esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Findings
### Predictors for Player Performance (Goals and Assists)
#### 5.1.3 Age Category  
In the field of football performance analysis, age is frequently seen as a relevant factor that may influence player performance, as shown in Figure 12. This research thoroughly examined player attributes and their relationship to performance indicators. Initial findings from correlation analysis and feature importance scores obtained by ML models revealed that age had a relatively minor impact on player performance, with correlation scores of \(-0.05\) indicating a weak negative correlation, and feature importance scores ranging from \(-0.05\) to \(0.032\), indicating that it is a weak predictor when it comes to predicting performance.  
However, a closer look at the top performers (Figure 12) revealed a dominance of players within the 'Prime' age group (23 to 31 years), suggesting that the prime years are likely the most productive due to a mix of experience, tactical understanding, and physical fitness. The 'Youngster' group, aged 16 to 22, was less represented but notably impactful, showcasing their potential. 'Veteran' players over 32 were scarcely found among the top ranks, indicating that while experience is beneficial, it might not be crucial for peak performance.  
The study also observed that age, while not a strong standalone predictor, is indeed influential during a player's prime years, as shown in Table 9. For instance, elite players  
Figure 11: Goals and Assists by Teams (Top 10).  
Figure 12: Age vs. Goals and Assists (Top 20).  
like [PERSON] (27), [PERSON] (28), and [PERSON] (26) not only played almost every match but also ranked highly in performance, aligning with the observation that prime-aged players in top teams tend to exhibit higher levels of performance.  
In conclusion, while individual variables such as 'Matches Played', 'Age Category', and 'Squad/Team' may not stand out as dominating predictors of goals and assists, they highlight a discernible trend. Exceptional players who consistently offer top-tier statistics tend to fall into specific age groups and are frequently associated with high-performing clubs. This insight emphasizes the intricate interplay of these variables, underlining their overall impact on a player's output in terms of goals and assists.
# Equivalent Simulation Study of Delta-Rotor Engine
## 3 Combustion Process Equivalence
To simplify the calculation, an SG (Savitzky-Golay) filter was first used to process and obtain a smoother heat release rate curve. The SG filter is a filtering algorithm based on the local polynomial least squares method, often used to smooth and denoise data. The greatest advantage is that it ensures that the shape of the data remains unchanged. The filter function is expressed by Equation (12), where \(P\) represents the original data, \(P^{\prime}_{j}\) represents the filtered data, \(C\) is the SG filter coefficient, and \(N\) is the data length. \[ \begin{array}{c}P^{\prime}_{j}=\frac{\sum_{i=-m}^{i=m}C_{i}P_{j+i}}{N}\\ N=2m+1.\end{array}  \tag{12}\]  
After determining the crankshaft angle \(T_{MAX}\) with the maximum heat release rate, the equivalent heat release rate curve for \(T\leq T_{MAX}\) was obtained first. The fitness function \(Y_{1}\) at this stage is given as in Equation (13), where \(T_{1}\) is the ignition time. After genetic algorithm optimisation, the best \(p_{1}\), \(\beta_{1}\), and \(\gamma_{1}\) can be obtained. \[Y_{1}=\frac{\sum_{i=T_{MAX}}^{i=T_{MAX}}f_{1}(i,p_{1},\beta_{1},\gamma_{1})-P ^{\prime}_{i}}{T_{MAX}-T_{1}}. \tag{13}\]  
After determining the relevant parameters of the first combustion, the heat release rate curve of \(T\geq T_{MAX}\) is considered to be equivalent. The fitness function \(Y_{2}\) at this stage is as shown in Equation (14), where \(T_{2}\) is the end time of the combustion. After optimisation by genetic algorithm, the best \(p_{2}\), \(\beta_{2}\), and \(\gamma_{2}\) can be obtained. The relevant parameters of the equivalent heat release rate function can be determined using the aforementioned calculations ([PERSON] et al., 2023)[25]. \[Y_{2}=\frac{\sum_{i=T_{MAX}}^{i=T_{2}}f_{1}(i,p_{1},\beta_{1},\gamma_{1})+f_{2 }(i,p_{2},\beta_{2},\gamma_{2})-P^{\prime}_{i}}{T_{2}-T_{MAX}}. \tag{14}\]  
Figure 6: Calculation flowchart of equivalent heat release rate.
