration phase (20-30 m) but not during early acceleration. These results are similar to those of other forms of repeated sprint training. Therefore, they are unlikely to solve the current problems. In addition, it is known that post-peak height velocity (PHV) in youth athletes (age: 15.2 \(\pm\) 1.6 years) that participated in the previous study requires neurological and morphological adaptation to improve sprinting [42; 53; 52]. Post-PHV athletes also need to develop initial acceleration, for which the level of lower limb strength to overcome inertia is critical; therefore, a higher resistance than the elastic band should be considered from the initiation of acceleration [54; 55]. Furthermore, the study did not investigate how resistance with a short rest interval affects sprint performance and mechanical characteristics during training. This makes it challenging for practitioners to design and apply training methods based solely on the findings of this study. Therefore, new training approaches are necessary to improve short sprint performance in soccer players and help them repeatedly execute efficient accelerations, even when fatigued. The present study aimed to examine the effects of a short rest interval on resisted sprint performance and sprint mechanical variables in elite youth soccer players to determine whether brief recovery periods could be used for resisted sprint training for soccer short sprint performance. Two types of training were compared: (a) RST2M, which involved six repetitions of 20 m resisted sprints with a 2 min rest interval (traditional), and (b) RST40S, which involved six repetitions of 20 m resisted sprints with a 40 s rest interval (repeated sprint protocol). Under both conditions, a load of 30% Vdec was applied. Furthermore, to evaluate and compare neuromuscular fatigue levels after each procedure, players performed a countermovement jump (CMJ) before and after each condition. We hypothesized that RST40S would exhibit lower performance and alterations in mechanical variables compared with RST2M in overall 0-20 m distance. Previous studies have focused on main taining sprint quality through manipulation of rest periods, sprint distance, and volume. However, our study was designed to develop a novel training methodology that enhances the ability to perform repeated explosive accelerations with effective sprint mechanics while maintaining high exercise intensity.
## 2 Materials and Methods
### Analysis of Volatile Metabolites by GC-MS/MS
#### 2.5.2 GC-MS Conditions  
Following the sampling process, the VOCs were absorbed from the fiber coating in the injector port of the GC device (Model 8890; Agilent) for 5 min while operating in splitless mode. An Agilent Model 8890 GC and an Agilent 7000D mass spectrometer outfitted with a 30 m \(\times\) 0.25 mm \(\times\) 25 cm DB-5 MS (5% phenyl-polymethylsiloxane) capillaries columns were used to identify and measure VOCs. The medium that carried gas was helium, moving at a linear speed of 1.2 mL/min. The detection temperature was set at 280 \({}^{\circ}\)C, while the injection device was kept at 250 \({}^{\circ}\)C. The oven was preheated to 40 \({}^{\circ}\)C for 3.5 min, and then the temperature was raised at a rate of 10 \({}^{\circ}\)C per minute, 7 \({}^{\circ}\)C per minute, and 25 \({}^{\circ}\)C per minute until 280 \({}^{\circ}\)C, where it remained for 5 min. At an energy of 70 eV, mass spectra have been collected in the electron impact (EI) ionization modality. The quadrupole mass detection device, ion source, and transmission line were all adjusted to temperatures of 150 \({}^{\circ}\)C, 230 \({}^{\circ}\)C, and 280 \({}^{\circ}\)C, respectively. Analyte identification and quantification were performed using the MS ion monitoring (SIM) mode.
## 4 Results
### Review Papers and Fuzzy MCDA  
Regarding fuzzy MCDA, it was found that the approach was not mentioned in 11 out of 80 papers, but fuzzy applications were included in 44 papers, representing a significant percentage (55%), where many review papers include a single-digit number of fuzzy applications. Medium and wide use of fuzzy MCDA was observed in 10 and 13 review papers, respectively, while 2 review papers focused exclusively on fuzzy MCDA.  
