esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aimed to examine the influence of the Video Assistant Referee (VAR) system on match performance indicators in professional football, specifically within the Turkish Super League. The objectives were two-fold: (i) to compare match variables such as yellow cards, red cards, goals, penalties, fouls, and offsides between seasons with and without VAR, and (ii) to analyze the evolution of these variables across four consecutive seasons following VAR implementation. A total of 2636 matches were analyzed, comprising 1224 matches played without VAR (2014-2018) and 1412 matches played with VAR (2018-2022). Match data were obtained from InStat Scout(r) and included key indicators directly associated with referee decisions. Statistical analyses included the Independent Sample T-Test to assess differences between the pre- and post-VAR periods, One-Way ANOVA with Tukey post hoc tests to examine seasonal trends post-VAR, and generalized linear models to identify the effects of VAR implementation on each performance variable. The results revealed significant reductions in fouls, yellow cards, and offsides (\(p<0.001\)), and a significant increase in penalties awarded (\(p<0.001\)) following the introduction of VAR. No statistically significant differences were found for red cards or goals. Furthermore, the number of fouls committed showed a consistent decline across each season after VAR implementation, suggesting a long-term behavioral adaptation by players. These findings underscore the lasting impact of VAR on the dynamics of professional football matches and highlight the need for players, coaches, and referees to accordingly adapt their strategies. This study contributes to the growing body of evidence supporting VAR's role in improving decision accuracy, though its broader implications for game flow and player performance warrant further investigation.  
V 2025 15 4789 1
# L-Band Erbium-Doped Fiber Optimization and Transmission Investigation +
## 4 Conclusions  
The combination of mode division multiplexing technology and the L-band is a promising way to improve transmission capacity at a low cost. Due to the high output power difference among signal modes from the conventional L-band EDFA, it needs to be redesigned urgently. We optimized the FM-EDF with the particle swarm optimization algorithm and manufactured the optimized fiber. Then, the performance of the EDF was verified with experiments. The modal gain and DMG indicate good performance of the designed FM-EDFA: In the wavelength range between 1575 nm and 1610 nm (i) the maximum modal gain is 22.22 dB, and (ii) the maximum DMG is less than 2.28 dB. The FM-EDFA was also tested in a DSP-free transmission system. (iii) When the backward pump power reaches 450 mW, the BER among all mode channels in the wavelength range between 1570 nm and 1610 nm is below 1 \(\times\) 10\({}^{-5}\).  
Figure 12: (**a**) The BER of three signal mode channels in the wavelength range of 1570 nm to 1610 nm; (**b**) ASE spectrum.  
**Author Contributions:** Conceptualization, L.P.; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); writing--original draft preparation, K.H.; writing--review and editing, [PERSON]; visualization, J.L.; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities, grant number 2024 JBMC032.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Research data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Yangtze Optical Fibre and Cable Joint Stock Limited Company (YOFC). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Friction Force Prediction at TCI Based on TPF
### Prediction of Friction Component at TMI (\(F_{m}\))  
The tool-matrix friction component is an important component of cutting force. The rake face of the tool and the SiCp/Al are squeezed. Due to the large pressure between the bottom layer of the chip, the rake face of the tool and the high-temperature adhesive friction, a small part of the material adheres to adhesive wear on the tool. A study proposed by [PERSON] et al. [28], shows that the tool-chip interfaces are divided into sticking and sliding zones. Figure 4 shows a schematic presentation of the contact region of the tool-chip during the EVC. The plane OE and EF represent the sticking region and the sliding region, respectively. The plane OF and DQE are the tool-chip contact interface and the cutting interface, respectively. This friction behavior is changed during the EVC cutting stroke, and only part of the trajectory participates in the cutting process. As shown in Figure 4, the sticking region and the sliding region exist in chip generation behavior instead of tool-chip contact.  
Figure 4: Friction mechanism of TMI in aluminum matrix.  
