esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Data
### Data and Experimental Environment  
#### 2.3.1 Dataset  
In this work, two datasets are used for the evaluation and verification of the improved model: namely, the Break-ID-1632 image dataset (Figure 5) and the cable damage dataset (Figure 6). The Break-ID-1632 image dataset is obtained by installing a high-definition camera on an intelligent patrol robot to capture real-time photos on high-voltage transmission lines. There are 1814 photos, with 1632 training photos and 182 test photos. The pixel size is 800 \(\times\) 600, and it includes nine categories: normal, damaged, one broken strand, two broken strands, three broken strands, four broken strands, five broken strands, six broken strands, and seven broken strands, covering a wide range of categories, including location, environment, object, etc. The uniqueness of this dataset lies in the following aspects: (1) The dataset contains a wealth of real-world images, covering grasslands, forests, and snowy environments, effectively simulating real-world target detection and tracking scenarios, and (2) the annotation information is exceptionally detailed, meticulously recording the location, size, and shape of the target, providing precise reference standards for algorithm research. It is also worth mentioning that the Break-ID-1632 dataset covers images under different weather and lighting conditions, as well as various angles and height perspectives, which helps the algorithm to be applied and evaluated in various complex backgrounds. The Break-ID-1632 image dataset is a self-made dataset that can be widely used in fields such as target detection, tracking, and remote sensing. It can be used to verify and optimize algorithms, and it can also be compared with other algorithms. Meanwhile, these data provide important reference resources for high-voltage power transmission line inspection. Other datasets include the cable damage dataset, which has 1318 photos, along with training sets and test sets consisting of 1187 and 131 photos, respectively, including burned and
## 4 Results and Discussion  
### Power, Slope Efficiency, and Optical Spectrum  
In our experiments, we recorded the power after CPS and the output power of the low-noise fiber amplifiers with an optical power meter (Ophir, Starlite, Jerusalem, Israel). As shown in Figure 3, the power increases linearly with the pump current. The amplifier with 100 mW seed can deliver an output power up to 5 W when the pump current is 8 A, and the corresponding slope efficiency is 46.1%. However, the output power and slope efficiency are less than the results after CPS. We consider that this difference was caused by the insert loss of the optical devise and fiber fusion loss in the fiber amplifier. The total loss after CPS is approximately 1.5 dB. Compared to the laser in [18], our fiber amplifier is comparable in terms of output power and slope efficiency.  
The signal-to-noise ratio (SNR) of the seed and amplified laser was detected by an optical spectrum analyzer (OSA) covering the spectral range from 950 to 1500 nm (YOKGO-GAWA, AQ6370D, Tokyo, Japan). The spectrum was obtained at 2.5 W output of the amplifier operation with a gain of 14 dB. Figure 4 shows the measured results. There is no ASE at approximately 1030 nm. The SNR is higher than 75 dB, and negligible degradation is observed.  
Figure 3: Output power and slope efficiency measured versus pump current of the pump diode.
# Tribological Behavior of Biomass Fast Pyrolysis Fuel and Diesel Blends
## 3 Results and Discussion  
### Preparation of Emulsion Biomass Fuel Blends  
An amount of 5 wt.% emulsified biomass fuel, or biomass fuel/diesel emulsified blend, was prepared based on our previous method [14] by taking 5 wt.% of biomass oil, 89.5 wt.% of 0\({}^{\#}\) diesel, and 5.5 wt.% of surfactant (composed of 93.1 wt.% of lipophilic surfactant and 6.9 wt.% of hydrophilic surfactant). The detailed preparation process was to add lipophilic surfactant to 0\({}^{\#}\) diesel, respectively, hydrophilic surfactant to biomass fuel, and then use a high shear emulsifying machine to mix at 30 \({}^{\circ}\)C with 1500 r/min. By shearing and stirring the mixture of the above two components for 20 min, a stable emulsified biomass fuel was prepared, and its stability time could be up to over 60 days. The appearances of the fuels are shown in Figure 2. According to the above methods and process conditions, 10 wt.% and 20 wt.% biomass oil were taken, respectively, and appropriate additives were selected to prepare emulsified biomass fuel with different biomass fuel contents. Their optimum process conditions are shown in Table 1.  