More specifically, [PERSON] et al. [47] present a comprehensive overview of the application of fuzzy MCDA in energy policymaking, where decision-makers must reconcile competing criteria such as cost, environmental impact, and social acceptance, within an uncertain context. For example, fuzzy MCDA enables the assessment of renewable energy initiatives by incorporating subjective or uncertain elements as fuzzy values, thereby establishing a malleable structure that is consistent with the intricacies of the real world. Similarly, [PERSON] et al. [49] discuss the application of fuzzy logic in renewable energy systems, with a particular focus on optimising resource allocation, technology selection, and site selection for wind and solar installations. By modelling uncertainties related to environmental conditions and community impacts, fuzzy MCDA facilitates more resilient and adaptable decision making, which is crucial for sustainable energy planning. These examples demonstrate the effectiveness of fuzzy MCDA in addressing practical challenges in the environmental and energy domains, thereby enhancing the robustness of decision-making processes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Journal** & **No. of Review Papers** \\ \hline _Renewable and Sustainable Energy Reviews_ (IF 15.9) & 12 \\ _Energies_ (IF 3.2) & 6 \\ _Sustainability_ (IF 3.9) & 5 \\ _Waste Management \& Research_ (IF 3.9) & 4 \\ _Journal of Cleaner Production_ (IF 11.1) & 2 \\ _Science of The Total Environment_ (IF 9.8) & 2 \\ _Energy Strategy Reviews_ (IF 8.2) & 2 \\ _Waste Management_ (IF 8.1) & 2 \\ _Environmental Reviews_ (IF 5.7) & 2 \\ _Water_ (IF 3.4) & 2 \\ _Journal of Multi-Criteria Decision Analysis_ (CiteScore 4.1) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The dominant journals with the most published review papers.
# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
## 2 Materials and Methods  
### The Study Area  
The area under investigation is the strait between two highly popular tourist islands in Greece, namely Paros and Antiparos Islands in the Cyclades plateau, located in the central Aegean Sea; the region extends between 37\({}^{\circ}\)05\({}^{\prime}\) N and 36\({}^{\circ}\)56\({}^{\prime}\) N, and 25\({}^{\circ}\) 03\({}^{\prime}\) E and 25\({}^{\circ}\)09\({}^{\prime}\) E (Figure 1).  
The depths at the northern part of the strait range between 10 and 30 m, and at its southern part between 10 and 80 m. The seabed relief is complex, since it includes a large number of small islets, outcrops, and shallow areas, whilst its deeper part (\(>\)20 m) is smooth, therefore satisfying the boundary conditions of being equivalent to a Lambertian reflector. The strait is used by heavy vessel and leisure boat traffic due to Parikia port, which is the main port of Paros island.
# Parametric Analysis of Compliant End Face Gas Film Seals Considering Slip Flow Effects
## 2 Analytical Model
### Mathematical Model
#### 2.2.5 Seal Performance Parameters  
The comprehensive performance of compliant end face gas film seals was evaluated by combining the gas film opening force, mass leakage rate, and viscous friction power consumption.  
The dimensionless gas film opening force is:  
\[\overline{F}=\int_{0}^{2\pi}\int_{1}^{\frac{R_{o}}{R_{\rm i}}}\ PRdRd\theta \tag{14}\]  
The dimensionless mass leakage rate without considering slip flow effects  
\[\overline{Q_{m}}^{\prime}=\int_{0}^{2\pi}\frac{\partial P}{\partial R}H^{3} Rdd\theta \tag{15}\]  
The dimensionless mass leakage rate due to slip flow effects is  
\[\overline{Q_{m}}^{\prime\prime}=\int_{0}^{2\pi}\frac{\partial P}{\partial R} H^{2}Rd\theta \tag{16}\]  
The dimensionless mass leakage rate considering slip flow effects  
\[\overline{Q_{m}}=\overline{Q_{m}}^{\prime}+\overline{Q_{m}}^{\prime\prime} \tag{17}\]  
The dimensionless viscous friction power consumption without considering slip flow effects  
\[\overline{W_{f}}^{\prime}=\omega\int_{1}^{\frac{R_{o}}{R_{\rm i}}}\int_{0}^{2 \pi}\biggl{(}\frac{H}{2}\frac{\partial P}{\partial\theta}+\frac{\Lambda R^{3} }{6}\biggr{)}RdRd\theta \tag{18}\]  
The dimensionless viscous friction power consumption considering slip flow effects  
\[\overline{W_{f}}=\omega\int_{1}^{\frac{R_{o}}{R_{\rm i}}}\int_{0}^{2\pi} \left(\frac{H}{2}\frac{\partial P}{\partial\theta}+\frac{\Lambda R^{2}}{6 \biggl{(}H+\frac{2\lambda(2-\sigma_{v})}{h_{0}\sigma_{v}}\biggr{)}}\right)RdRd\theta \tag{19}\]  
In Equation (19), \(\lambda\) is the mean free range of the dielectric gas, m; \(\sigma_{v}\) is the Tangential Momentum Coordination Coefficient (TMAC), and the value can be selected according to the surface properties of different engineering materials.  