The elliptical cutting trajectory makes the EVC cutting process go through ironing, ploughing, and finally turning up the chips to form a plastic chip layer. Vibration in the Y direction will produce micro-cutting depth movements to reduce cutting forces. At the same time, coupling with vibration in the X direction will achieve intermittent cutting. The friction component between the tool and matrix can be calculated as an elliptical vibration cutting aluminum material, and it is obtained by the following equation [27]:  
\[F_{M}=0.625\cdot\tau_{(tool)}\cdot L_{(t)}\cdot r\sqrt{\frac{B_{r}}{\sin\alpha^ {*}}} \tag{24}\]  
\(\tau_{(tool)}\) is the shear strength of the tool material, \(L_{(t)}\) is the length of the active part of the cutting edge, \(B_{r}\) is the Briks similarity criterion (Astakhov) that can be calculated by the equation [27]:  
\[B_{r}=\frac{\cos\alpha}{\varsigma-\sin\alpha} \tag{25}\]  
\(\varsigma\) is the chip compression ratio [29].  
The friction component occurring at the TMI is considered to be the performance result of EVC cutting aluminum substrate. The flow stress of the part is affected by the cutting temperature owing to the matrix aluminum's thermoelectricity. The Johnson-Cook (JC) model was selected as the constitutive relationship model of the matrix aluminum [30], which is given as:  
\[\tau_{S}=[1-T^{*m}]\cdot[1+C\ln\epsilon^{*}]\cdot[p+k\cdot\epsilon^{n}] \tag{26}\]  
The parameters of the SiCp/Al Johnson-Cook model are shown in Table 1. Variables \(\epsilon^{*}\) and \(T^{*}\) can be obtained by Equations (27) and (28) [28; 31; 32]:  
\[\left\{\begin{array}{l}\varepsilon=\cot\phi_{c}+\tan(\phi_{c}-\alpha)\\ \varepsilon^{*}=\frac{\varepsilon}{\varepsilon_{0}}\\ T^{*}=\frac{T-T_{r}}{T_{m}-T_{r}}\end{array}\right. \tag{27}\]
# Adaptive PI + VPI Harmonic Current Compensation Strategy under Weak Grid Conditions
## 3 Adaptive SRF-PLL Based on a Lattice Notch Filter  
### Lattice Notch Filter  
Considering the problem of the limit cycle in the adaptive process of the direct form digital filter, a lattice filter is selected in this paper. Lattice filters are constructed around second-order all-pass functions.  
\[V(z)=\frac{\sin\theta_{2}+\sin\theta_{1}(1+\sin\theta_{2})z^{-1}+z^{-2}}{1+\sin \theta_{1}(1+\sin\theta_{2})z^{-1}+\sin\theta_{2}z^{-2}} \tag{27}\]  
The above all-pass function satisfies \(\left|V_{1}(e^{i\omega})\right|=1\) for all \(\omega\), which can be written as \(V(e^{i\omega})=e^{i\phi(\omega)}\), where \(\phi(\omega)\) is the phase-frequency characteristic function of \(V(e^{i\omega})\), is a \(\omega\) monotonically increasing function and satisfies the boundary constraint \(\phi(0)=0\), \(\phi(\pi)=2\pi\). Then the following two functions are defined as:  
\[N(z)=(1+V(z))/2 \tag{28}\]  
\[P(z)=(1-V(z))/2 \tag{29}\]  
Then \(N(z)\) and \(P(z)\) are a pair of filters with complementary co-frequency characteristics, where \(N(z)\) is a notch filter and \(P(z)\) is a bandpass filter. The transfer functions are:  
\[N(z)=\frac{1+\sin\theta_{2}}{2}\frac{1+2\sin\theta_{1}z^{-1}+z^{-2}}{1+\sin \theta_{1}(1+\sin\theta_{2})z^{-1}+\sin\theta_{2}z^{-2}} \tag{30}\]  
\[P(z)=\frac{1-\sin\theta_{2}}{2}\frac{1-z^{-2}}{1+\sin\theta_{1}(1+\sin\theta_{ 2})z^{-1}+\sin\theta_{2}z^{-2}} \tag{31}\]  
It is not difficult to find that Equation (30) is the realization form of Equation (22), and the two Equations have the same frequency characteristics. The realization of the notch filter \(N(z)\) is shown in Figure 7b. The inner part of the inner dashed box is the structure of the lattice second-order all-pass filter. Figure 7a shows the structure of the lattice unit.