[PERSON] et al. [15] reported that the emulsified biomass fuel has good stability when the mass fraction of biomass fuel was not more than 20% and the stability time was more than 5 days. When the mass fraction was reduced to 10%, the stability time was more than 17 days, and when the mass fraction was reduced to 5%, the stability time would be more than 40 days. If the mass fraction of biomass fuel reached 30%, the stability time did not exceed 3 h, which might be due to one or several unstable factors in biomass fuel affecting the stability of emulsified biomass fuel. In addition, when the mass fraction of biomass fuel was greater than 20%, the viscosity of emulsified fuel would exceed the upper limit of vehicle diesel requirements (8 mm\({}^{2}\)/s, 20 \({}^{\circ}\)C). In summary, when the emulsified biomass fuel was applied to the engine, the mass fraction of added biomass fuel should not exceed 20%. Therefore, this study investigated the physicochemical and tribological performances of emulsified biomass fuel with biomass fuel content less than 20 wt.%. As shown in Figure 2, the appearance color of emulsified biomass fuel becomes darker changed from red-brown to dark brown with the increase in biomass fuel mass fraction.
## 3 Discrete Event Simulation Model
### Equipment Modeling
#### 3.1.1 Maintenance costs  
Different types of costs have to be considered: costs due to condition monitoring (CMT); preventive maintenance (PM); corrective maintenance (CM); consequence of idling, minor stops and failure or breakdowns; and costs of upgrading the components or substituting them (\(C_{u}\)). Each of them is defined next.  
The cost associated to condition monitoring (\(C_{cnt}\)) is given by:  
\[C_{cnt}=L\cdot C_{bct}. \tag{4}\]  
Figure 2: Age vs. chronological time in a PAR model under a CM maintenance strategy [12].  
\(L\) represents the analysis period and \(C_{hct}\) is the hourly cost of monitoring. Preventive maintenance costs are calculated following:  
\[C_{pm}=n_{c}\cdot e\cdot d_{pm}\cdot c_{hpm}, \tag{5}\]  
where \(d_{pm}\) and \(c_{hpm}\) represent, respectively, the mean time and the average hourly cost of performing PM, and \(\mathbf{x}\) is the vector of decision variables. It is worth noting that this cost will be incurred in every PM action in this model. In addition, some preventive actions will require adding the cost of upgrading the component.  
The cost contribution due to corrective maintenance is given by:  
\[C_{cm}(\mathbf{x})=u_{cm}^{*}(\mathbf{x})\cdot c_{hcm}\cdot L, \tag{6}\]  
where \(c_{hcm}\) is the average hourly cost of performing corrective maintenance, and \(u_{cm}^{*}(\mathbf{x})\) is the averaged unavailability of the analysis period \(L\). The calculation of \(u_{cm}^{*}(\mathbf{x})\) requires knowing \(u_{r}^{*}(\mathbf{x})\), the time-dependent unreliability for discontinuous equipment, which takes the expression of \(u_{rm}^{*}(\mathbf{x})\) each period between PM activities:  
\[u_{rm}^{*}(\mathbf{x})=\rho+(1-\rho)\cdot(1-\exp{(-h_{m}^{*}\cdot M_{m})})\,. \tag{7}\]  
Thus, it is possible to obtain \(u_{cm}^{*}(\mathbf{x})\):  
\[u_{cm}^{*}(\mathbf{x})=\frac{\sum_{m=1}^{e}u_{rm}^{*}(\mathbf{x})\cdot d_{cm }}{L_{c}}. \tag{8}\]  
Finally, we define the costs of upgrading the component (\(C_{u}\)) when the amount of maintenance activities prior to change is exceeded. This term is calculated by multiplying the number of components that have been changed (\(n_{c}\)) with the cost of upgrading or substituting one component (\(C_{c}\)):  
\[C_{u}=C_{c}\cdot n_{c}. \tag{9}\]
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 2 Related Works
### Transformer Networks  
Transformer [22] was first applied in Natural Language Processing [43; 44; 45] in the place of RNNs [46; 47] to model sequential data, such as text generation [48], machine translation [49], etc. The core idea of the transformer consists of a multi-head self-attention method that computes the query, key, and value with the input embeddings. Compared with RNNs using iterative manners, transformers take advantage of parallel computation and capture long-range dependency. Due to the success in NLP, transformers have also been applied to other fields such as stock prediction [50], robot decision making [51], computer vision [52; 53], etc. Studies [27; 54] constructed spatial and temporal transformers for pedestrian trajectory predictions and achieved remarkable results compared with traditional sequence models. Some works [17; 55; 56] designed transformer-based modules for pedestrian and vehicle trajectory predictions and received encouraging results. However, the self-attention computed in these methods is dense and introduces superfluous interaction. Consequently, we design a novel sparse transformer that only pays attention to the agents who need to be focused on.
