esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this survey, we present possible research directions for the future. Inspired by state-of-the-art developments in 5G communication technology and DL, we provide an overview of the most recent work on utilizing DL for wireless physical layers. Furthermore, we outline many cutting-edge and successful DL-based communication frameworks. This review delivers high-quality services by investigating DL for the wireless physical layer in light of the aforementioned factors. Our research focuses on B5G systems to accommodate new needs in future communication situations. Through the trade-off between EE and SE, this article ensures high data rates for users and allows HetNets to optimize UE, spectrum allocation, and a large number of antennas by taking advantage of the backhaul capacity limitation to maximize both EE and SE. The suggested AB-HP approach aims to reduce channel-estimate overhead and RF chains by reducing the beam training overhead. Using the maximal power technique consistently results in inferior performance for the proposed maximum RBT-DRL. Based on a Pareto-optimal solution to the optimization problem, this shows that maximizing beamforming and PA simultaneously can increase  
Figure 13: SE versus time step for memory and processing optimization.  
the spectral efficiency of mm-wave systems. Future work will focus on addressing latency constraints, ensuring fair user allocation, and improving compatibility and computational complexity with diverse network architectures to further optimize performance in dynamic and heterogeneous environments.  
Conceptualization, A.S. and N.S.M.S.; methodology, A.S. and M.A.A.; software, A.S., M.A.A. and G.A.H.; validation, C.J.J. and M.A.A.; formal analysis, A.S. and A.H.; investigation, C.J.J.; resources, A.S. and C.J.J.; writing--review and editing, S.A. ([PERSON]) and R.A.; visualization, S.A. ([PERSON]); supervision, A.H. and F.S.A. updated the comments. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to Universiti Tunku Abdul Rahman Research Fund (UTARRF) by supporting the Fund through Vote no. (6557/2A02) and TIERI grant vot Q44 from Universiti Tun Hussein Onn Malaysia.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
The authors declare no conflicts of interest.
## 3 Results
### Influence of the EL on the Reinforcement Performance of FRP Rock Bolts  
When the EM of rock bolts was set as 50 GPa, the corresponding reinforcement performance of FRP rock bolts is shown in Figure 9. Apparently, the reinforcement performance of FRP rock bolts was much more diverse than metal rock bolts. For FRP rock bolts, even when the EL was short as 0.5 m, there was a non-linear relation between the anchorage force of rock bolts and the PO displacement. This non-linear relation was more apparent when the EL was long.  
To investigate how the EL influenced the largest anchorage force of FRP rock bolts, the largest anchorage force of FRP rock bolts was extracted and plotted. It should be noted that when the EL was 3 m, the anchorage system failed by tensile rupture of FRP rock bolts. In this case, the failure mode of the anchorage system was diverse from the others. Therefore, when the EL was smaller than 3 m, the largest anchorage force of FRP rock bolts was plotted together, as shown in Figure 10.  
Figure 8: Debonding behaviour of the surface between rock bolts and grout: (**a**) EL = 1.5 m; (**b**) EL = 2.5 m.  
Figure 7: Numerical PO tests to confirm the critical EL.  
Apparently, for FRP rock bolts, there was a strong linear relation between the largest anchorage force and EL. The correlation coefficient was 0.9997. Moreover, the corresponding linear regression equation was shown below:  
\[y=66.27x+18.19 \tag{3}\]  
Relying on this equation, the critical EL of FRP rock bolts can be predicted. For FRP rock bolts, the tensile rupture force was still 220 kN. Therefore, after substituting this tensile rupture force into Equation (3), the corresponding critical EL can be obtained. It showed that under this scenario, for FRP rock bolts, the predicted critical EL was 3.03 m.  
Further numerical simulation was conducted to confirm the exact critical EL. To realise this, the EL was increased gradually from 2.5 m. When the failure mode of the anchorage system changed from bonding failure at the surface between rock bolts and grout to the tensile rupture of the FRP rock bolts, the corresponding EL was regarded as the critical EL. It was coincident that when the EL increased to 3 m, tensile rupture of FRP rock bolts  
Figure 10: Relation between the largest anchorage force of FRP rock bolts and the EL.  
Figure 9: Reinforcement performance of FRP rock bolts when the EL was diverse.  
occurred. Therefore, in this scenario, for FRP rock bolts, the critical EL was 3 m. Compared with the predicted critical EL of 3.03 m, there was a quite small difference between them. This further confirmed that using the linear regression analysis method to predict the critical EL of rock bolts was effective.