# A Comparative Study of SOx, NOx, PM2.5 and PM10 in the UK and Poland from 1970 to 2020
###### Abstract  
Presently, there is growing discourse surrounding climate change, global warming, and the possibility of urban smog. Daily, we encounter air pollutants unwittingly, often oblivious to the substantial adverse impacts they may impart on our health. This manuscript furnishes a thorough examination of the shifts in the concentrations of distinct air pollutants, namely, sulphur oxides (SOx), nitrogen oxides (NOx), and particulate matter PM2.5 and PM10, within the contexts of the United Kingdom (a country progressively transitioning to alternative energy sources) and Poland (a country in the EU with the second lowest climate policy evaluation in the climate change performance index or CCPI) from 1970 to 2020. This comparative study between the two countries clearly showed the importance of the transition to clean energy sources. The UK's efforts towards cleaner energy sources have led to reduced emissions of sulphur oxides (SOx) and particulate matter. In contrast, Poland's heavy dependence on energy primarily from coal over the years has resulted in elevated pollution levels, notably in the concentration of pollutants such as particulate matter. This study also revealed that changes in emissions predominantly stem from technological advancements and economic activities. Additionally, political regulations and the gradual phase-out of specific fuels or energy-rich substances and their substitution with alternative energy sources have also imparted considerable influence.  
air pollution; sulphur oxides; nitrogen oxides; particulate matter; long-term trends +
Footnote †: journal: Applied sciences
# Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems
## 4 SDAG Model and HSDFG Scheduling
### HSDFG Scheduling
#### 4.3.3 Core Selection  
The PAPS algorithm does not use a policy for the core selection phase, but in our experiments we compare the load balancing policy with the earliest finish time(EFT) policy, and the results show that the EFT policy performs better. We therefore use the EFT policy in the core selection phase, defined as follows.  
\[min(cl(c_{i})),\ i=1,2,\ldots,M \tag{19}\]
## 4 Forecasting Results and Discussion
### Multistage Models with Similar Algorithms  
As discussed in Section 4.1, the LSTM outperformed the LASSO, SVR, and FBP algorithms. Accordingly, the multistage models are developed to see whether the performance of those models can be improved. For this, we chose to create the multistage models using only the SVR and FBP models, given that the LASSO algorithm gave poor results.  
Table 5 and Figure 8 show a performance comparison between the two multistage models constructed from the SVR and the FBP. The results show a significant advantage to the FBP-FBP (with and without consideration of temperature as a feature) compared to all other cases, including the SVR-SVR with and without consideration of temperature and the FBP-FBP with temperature. The recorded metrics are an \(R^{2}\) value of 68.81%, an MAE of 2.51, and an RMSE of 3.25. Once again, the effect of the temperature is very minor and only accounted for an increase of 0.75% in accuracy.  
Figure 8: MAE, \(R^{2}\), and RMSE values for the multistage similar models with and without temperature as a feature. (**a**) Mean Absolute Error (MAE). (**b**) Coefficient of Determination (\(R^{2}\)). (**c**) Root Mean Square Error (RMSE).  
Figure 7: Testing data vs LSTM forecasted data of the hospital’s electricity demand.  
The output from the FBP-FBP multistage model without temperature as an extra feature is plotted to visualise the performance (see Figure 9). As can be seen, the slightly poor accuracy reported in the \(R^{2}\) value of 68.06% is visually clear in the comparison of the predicted data with the observed/testing data.
# Evaluation of Rock Brittleness Index under Dynamic Load
## 2 Materials and Methods
### Test Specim  
Five rock materials, including fine granite, coarse granite, shale, marble, and sandstone, were selected as experimental research objects. All rocks had good integrity, uniform and dense texture, and no defects visible to the naked eye. The rock specimens were prepared by drilling and cutting. The specimens were divided into three categories. The first was a standard cylindrical specimen with a diameter (D) of 50 mm and a height (H) of 100 mm. The second was a standard Brazilian split specimen with a diameter (D) of 50 mm and a height (H) of 25 mm. The third was the dynamic impact compression specimen with a diameter (D) of 50 mm and a height (H) of 50 mm. Non\(-\)parallel end faces and axial deviations were within the allowable limits of the International Society of Rock Mechanics (ISRM) standards. Figure 1 shows the specimens of five kinds of rocks after processing.  