# Earthquake Prediction for the Duzce Province in the Marmara Region Using Artificial Intelligence
## 4 Experimental Datasets  
Based on previous studies, we carried out adjustments and additions to our dataset. The seismic data (earthquake magnitude, latitude, longitude, and depth) were provided by the Kandilli Observatory. The seismic data contain records of earthquakes with magnitudes of 3 and above that occurred between 1990 and 2022 in the Duzce Province in the Marmaa region.  
In the estimation phase, the seismic data are used to train and test the model. RNN was used as the model. To increase the robustness of the model, the distance of the Moon from the Earth, which is assumed to be effective in the formation of an earthquake, is added. The b value and d value are calculated for each earthquake that occurred within the 33 years which had a magnitude greater than 3.0 and are used as another dataset and included in the model.  
To test the accuracy of the model, first, the earthquakes that occurred in November and December 2022 were compared to the data predicted using the model. Second, the August 1999 earthquakes with higher intensity were compared with the data estimated using the model.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 5 Performance Evaluation
### Result Analysis
#### 5.3.2 Average Hop Count  
When a dense network is considered, the number of nodes is high and is located near each other. More nodes are deployed in the small forest region. To perform DODAG for all the nodes in the network, it is efficient to do routing using RPL only for aggregators. The average number of nodes in the DODAG formation towards the root varies for both cases. Figure 5 represents the average number of hop count in the final DODAG from source to DODAG ROOT for both protocols. The average hop count of CAA-ERPL is 2, 3, 5, 7, 9 and, for E-RPL, is 3, 4, 6.5, 8, 10. In CAA-ERPL, the average hop count decreases, as it processes first data aggregation and then routing. So, it provides the stability in the network, though it is dense.
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 3 Results
### System Architecture and Key Features
#### 3.4.2 Security and Access Control  
Robust security measures are in place across all layers to safeguard against unauthorized access. Network segmentation ensures that machines are assigned specific roles and access levels, with the front end and back end in the DMZ handling external requests while other critical components, such as the scenarios setup and the HPC area, are restricted to internal access via VPN. Access to the front end can be configured as either public or private depending on the application setup. If authentication is enabled, access to the front end is managed through JWT-based tokens, ensuring secure user identification and role-based access. This setup provides flexibility in managing who can access different levels of data and functionalities. Additionally, the physical and logical isolation of the HPC cluster provides further protection, shielding it from external access.
## 3 Results and Discussion
### Lignin Extraction from Ball-Milled Pinewood  
In order to find out the effects of treatment conditions on the lignin yield, the lignin extraction efficiency, based on initial raw lignin contents, was also determined and is shown in Table 2. Briefly, the filtrate obtained from all of the six performed experiments under our described conditions was first  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**GVL Extraction Conditions**} & \multirow{2}{*}{\begin{tabular}{c} **Solid** \\ **Recovery** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Solid** \\ **Recovery** \\ **(\%)** \\ \end{tabular} } & \multicolumn{3}{c}{**Solid Composition (\%)**} & \multirow{2}{*}{
\begin{tabular}{c} **Solution pH** \\ **after Complete** \\ **Reaction** \\ \end{tabular} } \\ \cline{2-2} \cline{6-8}  & **Time (hours)** & & & & & & & \\ \hline \multirow{8}{*}{**Pinewood**} & **2** & **140** & **86** & **42** & **5** & 14 & 29 & 6.0 \\ \cline{2-8}  & **4** & 140 & 80 & 38 & 6 & 12 & 30 & 5.9 \\ \cline{1-1} \cline{2-8}  & **2** & 160 & 70 & 37 & 4 & 15 & 26 & 5.5 \\ \cline{1-1} \cline{2-8}  & **4** & 160 & 65 & 34 & 10 & 12 & 25 & 5.2 \\ \cline{1-1} \cline{2-8}  & **2** & 180 & 60 & 28 & 9 & 10 & 24 & 4.0 \\ \cline{1-1} \cline{2-8}  & **4** & 180 & 50 & 25 & 8 & 12 & 30 & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effects of pretreatment conditions on the chemical composition of pinewood biomass.  