## 5 Algorithm Application and Results
### Results and Discussion
It is given by: \[p=\frac{tp}{tp+fp};\] (16)
* The **recall score (r)*
* is the ratio of correctly predicted positive observations in a class, thus from an intuitive perspective, it is the capacity of the algorithm to determine all positive samples. Its value is calculated as: \[r=\frac{tp}{tp+fn};\] (17)
* The **F1-score (F1)*
* is the weighted average of the recall and precision and is commonly more important when there are large imbalances of the data in the class distributions. It is given by: \[F1=\frac{2\times precision\times recall}{precision+recall}.\] (18)  
In the case of the tested algorithms, the lowest values can be seen for the CNN, while the highest ones were observed for the FNN and GRU models. Even with these differences, the overall results showed that all algorithms could be used to determine the classes presented in this work. It is worth noting that, when evaluating an SLJ, it is more important that the algorithm does not give false negatives than it generates false positives. This remark makes the recall score more important than the other indices. Indeed, the recall score is directly dependent on the false negatives, while the other criteria use false negatives more indirectly. Using this as a basis, it is possible to see in Table 2 that both LSTM and the CNN presented lower recall values than the FNN and GRU. This makes  
Figure 20: Comparison between the loss and accuracy for the LSTM algorithm in both training and validation cycles. both LSTM and the CNN less suitable for the problem at hand and both the FNN and GRU the most-promising ones. It is important to understand that the accuracy, precision, recall, and F1-scores of Expressions (15)-(18) are relevant, but other evaluation criteria need also to be analyzed, such as the number of epochs to reach convergence and the total time taken to achieve the solutions. In Table 3, it is possible to compare the results, which give a more accurate insight into each algorithm. One surprising value is that, although the GRU took longer to calculate for each epoch compared to the CNN and FNN, the total epochs required were fewer, which allowed for the total time to be minimized. It is also possible to see that the GRU and LSTM would largely benefit from a feature extraction protocol, which could reduce the number of features to a few dozens, as large datasets tend to slow calculations and the training process. When considering the results summarized in Tables 2 and 3, it is possible to see that, out of all the algorithms, the CNN presented good results, as it was only 2% off the best accuracy and could accomplish this in almost half the time of the next fastest algorithm. At the other end of the spectrum, LSTM took quite a long time to reach the solution and presented the second-lowest value for the accuracy. Finally, the FNN and GRU presented the best accuracy and had relatively acceptable convergence times, making them overall the most-suitable choices.
GPP is generally estimated using models because it is difficult to measure directly [36]. Satellite-based, light-use efficiency (LUE) models have been widely used because they rely on simple algorithms to estimate GPP [37]. The LUE model states that carbon exchange is a function of the amount of light energy absorbed by vegetation and the efficiency with which that light energy is used to fix carbon [38]. One of the LUE models that is commonly used for estimated GPP is the vegetation production model (VPM) [39]. The VPM is a light-use efficiency model that utilizes remote sensing imagery to estimate GPP based on the impacts of temperature, water stress, and phenology [40]. GPP from Landsat data based on VPM estimates has been validated by several studies that indicate good agreement with CO\({}_{2}\) eddy flux towers [41; 42]. The potential of using the VPM model to scale up GPP estimation in Indonesia has also been evaluated for CO\({}_{2}\) flux tower sites that use MODIS data [43]. Based on these conditions, in this work, we attempt to use Landsat data and land use information from topographic maps to estimate vegetation carbon uptake by urban tourism areas and their changes over the past two decades due to land use conversion. Three types of Landsat data, including Landsat 5, Landsat 7 and Landsat 8, are used to calculate annual GPP for 1995, 2003 and 2014, respectively. Annual changes in GPP values estimated using VPM in Denpasar, an urban tourism city on Bali Island, Indonesia, were analysed and extracted for each land use type. It is expected that the results can provide information related to the impact of land use change on ecosystem services and particularly to the biological carbon uptake capability of urban vegetation.