In all tests, each rock type included three parallel specimens. The relevant parameters measured in parallel specimens were basically the same. The standard deviations of uniaxial compressive strength and dynamic compressive strength were below 5 MPa, the standard deviations of static Brazilian splitting strength were below 1 MPa, and the standard deviations of dynamic Brazilian splitting strength were below 3 MPa. Table 2 shows the average physical and mechanical parameters of different rocks; the average values of the parameters were taken from the three specimens.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Rock Type** & **Density (g/cm\({}^{3}\))** & \(\sigma_{cs}\) (MPa) & \(\sigma_{cd}\) (MPa) & \(\sigma_{ts}\) (MPa) & \(\sigma_{td}\) (MPa) & \(E_{s}\) (GPa) & \(E_{D}\) **GPa** & \(\hat{\varepsilon}_{cd}\) **(s\({}^{-1}\))** \\ \hline Fine & 2.78 & 168.35 & 417.41 & 11.88 & 27.65 & 39.50 & 71.05 & 105 \\ Coarse & 2.64 & 137.70 & 306.84 & 7.42 & 15.25 & 31.07 & 41.37 & 104 \\ granite & 2.37 & 117.17 & 209.55 & 5.32 & 8.72 & 24.84 & 25.19 & 90 \\ Shale & 2.83 & 101.37 & 190.31 & 4.28 & 7.81 & 35.10 & 38.52 & 103 \\ Sandstone & 2.32 & 79.36 & 154.72 & 4.80 & 8.13 & 14.19 & 18.50 & 95 \\ \hline \end{tabular}
\end{table}
Table 2: The average physical and mechanical parameters of rock specimens (\(\sigma_{cs}\) is uniaxial compressive strength; \(\sigma_{cd}\) is dynamic compressive strength; \(\sigma_{ts}\) is static Brazilian splitting strength; \(\sigma_{td}\) is dynamic Brazilian splitting strength; \(E_{S}\) is the static elastic modulus; \(E_{D}\) is the dynamic elastic modulus; \(\hat{\varepsilon}_{cd}\) is the strain rate of the specimens in a dynamic compressive test).  
Figure 1: Five types of rock specimens.
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 2 Background and Related Work
### Wireless ECG Sensor
Furthermore, the sensor has also been used for: abdominal fetal ECG monitoring, ECG monitoring in veterinary practice, on dogs, cats and horses, biometric authentication, and heart rate variability (HRV) biofeedback assessment. Its exceptionally lightweight design allows for unobtrusive use during sports activities or during exhaustive physical work as well [26]. In addition to ECG, it has been demonstrated that other features can also be extracted from the measured potential difference on the body's surface, such as muscle activity and respiration [22; 27]. Moreover, the standard 12-lead ECG can be synthesized from the measurements from three sensors placed in appropriate positions [28; 29; 30]. Lastly, the future addition of a temperature sensor and accelerometer [22] promises to transform the next generation of wearable sensors to a truly multifunctional design.
# Review of Structural Modal Tracking in Operational Modal Analysis: Methods and Applications
###### Abstract  
Modal tracking has evolved into a crucial research domain over the past decades. It synergizes with automatic modal parameter estimation (AMPE) to achieve automatic operational modal analysis (AOMA). Modal tracking aims to establish the dynamic correspondence between the currently identified mode and historical reference modes, thereby following the evolution of modal parameters over time. Established modal tracking methods involve two stages: reference mode determination and current mode linking. This paper reviews the developments of modal tracking from both methodological and application perspectives, emphasizing the pivotal challenges in existing results and suggestions for future research. Additionally, a detailed summary of the distinct strategies employed across two stages and the application of modal tracking in various types of structures is provided. Future research should focus on the adaptive updating of reference modes and linking thresholds while enhancing the capability to eliminate spurious modes and track rapidly changing physical modes. This review seeks to assist researchers in efficiently grasping the advancements in modal tracking and in developing more suitable and robust methods for civil engineering structures in the future.  
structural health monitoring; operational modal analysis; modal tracking; modal evolution; automatic modal estimation
# Rates of High-Temperature Evaporation of Promising Fire-Extinguishing Liquid Droplets
## 3 Experimental Setup and Methods
### Heating in the Air Stream  
The scheme of droplet heating is similar to the one used in experiments of Reference [43]. The study of evaporation of single liquid droplets during their motion through a high-temperature gas medium employs a stand (Figure 1b) with the main elements: a high-speed video camera with a shooting frequency of up to \(10^{5}\) frames per second and a maximum shooting resolution of \(1280\times 1280\) pixels, a measuring complex consisting of a high-speed analog input Board of the \"National Instruments 9219\" type with a sampling rate of 100 points/s, and a synchronizing processor with signal discretization not exceeding 10 Ns, supporting external and internal launch modes.  