added to 800 mL of DI water and left overnight to settle down the lignin as precipitates. Afterward, these fractions were centrifuged using a laboratory centrifuge machine. The supernatant was decanted off carefully, and the leftover solid fractions were subjected to freeze-drying for 24 h. These freeze-dried lignin samples were used for their characterization and further analysis. The lignin yield, in a somewhat expected manner, showed a gradual trend in comparison with the treatment severity. For example, starting from less harsh treatment conditions of 140:2, it was quite a low yield, with only 3.0% lignin. However, as the thermal severity was increased over a long retention time, the yield was correspondingly increased to 33%. This trend showed that the ball-milled biomass was dissolved in GVL and it was likely to separate out the acetyl contents to create an acidic environment as the treatment severity was prolonged. The highest lignin yield of 33% was obtained at 180:4, the extreme conditions in our case. Interestingly, the biomass solid recovery and the corresponding pH value of the final solution was around 50% and 3, respectively. This shows that there was indeed some acidic environment due to harsher treatment conditions.
## 4 Comparative Analysis of Pile Driving Formulas
The analysis of bearing capacity errors compared to the PDA (Pile Driving Analyzer) results reveals that the original [PERSON] formula has the highest absolute mean error at 56.77% and a corresponding standard deviation of 8.85%. This indicates that the original [PERSON] formula significantly deviates from the PDA values, showing less reliability but small variability, showing high consistency. The high standard deviation further underscores the inconsistency of this formula in predicting accurate bearing capacity values. In contrast, the Hiley formula demonstrates a more reliable performance, with an absolute mean error of 11.99% and a standard deviation of 11.52%. These values suggest that the [PERSON] formula estimates closer to the PDA results and exhibits moderate variability. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Formula**} & **Mean Error** & **Absolute Mean** & **Absolute Error** \\  & **(\%)** & **Error (\%)** & **Std. (\%)** \\ \hline Hiley & 9.49 & 11.99 & 11.52 \\ Danish & 10.71 & 15.78 & 15.33 \\ Gates & \(-\)11.78 & 18.27 & 10.26 \\ Original Gates & \(-\)56.77 & 56.77 & 8.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of bearing capacity estimates using four different pile driving formulas and PDA results (mean error, absolute mean error, and standard deviation of absolute mean error). Figure 2: Comparisons of bearing capacities (Qu) determined using 258 PDA data points and bearing capacities calculated using pile driving formulas (**(a)** Hiley, **(b)** Danish, **(c)** Gates, and **(d)** original Gates). The Danish and Gates formulas also show moderate errors, but the Danish formula exhibits the highest absolute error standard deviations, meaning it is less consistent. However, they are generally more accurate than the original Gates method. Overall, the Hiley method stands out as the most consistent and reliable among the evaluated methods for estimating bearing capacity. The mean errors reveal that both the Hiley and Danish formulas have a tendency to overestimate the bearing capacity, though the overestimation is relatively small. The Gates and original Gates formulas, on the other hand, tend to underestimate the bearing capacity, with the original Gates formula showing a particularly large underestimation. This analysis highlights the importance of understanding the direction and magnitude of biases in different estimation formulas, with the Hiley formula emerging as the most balanced and reliable among those analyzed.
# A Review of Distribution Grid Consumption Strategies Containing Distributed Photovoltaics
## 2 Principle of Photovoltaic Power Generation and Consumption Mode  
### Principle of Photovoltaic Power Generation  
The principle of photovoltaic (PV) power generation refers to the process of directly converting solar energy into electricity by utilizing the photovoltaic effect of semiconductor materials (e.g., silicon, germanium, etc.) [4]. The most important original in this process is the photovoltaic cell; when the sunlight irradiates the surface of the photovoltaic cell, the photons therein excite the electrons in the semiconductor, causing them to jump into the conduction band, forming free electron and hole pairs. These free electrons and holes combine near the P-N junction to form an electric current, which generates electricity. Figure 3 shows the equivalent circuit of a photovoltaic cell [5]. A complete photovoltaic power generation system consists of photovoltaic modules, racking, inverter, monitoring system, etc. The inverter converts the DC power to AC power to meet the demand of the grid or the user. The monitoring system is responsible for real-time monitoring of the operational status of the system to ensure power generation efficiency and safety.  
As can be seen from Figure 3, the main components of a PV cell are the current source, the external load, and so on. In practice, a single photovoltaic cell is not enough to be utilized, so it is necessary to form a photovoltaic array by series or parallel connection to obtain a large enough output power.  
