esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Literature Review
### Defining the Prediction Task  
In our study, we predict player performance for the upcoming season using key metrics such as \(xG\) and \(aGg\), with these predictions based on historical data from the 2014/2015 to 2021/2022 seasons. Moreover, the input variables reflect each player's past performance metrics, capturing their historical contributions across multiple seasons, while the target variables are designed to project future performance outcomes. This approach clarifies that the model's input features are derived from previous seasons, whereas the target variables represent the expected results in the forthcoming season. This feature structuring ensures that the model is trained to accurately forecast future performance based on patterns observed in historical data. While predicting performance for an entire career or all past seasons is less practical due to variability in player form, focusing on next-season predictions offers actionable insights. To ensure realistic decision-making conditions, our experiment design simulates real-world scenarios by using only the data available before the predicted season. We also analyze player efficiency by comparing the difference between \(xGg\) (chances created per game) and \(aGg\) (goals scored per game), which helps assess how effectively players convert chances. We categorize players as either efficient scorers or consistent performers based on their ability to capitalize on opportunities or  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Team** & \(aG\) & \(xG\) & **Difference** \\ \hline Arsenal & 88 & 73.33 & 14.67 \\ Manchester City & 94 & 80.47 & 13.53 \\ Spurs & 70 & 57.81 & 12.19 \\ Chelsea & 38 & 50.08 & \(-\)12.08 \\ Everton & 34 & 45.78 & \(-\)11.78 \\ Man Utd & 58 & 68.74 & \(-\)10.74 \\ West Ham & 42 & 50.77 & \(-\)8.77 \\ Fulham & 55 & 47.03 & 7.97 \\ Wolves & 31 & 37.84 & \(-\)6.84 \\ Newcastle & 68 & 73.48 & \(-\)5.48 \\ Brighton & 72 & 74.75 & \(-\)2.75 \\ Southampton & 36 & 38.41 & \(-\)2.41 \\ Bournemouth & 37 & 39.46 & \(-\)2.46 \\ Nottingham Forest & 38 & 39.78 & \(-\)1.78 \\ Liverpool & 75 & 73.74 & 1.26 \\ Leeds & 48 & 48.57 & \(-\)0.57 \\ Leicester & 51 & 51.32 & \(-\)0.32 \\ Brentford & 58 & 57.89 & 0.11 \\ Crystal Palace & 40 & 39.95 & 0.05 \\ Aston Villa & 51 & 50.99 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clubs ranked by the difference between \(aG\) and \(xG\) in the Premier League for the 2022/2023 season.  
maintain steady performance levels. These predictions offer practical value for coaches and teams. For example, when preparing for a match against a strong opponent where scoring chances might be scarce, coaches can rely on efficient scorers who excel at converting lower xG opportunities into goals. This strategic selection helps ensure the team is better prepared for challenging matches. Additionally, these predictions assist teams in evaluating whether a player's future contributions align with their tactical and strategic goals, guiding decisions on recruitment, transfers, and contract renewals.
# Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations
## 3 Results
### Denoising Performance under a Fixed Noise Level  
At the typical noise level of 0.5 (50% pixels are contaminated by salt and pepper noise), both PANO-L\({}_{1}\) and TV-L\({}_{0}\) remove noise much better than the classic AMF. However, the two state-of-the-art approaches still lose some straight lines (Figure 4d,e) or weaken some textures (Figure 5d,e). The proposed method reconstructs these image features much better than others. As listed in Table 1, evaluation criteria including both PSNR and MSSIM indicate that the proposed method reconstructs the most consistent images to the noise-free ones.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multirow{2}{*}{**Quantitative Measure**} & \multicolumn{4}{c}{**Methods**} \\ \cline{3-6}  & & **AMF** & **PANO-L\({}_{1}\)** & **TV-L\({}_{0}\)** & **FDLCP-L\({}_{0}\)** \\ \hline \multirow{2}{*}{_Boat_} & PSNR & 36.32 & 37.95 & 38.29 & **40.26** \\  & MSSIM & 0.8715 & 0.9375 & 0.9307 & **0.9631** \\ \hline \multirow{2}{*}{_Lena_} & PSNR & 37.42 & 38.87 & 39.48 & **41.01** \\  & MSSIM & 0.8878 & 0.9360 & 0.9382 & **0.9531** \\ \hline \multirow{2}{*}{_Barbara_} & PSNR & 34.61 & 36.28 & 35.63 & **39.44** \\  & MSSIM & 0.8080 & 0.9293 & 0.8746 & **0.9644** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative measures at the salt and pepper impulse noise level of 0.5.