Dimensionless stiffness-to-drainage ratio without considering slip flow effects  
\[\overline{\Gamma}^{\prime}=\frac{\overline{F}}{\overline{H_{avg}Q_{m}}^{\prime}} \tag{20}\]  
Dimensionless stiffness-to-drainage ratio considering slip flow effects  
\[\overline{\Gamma}=\frac{\overline{F}}{\overline{H_{avg}Q_{m}}} \tag{21}\]  
where \(\overline{H_{avg}}\) is the dimensionless average of the gas film thickness after the deformation of the compliant end face.
## 3 Results
### Principal Component Analysis (PCA)  
Figure 4 shows the principal component analysis (PCA). PCA was performed to investigate how the experimental parameters influence the variation in the data. Principal components PC1 and PC2 accounted for 68% of the total variance. It was observed that high initial concentrations of the antibiotic and high dosages of the nanoparticles result in lower pollutant removal rates. This can be explained by factors that hinder the efficiency of photocatalysis, which aligns with the previously conducted analyses. The pH, being a variable close to the origin point, shows that it is weakly correlated with the main directions of variation in the data. On the other hand, time has a significant influence on the experiments with higher removal percentages. This is due to the constant generation of hydroxyl radicals, which can react with the antibiotic molecule, promoting its photocatalytic degradation.
# A Survey on Blockchain-Based Federated Learning
## 2 Preliminary
### Network layer  
The network layer primarily furnishes mechanisms for information exchange among each node within the blockchain network, including the P2P network mechanism, the information communication mechanism, and the data verification mechanism. With a P2P network, the messages are directly propagated between nodes. Each node has the same functionality and status, and there is no centralized device. Each node is responsible for routing, block data validation, block data propagation, transaction information packaging, and discovering new nodes [39]. Under the P2P networking method, the system can still operate normally even if any node breaks down.
## 4 Materials and Experimental Methodology
### Experimental Setup and Procedure
This relatively long wavelength compared to the bar diameter (\(a/\lambda\approx(0.037\,\mathrm{m})/(1\,\mathrm{m})\approx 0.037\ll 1\), where \(a\) is the bar radius and \(\lambda\) is the wavelength) supports the application of one-dimensional stress wave theory for analyzing these experiments [14]. To ensure the accuracy of the spall strength calculation using Equation (4), the specific material properties for each specimen configuration were carefully determined from the experimental data. For the UDRC specimens, the effective density (\(\rho_{eff}\)) was calculated using a rule-of-mixtures based on the respective volume fractions and densities of steel and concrete. For plain concrete specimens, the measured bulk density was used. The longitudinal wave speed (\(C_{0}\)) was not assumed to be a constant value but was determined experimentally for each individual test. This was achieved by measuring the transit time (\(\Delta t\)) of the incident wave front between the strain gauge located at 300 mm from the impact end (i.e., 700 mm from the free end) and the free surface itself. The wave speed for each test was then calculated as \(C_{0}=(700\,\mathrm{mm})/\Delta t\). This data-driven approach ensures that the effective wave speed, which accounts for the influence of the reinforcement, is accurately  
Figure 14: Schematic of the SHPB experimental setup configured for one-dimensional spalling tests on concrete and UDRC specimens. used in the spall strength calculation, thereby directly addressing the heterogeneity of the UDRC system.