# Durability Evaluation Study for Crumb Rubber-Asphalt Pavement
## Materials and Methods
### Experimental Methods Setup
#### 2.2.3 Moisture Stability Evaluation Method of Crumb Rubber-asphalt Mixture
* The tensile strength ratios \((TSR)\) of the crumb rubber-asphalt mixture, \(TSR_{1}\), and \(TSR_{2}\) are respectively calculated according to Equations (4) and (5) as follows: \[TSR_{1}=\frac{\overline{R}_{T2}}{\overline{R}_{T1}}\times 100\] (4) \[TSR_{2}=\frac{\overline{R}_{T3}}{\overline{R}_{T1}}\times 100\] (5) In the formulas: \(TSR_{1}\)--The indirect tensile strength ratio under freeze-thaw cycle (%); \(TSR_{2}\)--The indirect tensile strength ratio under freeze-thaw-boil cycle (%); \(\overline{R}_{T1}\)--Average value indirect tensile strength of the first set of effective specimens under normal conditions (MPa); \(\overline{R}_{T2}\)--Average value of indirect tensile strength of the second set of effective specimens after a freezing and thawing cycle (MPa); \(\overline{R}_{T3}\)--Average value of indirect tensile strength of the third set of effective specimens under long-term aged after a freeze-thaw-boil cycle (MPa). Long-term aging treatment of the third set of specimens is required to assess the long-term water stability of the mixture (refer to test method T 0734-2000 in \"Testing Procedures for Asphalt and Asphalt Mixtures for Highway Engineering\"; loose mix 135 \({}^{\circ}\)C/4 h, compacted test specimen 85 \({}^{\circ}\)C/5 d) [46]. After aging, the freeze-thaw-boiling-splitting test for the third set of test specimens as described above was carried out on this specimen to obtain the splitting strength ratio, which was recorded as \(TSR_{2}\). At present, the Chinese standard generally uses an unaged asphalt mixture for freeze-thaw split testing to evaluate water stability, while AASHTO tests the long-term aged mixture in America. For an ordinary asphalt mixture, the impact of the implementation of the Chinese standard is not significant, but considering the special mechanical characteristics and the complex working environment experienced by the crumb rubber-asphalt mixture, this study proposes that it is necessary to carry out the above-mentioned freeze-thaw boiling procedure on a long-term aged test specimen. (2) Cantabro Abrasion Test under water-immersion  
The Cantabro test is usually used for assessing the water damage resistance of the porous mixtures, of which the main distress is aggregate stripping. Thus, because the main distress of crumb rubber-asphalt pavement is also aggregate stripping, we here introduce it as an auxiliary evaluation method. A water immersion test was carried out by immersing the specimens in the water at 60 \({}^{\circ}\)C for 48 h. The main objective of the Cantabro abrasion test [46] under water immersion was to investigate the expansion of rubber crumbs in hot water. The pressure of the Marshall specimen in the test machine was used to simulate its stability after instantaneous extrusion and elastic recovery of the mixture under dynamic traffic load.
# Optimizing Non-Glare Zone Width of Adaptive Driving Beam (ADB) Using Fuzzy Logic Control
## 2 Modeling: Night Drive Simulator  
This research is conducted on LucidDrive, a night drive simulator. Compared to physical tests and experiments, not only is the cost of a virtual night drive much lower, but it is also not limited by testing vehicles, types of roads, or weather conditions. Additionally, due to the current legislation status of ADB in America, access to ADB vehicles is limited. As a result, a virtual night drive simulator was preferable in performing this study.  
Figure 1: Example of a curvy road where indirect glare from the side rearview mirror is experienced by the preceding driver. ©2021 Microsoft Corporation, ©2021 TomTom.  
The first step of the research is to reproduce the ADB system and the side rearview mirror glare problem in the night drive simulator. The simulator provided two approaches for simulating the ADB function within the Adaptive Forward-Lighting System (AFS) plug-in of the software: pixel masking and matrix beam [19]. The pixel-masking algorithm simulates an ideal high-definition ADB system allowing for precise control of the non-glare zone's shape. The pixel-masking algorithm creates a virtual mask in front of the lamp to shade out the non-glare zone according to the shape of the boundary box. In the other approach, the matrix beam algorithm simulates a more realistic ADB system, with each segment having an individual beam pattern file. If the boundary box is within a certain region, the corresponding segment will be turned off. The shape of the non-glare zone is limited by the numbers and the beam patterns of the LEDs. As the resolution of the matrix headlamp increases, the difference in performance between these two approaches will reduce, yet pixel masking is computationally cheaper, as it only needs one beam pattern file. The comparison is shown in Figure 2.  
For more accurate control of the width of the boundary box and a better illustration of the trade-off relationship between the indirect side rearview mirror and road illumination, the pixel-masking script is chosen to simulate ADB in this research. The beam pattern that is being used in this research is a combined eight-element segmental LED lamp.  
Figure 2: Comparison of pixel masking and matrix beam showing the difference of two simulation algorithms: (**a,c**) ADB simulation using matrix beam algorithm, in which each segment can be switched on/off individually; (**b,d**) ADB simulation using pixel-masking algorithm, which uses one beam pattern, with the boundary box of the stimulus vehicle masked out.
## 3 Experimental Test and Result Analysis
### Experimental Test
#### 3.4.2 Dataset  
The pattern analysis, statistical modeling, and computational learning visual object classes challenge (PASCAL VOC) represents a challenge in the field of international computer vision. This paper selects PASCAL VOC 2007. PASCAL VOC 2007, as one of the early datasets, holds an important historical position in the field of computer vision. PASCAL VOC 2007 covers over 12,000 labeled objects and is one of the commonly used standard test datasets for the YOLO algorithm. Due to its high-quality annotation information and various common target categories, it is more consistent and accurate in category definition and annotation, including more occlusions or more complex scene layouts, making it suitable for robot object detection and fully testing the performance of the model.