## 5 Distribution Results of SLR Stages  
The selection of studies for SLR was made in three stages, as shown in Figure 2. Applying the research keywords on five digital libraries in the first stage returned 15,543 results, where 2608 are from Scopus, 1141 from IEEE Xplore(r) digital library, 2577 from Science Direct, 6090 from Google Scholar, and 3127 from Springer. Filtering the results between January 2016 and mid-2021 and the English language option in the second stage reduces the returned results to 12,096 studies.  
However, approximately 5084 duplicate studies were discovered, reducing the total relevant studies to 7012. Later, in the third stage, filtering for the RPL research topic, a further 6461 studies were excluded, resulting in 551 studies. Meanwhile, from the selected studies, we provide a novel taxonomy of existing research literature on RPL, as shown in Figure 3. Such a taxonomy aids researchers by providing state-of-the-art research directions for the RPL protocol. Finally, we performed a full-text reading on RPL-based studies to extract the studies related to the scope of this study, resulting in a final count of 49 studies.
## 1 Introduction
[32] suggested slightly larger bias and less detectability of SM2 RAIN-ASCAT as compared to Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) in this specific region. This is probably due to the large uncertainty in the input satellite soil moisture product [33] and the fact that it is commonly influenced by radio frequency interference, which leads to a large sampling interval and therefore less effective retrievals [34]. This study intends to attribute the uncertainty in SM2 RAIN estimated precipitation with regard to the aforementioned issues with the satellite soil moisture product, namely, the retrieval accuracy and sampling interval, and to quantify their impacts by using in situ soil moisture observations. The well testified SM2 RAIN algorithm that was established by [PERSON] et al. [11] is adopted as the baseline algorithm. Meanwhile, the Naqu area in the central TP with extensive in situ soil moisture observations from the Central Tibetan Plateau Soil Moisture and Temperature Monitoring Network (CTP-SMTMN [35]) is selected as the study area. Soil moisture retrievals from the Soil Moisture Active Passive (SMAP) soil moisture product is adopted as the input to estimate precipitation. The SMAP satellite was launched by the National Aeronautics and Space Administration (NASA) on 31 January 2015, and is featured with relatively more reliable retrievals and less noise from radio frequency interference (RFI) [36; 37; 38; 39]. In general, the descending soil moisture retrievals (6:00 A.M. at local solar time) are commonly adopted by researchers due to their relatively higher quality than the ascending (6:00 P.M. at local solar time) data because of more favorable thermal equilibrium conditions between vegetation and near surface soil [40; 41]. Based on the above, the impact of retrieval accuracy and sampling interval on SM2 RAIN estimates is derived by using two different soil moisture datasets (i.e., SMAP and in situ soil moisture observations) with the same sampling intervals as input, and the same soil moisture datasets (i.e., in situ soil moisture observations) with different sampling intervals as input, respectively. To address different impacts from these two orbits, both the ascending and descending orbits are evaluated. It should be noted that the major focus of this study is to propose a way of quantifying uncertainties with regard to satellite soil moisture input during the SM2 RAIN implementation. The improvement of the current SM2 RAIN algorithm and the production of the regional or global precipitation product is beyond the scope of the current study and will be addressed in future studies. The paper is organized as follows: Section 2 describes the study area, datasets, and methods, Section 3 the research results, followed by conclusions and discussions in Section 4.