## 2 Film System Design
### Technical Parameters
\tag{5}\]  
The light energy \(E\left(\lambda\right)\) entering the infrared detector through the gas chamber and bandpass filter film is:  
\[E\left(\lambda\right)=\int_{\lambda_{0}-\Delta\lambda}^{\lambda_{0}+\Delta \lambda}S(\lambda)\times T_{g}(\lambda)\times T_{f}(\lambda)d\lambda \tag{6}\]  
Assuming \(c_{1}<c_{2}\), then \(E_{C_{1}}(\lambda)>E_{c_{2}}(\lambda)\). So, the rate of change in light energy under different concentrations of \(c_{1}\) and \(c_{2}\) can be expressed as:  
\[r=\frac{\Delta E(\lambda)}{E_{c_{1}}(\lambda)}=\frac{E_{c_{1}}(\lambda)-E_{c_{ 2}}(\lambda)}{E_{c_{1}}(\lambda)} \tag{7}\]  
Equation (7) illustrates the sensitivity of the bandpass filter film to different concentrations of gas. The higher the sensitivity, the more sensitive the bandpass filter film is to changes in light intensity at different concentrations and the higher the accuracy of the system. And the corresponding sensitivity of the absorption spectrum detection system for methane and ethane can be expressed as:  
\[P(\lambda)=\frac{\int_{\lambda\lambda}^{\lambda 2}C(\lambda)T(\lambda)f( \lambda)d\lambda}{\int_{\lambda 1}^{\lambda 2}C(\lambda)T(\lambda)d\lambda} \tag{8}\]  
Among them, _f_(\(\lambda\)) represents the absorption spectrum curve function of the gas, _T_(\(\lambda\)) is the transmittance of the filter at \(\lambda\) band, and _C_(\(\lambda\)) represents the sensitivity of the detector at wavelength \(\lambda\). Since the detector does not change, _C_(\(\lambda\)) is generally a fixed value; then, Formula (8) can be converted into Formula (9). \[P(\lambda)=\frac{\int_{\lambda\lambda}^{\lambda 2}T(\lambda)f(\lambda)d \lambda}{\int_{\lambda 1}^{\lambda 2}T(\lambda)d\lambda} \tag{9}\]  
It can be seen from Formula (9) that the response sensitivity of the characteristic spectrum of methane and ethane is mainly related to the transmittance of the filter device; the higher the passband transmittance of the filter device, that is, the higher the detector response intensity, the higher the corresponding sensitivity and the more accurate the detection. So, it is the key to improving the transmittance of the filter in the characteristic peak bands of methane and ethane and reduces the transmittance of its background noise bands to improve the detection system's corresponding sensitivity to methane and ethane absorption spectra. According to the above analysis, the spectral curve index parameters of the prepared filter device are shown in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Indicator Parameters** \\ \hline Transmission band & 3–3.5 μm and 6.5–8.2 μm \\ Cut-off band & 1.6–2.95 μm \& 3.55–6.45 μm, and 8.3–10 μm \\ Average transmittance within the transmission & \(\geq\)90\% \\ band & Cut-off depth & OD3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Designed spectral parameters of filter.
# Automated Conditional Statements Checking for Complete Natural Language Requirements Specification
## 4 Empirical Evaluation
### Descriptions of the Studies
#### 4.3.2 Study II: Selection of Clustering Methods  
There are also many candidate methods for the conditional statements clustering. In this study, we will compare several clustering methods with the same sentence embedding method and observe their performance. This study is to answer the research question RQ1 by selecting one suitable method for the conditional statements clustering. The selected clustering methods used for the comparison are as follows.  
1. K-Means: K-Means is the commonly used clustering algorithm based on Euclidean distance. We use the K-Means in the Scikit-learn. The number of clusters is set to 12 according to the sum of the squared errors (SSE).
2. Spectral Clustering: spectral clustering [51] is based on graph theory. We use the implementation of spectral clustering provided by Scikit-learn. The number of clusters is also set to 12.
3. Agglomerative Clustering: agglomerative clustering [52] is a hierarchical clustering approach. We use the implementation of agglomerative clustering provided by Scikit-learn. The number of clusters is also set to 12.
4. DBSCAN: DBSCAN [53] is a density-based clustering approach. The number of clusters is not required for clustering. We use the implementation of DBSCAN provided by Scikit-learn.
5. Affinity Propagation: affinity propagation [54] is a graph-based clustering method, which does not require specifying the number of clusters for clustering. We use the implementation of affinity propagation provided by Scikit-learn.  
To evaluate the performance of the clustering algorithms, the metrics of rand index and mutual information are also used.
# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Results
#### 5.3.4 Impact of Data Time Interval on Model Performance  
Beyond the quantity of data, this study also investigates the temporal granularity at which data are collected to explore the implications on the model performance. As in previous analyses, the comparison includes TBML models and DL models, along with their top-performing subsets: SPTB models and RNNs. Figures 12 and 13 illustrate the FPA and WRA scores across different time intervals of the datasets.  
Figure 12: FPA score for each model based on time interval of data. Blue bars represent the overall model types, while orange bars indicate the best-performing class of models.  
Figure 13: WRA score for each model based on time interval of data. Blue bars represent the overall model types, while orange bars indicate the best-performing class of models.  
DL models outperform TBML models in the 5/10 min, 15/30 min, and 1-month intervals. Notably, these intervals have a modest representation in the dataset, occurring six, four, and eight times, respectively. Conversely, TBML models show a slightly better performance in the 1/4 h interval (16 occurrences) and the 7/8/15/16-day interval (3 occurrences), with FPA and WRA advantages of 6.25% and 0.0469, and 33.33% and 0.0556, respectively. The time intervals of 8 days and 16 days are tested using the same model, see [9].  