To generate a heated air flow (Figure 1b) with controlled parameters (temperature \(T_{\mathrm{a}}\) and velocity \(U_{\mathrm{a}}\)), we used the unit consisting of an industrial heater Leister LE 5000 HT (temperature range of 20-1000 \({}^{\circ}\)C), a fan Leister ROBUST (flow rate of 0.5-5 m/s), and a hollow transparent cylinder (with outer diameter of 0.1 m and wall thickness of 2 mm) of quartz glass (with maximum permissible temperature of about 1800 \({}^{\circ}\)C). The temperature was monitored using the controller Leister CSS (with temperature error of \(\pm 1\)\({}^{\circ}\)C). The air velocity was changed using the frequency Converter LEISTER FC 550.  
Using a dispenser, a drop of liquid was placed on the holder (with a tip diameter of 0.25 mm). The latter was moved into the channel with heated air using an automated coordinate mechanism. The optimal rate of introduction (about 0.1 m/s) was determined empirically to eliminate the conditions of the drop break from the holder. Droplets were introduced into a muffle furnace in a similar way (p. 3.3). Typical video frames obtained during experiments with droplets of extinguishing agents in the air stream are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameters** & **Water** & \begin{tabular}{c} **Bentonite** \\ **Suspension** \\ \end{tabular} & \
\begin{tabular}{c} **Bischofite** \\ **Solution** \\ \end{tabular} & **EA-5 Solution** &
\begin{tabular}{c} **Foaming Agent** \\ **Emulsion** \\ \end{tabular} \\ \hline \(\theta\), \({}^{\circ}\) & 91.3 & 88.5 & 78.3 & 53.6 & 22.6 \\ d, mm & 3.41 & 3.45 & 3.78 & 4.74 & 8.16 \\ h, mm & 1.63 & 1.58 & 1.45 & 1.01 & 0.36 \\ S, mm\({}^{2}\) & 17.81 & 17.51 & 18.16 & 21.21 & 52.30 \\ \hline \end{tabular}
\end{table}
Table 2: Geometric characteristics of a sessile drop of the fire extinguishing agent on the used substrate.  
Figure 4: Typical frames of the studied liquid droplets, located on the holder in the flow of heated air.  
Figure 3: Shadow images of droplets of the studied fire-extinguishing compositions when registering their geometric characteristics during the interaction with the substrate (\(T_{\mathrm{sub}}\approx 25\)\({}^{\circ}\)C).
# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Location** & **Mean Interval (days)** & **Standard Deviation (days)** \\ \hline \multirow{8}{*}{2019} & As & 8.50 & 6.80 \\  & Lannas & 7.39 & 6.09 \\  & Ojebyn & 5.11 & 3.74 \\  & Robäcksdalen Field 1 & 3.89 & 2.41 \\  & Robäcksdalen Field 2 & 4.00 & 2.50 \\  & Robäcksdalen Field 3 & 4.00 & 2.41 \\  & Robäcksdalen Field 4 & 4.83 & 3.30 \\  & Robäcksdalen Field 5 & 7.00 & 7.47 \\ \hline \multirow{8}{*}{2020} & As & 3.38 & 4.08 \\  & Lannas & 6.95 & 8.39 \\  & Ojebyn & 4.13 & 3.80 \\  & Robäcksdalen Field 1 & 4.06 & 3.90 \\  & Robäcksdalen Field 2 & 4.06 & 3.59 \\  & Robäcksdalen Field 3 & 5.35 & 5.67 \\  & Robäcksdalen Field 4 & 4.58 & 3.98 \\  & Robäcksdalen Field 5 & 5.00 & 4.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The mean and standard deviation values of the intervals between available cloud-free Sentinel-2 images from May to September in 2019 and 2020. Figure 9: Distribution of all of the available Sentinel-2 images (black dots) and available cloud-free Sentinel-2 images (colored dots) during the growing season (May–September) in 2019 and 2020 for different study locations. factors, such as soil optical properties, and compensate for limited training data. The integration of Sentinel-1 and Sentinel-2 data would be another solution, since incorporating Sentinel-1 and Sentinel-2 data would increase the predictor variable diversity, and thus, more likelihood can be included in the modeling, and the model could yield more accurate results [39; 73].