## 3 Analyses of Environmental Parameters of Different Modular Wall Greenhouses
### Temperature Analysis inside the Greenhouses during the Test Period
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Module Type**} & \multicolumn{2}{c}{**Inlet Air**} & \multicolumn{2}{c}{**Outlet Air**} \\ \cline{2-5}  & **Temperature\({}^{\prime}\)\({}^{\circ}\)C** & **Relative Humidity/\%** & **Temperature\({}^{\prime}\)\({}^{\circ}\)C** & **Relative Humidity/\%** \\ \hline SG & 33.18 \(\pm\) 0.80 & 38.17 \(\pm\) 10.44 & 24.94 \(\pm\) 0.20 & 47.63 \(\pm\) 1.24 \\ PG & 37.14 \(\pm\) 1.57 * & 26.61 \(\pm\) 6.36 & 29.07 \(\pm\) 0.81 ** & 43.22 \(\pm\) 8.95 \\ \hline \hline \end{tabular} Note: Different * in the same column mean a significant difference between treatments (_p_ < 0.05, where 0.001 \(p\) < 0.01 is **, 0.01\(p\) < 0.05 is *).  
\end{table} 
Table 4: SPSS significance test of the average air temperature and humidity of the air inlet and outlet on a typical sunny day. Figure 7: Temperature and humidity changes on typical cloudy days (24 February 2021 9:00–27 February 2021 9:00). (**a**) SG; (**b**) PG. The calculated results of heat transfer and active heat transfer ratio (the ratio of heat transfer of active heat storage system to total heat transfer) of the two greenhouses under different weathers by active and passive heat storage and heat release Equations (1) and (2) are shown in Table 1. This revealed the total heat transfer of SG and PG. In detail, heat storage was 464.87 and 264.16 MJ on sunny days, and 110.44 and 61.60 MJ was the heat release on sunny days. Meanwhile, 54.82 and 46.89 MJ was the heat release on cloudy days, respectively. In terms of active heat transfer, SG was higher than PG on sunny days and SG was lower than PG on cloudy days. The active heat transfer ratio of the two greenhouses was higher on cloudy days. Because the external weather in the two greenhouses caused the back wall of the greenhouse to absorb little sunlight. That is, the back wall of the greenhouse rarely absorbed solar radiation and then stored heat. It needed to exert heat for a long time to maintain the temperature inside the greenhouse. In summary, heat storage of both greenhouses was higher than the heat release for the whole day, which reflected the good thermal performance of the two helioregnhouse. Moreover, the heat storage performance of SG was slightly better than that of PG.
# An All-Digital Optical Phase-Locked Loop Suitable for Satellite Downlinks +
## 3 System Implementation and Analysis
### Results
The ability of the \(atan2\) discriminator to track the signal despite longer feedback loop delays and fading is one of its most notable characteristics. Hence, the \(atan2\) provides an alternative to digital OPLL due to its performance in the presence of noise and its tracking capability. In both SRs, the \(atan2\) and \(sine\) discriminators were also compared in other wind speed values and turbulence scenarios. While the proposed discriminator maintains the system locked, the sinusoidal discriminator always loses the lock during the fading regime. The sinusoidal discriminator only maintained locking when the system delay was equal to zero, which was an unrealistic condition  
The effect of wind speed on system performance was also investigated in both SRs due to the proposed phase detector's tracking capability under fading regimes. Thus, the BER fluctuation according to wind speed in three different turbulent conditions (weak, moderate, and strong) is shown in Figure 12. The OPLL at an SR of 625 MSA/s shows a higher degradation compared to the OPLL at 10 GSa/s, as it exhibits a higher power penalty, as previously discussed. In Figure 12, each point represents the average BER calculated for thirty different events. Therefore, by simulating only thirty events, it was evaluated that the results were representative. As a result, the error caused by the number  
Figure 11: SNR level variations for wind speeds of 5 m/s and 60 m/s under strong turbulence. Each point represents a turbulence event totaling thirty different values or thirty iterations. of simulated events may be negligible, where it was chosen as a compromise between results and simulation time. Regarding the wind speed impact on the optical signal, the turbulence intensity contributes to data degradation for wind speeds lower than 20 m/s. However, for \(v_{rms}\) higher than that, it influences the degradation of the transmitted data more than the turbulence's intensity. Hence, when the wind speed is 60 m/s, as shown in Figure 12, the BER is influenced mainly by the wind speed for all the turbulence scenarios, thereby degrading the transmitted data.