## 2 Theoretical Basis  
### Equivalent Strain-Softening Model for Jointed Rock Mass  
1.1 Quantifying the Surrounding Rock Grading System Using the GSI (Geological Strength Index) Series  
Engineering practice shows that the post-peak behavior of rock mass is highly sensitive to variations in rock mass quality grade and confining pressure. The selection of a reasonable surrounding rock rating method is, therefore, a key factor to ensuring the safety and reliability of a support structure in underground engineering. The geological strength index (GSI) is a surrounding rock rating system that describes broken rock mass of poor quality, and has been widely used and improved in a large number of engineering practices. [PERSON] et al. [19; 20] proposed a rock mass grading method that takes the block volume \(V_{b}\) and structural plane condition factor \(J_{c}\) as decisive factors to obtain a GSI value for a rock mass using a GSI quantification chart. The volume \(V_{b}\) of a rock mass can be calculated by the joint spacing and joint plane angle. Rock mass joints are shown in Figure 1, where \(S_{i}\) and \(\gamma_{i}\) represent the distance and angle between the joint spacing and joint plane, respectively.  
The rock block volume \(V_{b}\) can then be calculated according to:  
\[V_{b}=\frac{S_{1}S_{2}S_{3}}{\sin\gamma_{1}\sin\gamma_{2}\sin\gamma_{3}} \tag{1}\]  
The condition factor \(J_{c}\) of the rock mass structural plane can be determined as:  
\[J_{C}=\frac{I_{W}J_{S}}{J_{A}} \tag{2}\]  
where \(J_{W}\) is a large-scale waveform coefficient that ranges from 1 to 10 m, \(J_{S}\) is a small-scale smoothness coefficient that ranges from 1 to 20 cm, and \(J_{A}\) is the joint alteration coefficient. In numerical simulations, the GSI value can sometimes be very hard to determine in  
Figure 1: Joint spacing and joint angle of rock mass.  
reference to the chart, which tends to be somewhat subjective and inherently introduces errors. To improve the system accuracy and convenience, [PERSON] et al. [21] established a formula for calculating the GSI based on \(V_{b}\) and \(J_{c}\) using surface fitting technology according to:  
\[GSI(V_{b},J_{c})=\frac{26.5+8.79\ln J_{c}+0.9\ln V_{b}}{1+0.015\ln J_{c}-0.0253 \ln V_{b}} \tag{3}\]  
A visual image method is used to describe the relationship between the GSI and \(V_{b}\) and \(J_{c}\), which improves the practicality and accuracy of the system. A visual image of the GSI system is shown in Figure 2.  
To further improve and perfect the quantitative method of the GSI value, [PERSON] et al. [20] proposed that \(V_{b}\) and \(J_{c}\) are not constant values and that their magnitudes gradually decrease during the local plastic shear failure process upon rock mass unloading. Equation (3) shows that the GSI magnitude of a rock mass will gradually deteriorate from the peak \(GSI^{p}\) to a post-peak residual \(GSI^{r}\). [PERSON] et al. [22] discussed the relationship between \(GSI^{p}\) and \(GSI^{r}\) and fitted an empirical relationship as follows:  
\[GSI^{r}=17.25\times e^{0.0107\times GSI^{p}} \tag{4}\]
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
###### Abstract  
Vehicular nodes are equipped with more and more sensing units, and a large amount of sensing data is generated. Recently, more and more research considers cooperative urban sensing as the heart of intelligent and green city traffic management. The key components of the platform will be a combination of a pervasive vehicular sensing system, as well as a central control and analysis system, where data-gathering is a fundamental component. However, the data-gathering and monitoring are also challenging issues in vehicular sensor networks because of the large amount of data and the dynamic nature of the network. In this paper, we propose an efficient continuous event-monitoring and data-gathering framework based on fog nodes in vehicular sensor networks. A fog-based two-level threshold strategy is adopted to suppress unnecessary data upload and transmissions. In the monitoring phase, nodes sense the environment in low cost sensing mode and generate sensed data. When the probability of the event is high and exceeds some threshold, nodes transfer to the event-checking phase, and some nodes would be selected to transfer to the deep sensing mode to generate more accurate data of the environment. Furthermore, it adaptively adjusts the threshold to upload a suitable amount of data for decision making, while at the same time suppressing unnecessary message transmissions. Simulation results showed that the proposed scheme could reduce more than 84 percent of the data transmissions compared with other existing algorithms, while it detects the events and gathers the event data.  
e 2017 10 October 2017; Accepted: 26 December 2017; Published: 29 December 2017
## 3 Detailed Analysis of Maximum Power Point Tracking (MPPT) Methods
### RL (Reinforcement Learning)-Based MPPT Method  
Reinforcement learning (RL) is a machine learning method in which an agent (controller) learns from its interactions with the environment. RL-based MPPT algorithms are used to discover and maintain the MPP of PV systems. This method continuously optimizes itself based on environmental conditions.  