The output characteristics of PV cells are generally divided into several parts: maximum power point, peak power, open circuit voltage, short circuit current, etc. The output characteristic curves of PV cells are shown in Figures 4 and 5, which are shown in Figure 4 P-U characteristic curves with light and Figure 5 P-U characteristic curves with temperature, respectively [6].  
Figure 3: Equivalent circuit of a photovoltaic cell.
## 3 Underwater Wireless Optical Communication System  
### Structure of UWOC  
Figure 6 shows the structure of the UWOC system between the AUV and the optical modem outside the water. Although the UWOC system includes both a transmitting and receiving unit and supports bi-directional communication, Figure 6 presents these units separately to clarify the distinct processes of sending and receiving.  
The transmitter on the AUV sends data in protocol signals, such as UART, RS232, and Ethernet. To reduce the noise of the corresponding signal data, the return-to-zero (RZ) modulation technique is used [20; 21]. In short-range scenarios, RZ modulation exhibits higher receiver sensitivity than non-return-to-zero (NRZ) modulation [22]. In this paper, RZ modulation was adopted since UWOC was used to remotely control an AUV at distances within a few meters. Modulated data are amplified by the power amplifier and transmitted through the LED. On the receiving side, a current is generated by detecting the LED light with the photodiode, and the corresponding current is signal processed to transmit data to the host.  
Figure 5: Developed GUI for the AUV.
## 4 Anomaly Detection Method Based on DeepESN
### Model Evaluation Metrics  
The metrics proposed to quantify the obtained results can be divided into those that evaluate effectiveness and those that evaluate efficiency.  
* Effectiveness metrics All of the effectiveness metrics are executed on the test set. They are used to evaluate the entirety of the methodology performing a binary classification task. For this reason, we used the main metrics in binary classification scenarios, which are accuracy, precision, recall, and F1 score. The accuracy provides the percentage of correct predictions: \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (12) where \(TP\) stands for true positive, \(TN\) for true negative, \(FP\) for false positive (1), and \(FN\) for false negative (0). The precision provides the percentage of true positives out of all positive predictions: \[Precision=\frac{TP}{TP+FP}\] (13) The recall provides the percentage of true positives out of all positive real examples: \[Recall=\frac{TP}{TP+FN}\] (14) The F1 score provides a balance between precision and recall: \[\text{F1}=2\cdot\frac{Precision\cdot Recall}{Precision+Recall}=\frac{2 TP}{2 TP+ FP+FN}\] (15)
* Efficiency metrics The metrics used to evaluate the efficiency of the methodology include time-related measures and CO\({}_{2}\) emissions. In terms of time, the seconds spent to train the model on the training set (training time) and those spent to predict all the results of the test set (inference time) have been measured. The time metric tells us how fast the  
Figure 7: System architecture.  
model is during the training and inference phases. Additionally, the CO2 emissions are measured during the training phase and inference phase. This metric indicates the volume of carbon dioxide emissions (kgCO2) generated during the training and inference process. The CO2 emissions are computed starting from power resource consumption. Specifically, the emissions are measured using the codecarbon python library version 2.7.1, which allows for the registration of the kWh for the CPU and RAM of the device. kWh are converted into CO2 emissions by multiplying them by the carbon intensity of the current country in kg/kWh. The formula used is the following:  
\[\text{ emissions}=(\text{CPU\_kWh}+\text{RAM\_kWh})\cdot\text{cci} \tag{16}\]  
CPU_kWh and RAM_kWh are monitored and measured during the run, while the country's carbon intensity (cci) is a constant taken from the work in [64] where are the carbon intensity of each country is listed. In our case, the cci is 0.373 kg/kWh, which is the carbon intensity for Italy in the year 2022.
## 3 Application to an Octagonal Viscoelastic Bar
### Dispersion Curves and Wavestructures  
In Figure 5 the dispersion curves for the octagonal bar are given in terms of: (a) energy velocity and (b) attenuation. These plots have been obtained by using the mesh with 512 CTS triangles, that provides accurate results up to 255.2 kHz, and considering an attenuation threshold of 100 dB/m (i.e. approximately 11.5 Np/m). In other words, all the modes with attenuation higher than 11.5 Np/m are not represented in this figure. This is why the energy velocity branches of high order modes result interrupted when they approach the frequency axis for very low energy speed (high modes attenuation).  