## 4 Conclusions  
We present a method for filtering noisy labels based on the classifier's confidence and an active learning approach based on pseudo-labels to use publicly-available unlabeled data. Our noisy label filtration method enhanced the CNN's performance of classifying referable DR, outperforming existing methods in the presence of noisy data. Furthermore, the model trained in the presence of label noise using our filtration method performed comparably to when no synthetic noise was injected under various settings. By utilizing a newly designed active learning scheme inspired by pseudo-labeling to efficiently discover rare positive cases of PM, the model was able to efficiently discover unfamiliar patterns present in the original small training set. After enlarging the original training set with publicly available data and labeling those discovered using our algorithm, the model was re-trained on the newly constructed dataset to attain an AUROC of 0.9993 on the PALM competition, ranking first on the off-site validation set.  
Our approach is applicable to practical clinical settings, where the development of deep learning systems usually demand a large volume of labeled data. Retrieving a large volume of labeled data may result in inaccurate annotations; efficient filtration and correction of suspicious cases help improve the performance of CNNs. Moreover, unprocessed medical data available on the web or electronic archives in hospitals can be collected and annotated efficiently when negative cases substantially outnumber positive cases. Finally, our experiments validate that adding a few positive cases can improve a CNN's classification performance considerably. Future work includes validating the robustness of our algorithms on other data modalities, perhaps in problems with continuous labels, under real-world environments.  
Conceptualization, J.S., J.K. and S.T.K.,Formal Analysis, J.S. and J.K.; Methodology, J.S., J.K., S.T.K. and K.-H.J.; Writing--Original draft, J.S., J.K. and S.T.K.; Writing--Review & Editing, J.S. and K.-H.J.; Supervision, K.-H.J. All authors have read and agreed to the published version of the manuscript.  
No funding provided for the study.  
No IRB required due to the nature of the study.  
No informed consent needed due to the nature of the study.  
All datasets used in the paper are publicly available and source codes are open to reproduce.  
J.S., S.T.K.: Employee of VUNO Inc., J.K.: No conflicts of interest, K.-H.J.: Shareholder of VUNO Inc.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & Convolutional Neural Network \\ DR & Diabetic Retinopathy \\ PM & Pathologic Myopia \\ ROC & Receiver Operating Characteristics \\ AUROC & Area Under the Receiver Operating Characteristics curve \\ \end{tabular}
# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
###### Abstract  
Solar energy is one of the most strategic energy sources for the world's economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous and risky tasks. Recently, the inspection of photovoltaic plants has been conducted with the use of unmanned aerial vehicles (UAV). Although the inspection with UAVs can be completed with a drone operator, where the UAV flight path is purely manual or utilizes a previously generated flight path through a ground control station (GCS). However, the path generated in the GCS has many restrictions that the operator must supply. Due to these restrictions, we present a novel way to develop a flight path automatically with coverage path planning (CPP) methods. Using a DL server to segment the region of interest (RoI) within each of the predefined PV plant images, three CPP methods were also considered and their performances were assessed with metrics. The UAV energy consumption performance in each of the CPP methods was assessed using two different UAVs and standard metrics. Six experiments were performed by varying the CPP width, and the consumption metrics were recorded in each experiment. According to the results, the most effective and efficient methods are the exact cellular decomposition boustrophedon and grid-based wavefront coverage, depending on the CPP width and the area of the PV plant. Finally, a relationship was established between the size of the photovoltaic plant area and the best UAV to perform the inspection with the appropriate CPP width. This could be an important result for low-cost inspection with UAVs, without high-resolution cameras on the UAV board, and in small plants.  