## 3 Optimization Model for Load Balancing in Streets
### Definition of Vehicular Congestion in the Optimization Model  
The vehicular congestion in a street \((i,j)\) is presented as the ratio between the use of the street at a state k, and the capacity of the street:  
\[\alpha_{(i,j)}(k)=\frac{U_{(i,j)}(k)}{C_{(i,j)}}=\frac{\sum_{v\in V}X_{v(i,j)d_{ v}}(k)}{C_{(i,j)}} \tag{1}\]
## Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1, [PERSON] 1,2, [PERSON] 1,3 and [PERSON] 4**  
1 State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China; E-Mails: [EMAIL_ADDRESS] (H.S.); [EMAIL_ADDRESS] (Z.H.); [EMAIL_ADDRESS] (J.Y.); [EMAIL_ADDRESS] (X.W.)  
2 Shanghai Institute of Radio Equipment, Shanghai 200090, China  
3 Technology Center, CNPC Logging Co., Xi'an 710077, Shaanxi, China  
4 Beijing Aerospace Control Center, Beijing 100094, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-29-8266-3689 (ext. 604); Fax: +86-29-8266-3689 (ext. 605).  
_Received: 27 November 2012; in revised form: 22 December 2012 / Accepted: 8 January 2013 / Published: 18 January 2013_  
**Abstract:** Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox.  
keywords:planetary gearbox; fault detection; vibration sensor signals; customized multiwavelets; redundant symmetric lifting schemes; improved neighboring coefficients pacs: 89.20.Kk; 89.20.Bb; 07.50.Qx
# A Background Subtraction Algorithm in Complex Environments Based on Category Entropy Analysis
## 4 Results and Discussion
### Selection of Performance Metrics  
Figure 5a shows an input image from a sequence. The detection results of the background subtraction method can be binarized into a cut image, whose background and foreground may be represented by black and white colors, respectively, as shown in Figure 5b. It is possible to evaluate the performance of the background subtraction method by comparing the cut image to the ground-truth cut images. According to the comparison results, the performance can be divided into four types: true positives (TP), meaning the correct detection of foreground pixels; false positives (FP), meaning the incorrect classification of background pixels as foreground pixels; true negatives (TN), meaning the correct classification of background pixels; and false negatives (FN), meaning the incorrect classification of foreground pixels as background pixels.  
The greatest difficulty in assessing the performance of background subtraction methods is the lack of standardized assessment criteria, which has prompted scholars to propose evaluation criteria that are only applicable to their algorithms. Therefore, according to [29], we proposed that the percentage of correct classification (PCC) is the most widely used metric for performance evaluation of a binary classifier. This metric contains four values, as shown in Equation (12). This metric was adopted in this study for the performance testing of the background subtraction method. It should be noted that a higher PCC value indicates a more accurate detection result. In order to more objectively evaluate the performance of the algorithm, we further adopted seven metrics such as Equations (13)-(19) [27].  
Figure 5: (a) Input image, (b) foreground.  
Finally, we used the average ranking \(R_{i}\) to combine the above eight metrics as performance evaluation criteria for different algorithms. The average ranking \(R_{i}\) for method \(i\) across all overall average metrics is given by Equation (20), where \(m^{\prime}\) is an overall-average metric such as the one in Equation (12) and \(rank_{i}(m^{\prime})\) denotes the rank of method \(i\) according to the overall-average metric \(m^{\prime}\).  
\[\text{PCC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}, \tag{12}\]  
\[\text{Recall }(\text{Re})=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{13}\]  
\[\text{Specificity }(\text{Sp})=\frac{\text{TN}}{\text{TN}+\text{FP}}, \tag{14}\]  
\[\text{False Positive Rate }(\text{FPR})=\frac{\text{FP}}{\text{FP}+\text{TN}}, \tag{15}\]  
\[\text{False Negative Rate }(\text{FNR})=\frac{\text{FN}}{\text{FN}+\text{TP}}, \tag{16}\]  
\[\text{Percentage of Wrong Classifications }(\text{PWC})=100\frac{\text{FN}+\text{FP}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}, \tag{17}\]  
\[\text{Precision }(\text{Pr})=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{18}\]  
\[\text{F}-\text{measure }=2\frac{\text{FN}}{\text{FN}+\text{TP}}, \tag{19}\]  
\[R_{i}=\sum_{m^{\prime}}rank_{i}(m^{\prime}). \tag{20}\]