A deeper comparison of subclasses reveals that RNN models outperform SPTB models in the 1 min, 5/10 min, 15/30 min, 1-day, and 1-month time intervals. Among these, the 1-day interval (9 RNN occurrences vs. 12 SPTB occurrences) is particularly significant, where RNN models achieve a notable FPA advantage of 27.78% and a WRA advantage of 0.2509. On the other hand, SPTB models surpass RNN models in the 1/4 h and 7/8/15/16-day intervals. The 1/4 h interval (16 occurrences) is particularly noteworthy, with SPTB models outperforming RNNs by 18.59% in FPA and 0.0785 in WRA.
## 6 Numerical Results
### Impact of Candidate Paths  
In Figure 5, we analyze the impact of the number of candidate paths (\(k\)) on the performance of the PBA in network MESH-20. We focus on the objective function value (\(z\), left chart) and algorithm computation times (right chart) as a function of the number of RUs (\(R\)). The results are averaged for six scenarios with \(\mu\in\{1,2,3\}\) and \(\gamma\in\{0.2,0.3\}\).  
We can see that \(z\) almost does not change with \(k\) in smaller network scenarios, \(R\in\{10,30\}\). In larger networks (\(R\geq 50\)), there is some improvement of \(z\) if more candidate paths are available. The computation times increase with the number of RUs, which is directly related to the increasing number of demands processed by the algorithm. At the same time, the number of candidate paths does not significantly impact the runtimes. In the remaining analysis, \(k=5\) if not mentioned otherwise.
# Investigations of Mixing Technique on the Rheological Properties of Self-Compacting Concrete
## 2 Experimental
### Rheological Measurement  
Measurements of concrete rheology are complicated because of the presence of large aggregates. To mitigate the negative effect of large aggregates, one possible solution is to entail a specimen sufficiently large. This has been achieved usually by enlarging the gap between the inner and the outer cylinders of the mold, i.e., at least 3-5 times the size of the coarse aggregates [38]. In this work,  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Items** & **Loss of Ignitions/\%** & **SO/\%** & **CI**-/\% & **Na\({}_{2}\)O + K\({}_{2}\)O/\%** & **\(t\)-C\({}_{2}\)O/\%** & **MgO/\%** & **Specific Surface Area/\({}^{2}\)-kg\({}^{-1}\)** & **Density/(kg\(\cdot\)cm\({}^{-3}\))** \\ \hline Cement & 2.97 & 2.39 & 0.016 & 0.63 & 0.71 & 3.32 & 364 & 3.08 \\ Slag & 0.18 & 1.46 & 0.012 & 0.44 & / & 12.72 & 301 & 2.86 \\ Fly ash & 3.97 & 0.41 & 0.006 & 0.65 & 0.03 & 1.65 & 438 & 2.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties and chemical composition of cement, slag powder and fly ash.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Cement** & **Hy Ash** & **Slag** & **Expansive Agent** & **VMA** & **Coarse Aggregate** & **Sand** & **Water** & **SP** & **Air-Entraining Agent** \\ \hline
250 & 63 & 159 & 47 & 0.78 & 747 & 913 & 182 & 4.75 & 0.075 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mix proportions of self-compacting concrete (SCC) in kg\(\cdot\)m\({}^{-3}\). VMA—viscosity modifying admixture; SP—superplasticizer.  
the rheological measurements were carried out by using the RHM-3000 ICAR rheometer, which was equipped with cylindrical spindles. The vane height was 290 mm. The diameter and height of the outer cylinder were 355 mm and 380 mm, respectively. The outer cylinder was installed with ribs to prevent possible slippage during the tests. Rheometer operation and measurements were computer controlled.  
Yield stress and plastic viscosity play important roles in the material deformability and stability. To alleviate the impact of thixotropy, a pre-shearing period (about 20 s with 0.50 rps) was set before testing and meanwhile attempting to reduce the speed step by step in the tests. The tests were performed stepwise at 10 different speeds (0.50 rps, 0.45 rps, 0.40 rps, 0.35 rps, 0.30 rps, 0.25 rps, 0.20 rps, 0.15 rps, 0.10 rps, 0.05 rps). The raw data of torque and apparent viscosity were recorded and then converted into shear stress \(\tau\) and shear rate \(\dot{\gamma}\), respectively. The yield stress \(\tau_{0}\) and the plastic viscosity \(\mu\), as well as the parameter \(c\), can be determined subsequently from regression analysis with the modified Bingham model (Equation (3)). Each test was completed within 70 s; hence, the impact of the slump-flow loss and the aggregate transfer on the test results can reasonably be ignored.