deep learning (DL); unmanned aerial vehicle (UAV); photovoltaic (PV) plants; semantic segmentation; coverage path planning (CPP) +
Footnote †: journal: Applied Sciences  
16 November 2021  
Academic Editors:  
[PERSON],  
[PERSON] and [PERSON]  
Received: 16 November 2021  
Accepted: 15 December 2021  
Published: 19 December 2021
# Synchronous Grouting Analysis of Shield Tunneling through High Water Pressure Fault Fracture Zone
## 3 Analysis of Synchronous Grouting Results in Fault Fracture Zones
### Analysis of Settlement and Deformation of Surrounding Rock  
The displacement field during excavation is shown in Figure 11. And the displacement field after excavation completion is shown in Figure 12.  
Figure 11: Displacement field during excavation.  
Figure 10: Schematic diagram of longitudinal section monitoring points of the model.  
As shown in Figures 13 and 14, the displacement curves of the observation points at 0.8 m above and below the tunnel as the shield tunneling progresses. As the shield tunneling machine advances, the monitoring points above the tunnel slowly move upwards. When the shield tail synchronous grouting passes through the monitoring points, the monitoring points reach their maximum upward movement. After the shield tunneling machine cutterhead section passes, the monitoring points begin to settle downwards. When synchronous grouting is carried out, the development of downward displacement slows down significantly. During the hardening process, the displacement of the monitoring points rapidly develops downwards and eventually stabilizes. For the monitoring points at the bottom of the tunnel, before the shield machine passes through, the mud pressure on the face of the tunnel has almost no effect on the displacement of the monitoring points. Until the shield machine cutterhead section passes through the monitoring points, the displacement of the monitoring points gradually rises upwards, and finally reaches stability. Compared with the monitoring points of rock and soil before and after the fault fracture zone, the displacement of the monitoring points inside the fracture zone is significant, and the deformation is significantly higher than that of the crack zone and ordinary normal surrounding rock monitoring points. The maximum displacement deformation reaches 2.2 mm.  
Figure 12: Displacement field after excavation completion.  
Figure 13: Top monitoring point changes with settlement during excavation process.
# Transfer Learning for Operator Selection: A Reinforcement Learning Approach
## 4 Experimental Results
### Results and Discussions
It is important to note that RLABC-T stops learning from new problem-solving runs, while RLABC-TC keeps updating the relevant centeroids of the corresponding clusters with upcoming new cases learned. The past experience jointly with undergoing learning remains beneficial in solving the problems faster without compromising the solution quality. The scope of this study has been kept to solve SUKP as an abstract combinatorial optimisation problem, which can be implemented into many real-world problems such as different variants of scheduling and timetabling problems. It is obvious that once a real-world problem can be solved with this approach as demonstrated, profit-loss analysis can be conducted to reveal the economical benefits. The outperforming algorithm among three is RLABC-TC, which transfers previously gained experience into new runs and keeps learning active. It has been compared with some recently published very competitive state-of-art works--labelled as GA [28], BABC [32], binDE [33], and GPSO [31]--and the comparative results are plotted in Figure 7 on which all benchmark instances of the three sets have been considered, as shown on the horizontal axis of the graph, while the quality of the solution is indicated on the vertical axis. As a maximisation problem, the highest mean value is always delivered by RLABC-TC, which demonstrates the strength of the proposed approach. Figure 6: The change of credit levels gained by each operator while solving some benchmark instances through iterations; (a) credits gained with the RLABC algorithm, (b) credits gained with the RLABC-T algorithm, and (c) credits gained with the RLABC-TC algorithm.