As it can be seen, below 100 kHz only three guided modes are clearly visible. In analogy with waves propagating in an equivalent cylindrical steel rod, we can term them as Flexural F(1,1),  
Figure 4: Different plane meshes of CST triangles.  
Figure 5: Dispersion curves for the octagonal bar of overall radius \(R=10\) mm, in terms of (a) energy velocity and (b) attenuation (512 CST triangles mesh).  
Torsional T(0,1) and Longitudinal L(0,1) wave. In reality in the considered damped octagonal bar four propagating modes exist in the 0-100 kHz frequency range with attenuation smaller than 100 dB/m. In fact, two Flexural modes with identical wavenumber but different mode shape, one that presents mainly bending behavior around the \(x\)-axis, F\({}_{\rm X}\)(1,1), and the dual flexural mode with main bending around the \(y\)-axis, F\({}_{\rm Y}\)(1,1), exist. These two flexural modes with identical dispersive properties are indicated in Figure 5 with F(1,1) only. Similarly, the high order flexural modes always appear in couples.  
The T(0,1) is the only nondispersive mode. As it can be noted from Figure 5(b), based on the hysteretic rheological model assumed, the nondispersive T(0,1) mode has attenuation linearly dependent with the frequency, while all the other modes present a nonlinear dispersive attenuation behavior. The analogy of these modes with those of the equivalent cylindrical rod can also be confirmed by a closer look to their wavestructures. The normalized real component of the F\({}_{\rm Y}\)(1,1), T(0,1) and L(0,1) wavestructures, calculated at around 30 kHz, are represented in Figure 6 (red line) along with the undeformed reference mesh over the waveguide cross-section (black line). In order to have easy to read representations, the 32 CST mesh has been used.  
While the F\({}_{\rm X}\)(1,1), F\({}_{\rm Y}\)(1,1) and L(0,1) waves are characterized by non null displacement components in all directions, the T(0,1) mode presents zero displacement in the \(z\)-direction and for this reason presented in a plane representation. It is worth noting that the dominant axial \(z\)-displacement component, if compared with the in-plane \(x\)-\(y\) components for the F\({}_{\rm Y}\)(1,1) and the L(0,1) modes, is empathized by using a different scale for the \(z\)-axis in the plots of Figure 6.
## 2 Materials and Methods
### Implementation of Laser Profilometry and Sensor Movement  
In this current study, a Keyence LJ-X8400 (Osaka, Japan) industrial grade laser profilometry system, including a LJ-X8000E control unit, was mounted outside the installation space, linearly translated alongside the process window by a linear unit. To avoid image distortions, the curved protective plastic window of the EOS machine was removed. The remaining planar laser protection glass was dimensioned for wavelengths of 10.20-10.80 \(\upmu\)m, leading to no interferences in the linear profilometry laser that operates at a wavelength of 405 nm. Pre-investigations revealed no influence on the profilometry results by the laser protection glass.  
Mounting the profilometry sensor outside the installation space at a certain distance led to two main advantages: no cooling is required, and the complete powder bed surface can be captured in the x- and y-directions. The extended distance to the powder bed surface leads to a reduction in resolution in the y-direction, but this was regarded as negligible due to the focus on vertical absolute data. Encoding was realized by the linear unit, resulting in 25,000 profile steps due to a powder bed width of 250 mm and an encoder resolution of  
Figure 7: Thermal grayscale imaging (top) and related color map (bottom) of the powder bed surface temperature distribution at certain process steps: (**a**) after coating a new layer of powder, (**b**) after surface heating time, and (**c**) after laser exposure. Clear visibility of pre-sintered areas through new-coated layer of powder (**a,b**), no visible defects on powder bed surface.  
10 \(\upmu\)m (x). The laser profilometry system provides a vertical resolution of 5 \(\upmu\)m according to data sheet [47]. Further parameters of the laser profilometer are summarized in Table 1.  
Since no CAD data is available, a specific reconstruction of the outer shell of the PBF-LB/P machine and proper fittings were necessary to mount the laser triangulation system, as can be seen in Figure 8. Further adaptations of the EOS system were made by implementing mechanical end-switches at the coater to synchronize sensor movement of the linear unit at the deployed SPS.