* Rapid adaptation to environmental changes.
* Continuous learning capability.  
* A slow learning process at the beginning.
* The reward function must be carefully designed.
# Graphene-Based Grid Patterns Fabricated via Direct Ink Writing for Flexible Transparent Electrodes
## 3 Results and Discussions
### Fabrication and Performance Evaluation of Grid-Patterned Graphene Electrodes
For practical application of GFTEs, the stability under different service environments such as high-temperature environments and acid-base corrosive environments needs to be taken into consideration. The thermal stability of our GFTEs was tested by placing **G-4 \(\times\) 4-15** in different high-temperature environments and measuring the change in its sheet resistance at regular time intervals, and the results are shown in Figure 7a. The increases in the sheet resistance of **G-4 \(\times\) 4-15** at 80 \({}^{\circ}\)C, 120 \({}^{\circ}\)C, and 160 \({}^{\circ}\)C were insignificant. After holding at 80 \({}^{\circ}\)C for 16 h, the sheet resistance of **G-4 \(\times\) 4-15** only increased by 4.5%, indicating its good thermal stability. The corrosion resistance of our GFTEs was tested by soaking **G-4 \(\times\) 4-15** in solutions with different pH values ranging from 1 to 14 for 1 min and measuring the change in its sheet resistance after rinsing and drying. As shown in Figure 7b, the sheet resistance increased by 3.8% in an acidic solution, which was the largest sheet resistance increase among different testing conditions. This result indicated that our GFTEs can work stably in corrosive environments. In conclusion, the GFTEs prepared in this work have good resistance to high temperatures and corrosion, which may be due to the superior stability of graphene over metallic nanowires. Figure 6: (**a**) Transmittance of **G-2 \(\times\) 2**, **G-3 \(\times\) 3**, and **G-4 \(\times\) 4** with 5 and 15 deposited layers; (**b**) performance comparison of sheet resistance and transmittance [43; 44; 45].
## 3 Methodology
We added the [PERSON]'s correlation coefficient (MCC) metric, and it is defined as follows:  
1. Accuracy: \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Prediction (Y)** & \\ \hline \multirow{4}{*}{Current (Ytest)} & \multirow{2}{*}{Positive} & Positive & Negative \\ \cline{2-4}  & & True Positives & False Negatives \\ \cline{1-1}  & & (TP) & (FN) \\ \cline{1-1} \cline{2-4}  & & False Positives & True Negatives \\ \cline{1-1}  & & (FP) & (TN) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix for binary classification. \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}. \tag{1}\]
2. Precision: \[Precision=\frac{TP}{TP+FP}.\] (2)
3. Recall: \[Recall=\frac{TP}{TP+FN}.\] (3)
4. F1 score: \[F1\;score=\frac{2\times Precision\times recall}{Precision+recall}.\] (4)
5. [PERSON]'s correlation coefficient (MCC): This is the only binary classification rate that generates a high score only if the binary predictor can correctly predict most positive and negative data instances. It ranges in the interval \([-1,+1]\), with extreme values \(-1\) and \(+1\) reached in cases of perfect misclassification and perfect classification, respectively. At the same time, MCC = 0 is the expected value for the coin-tossing classifier [15]. It is an alternative measure unaffected by the issue of unbalanced datasets. \[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)\times(TP+FN)\times(TN+FP) \times(TN+FN)}}\] (5)  
In summary, the methodological approach employed in this article combines the PCA technique with well-established SL algorithms for accurate data classification. In addition, our proposal searches for the hyperparameters of each algorithm to find the best results. Furthermore, we evaluated the effectiveness of this approach in terms of performance metrics. We compared it with previous work to demonstrate the utility of PCA analysis in data classification in next-generation networks.
# Monitoring of Training Load and Body Composition in Elite Male Kayakers
## 3 Results  
Table 4 shows the results of elite male Kayakers before and after preparatory and transition training periods. Changes in several variables were observed. Distance at VO2 peak on the kayak ergometer improved by 36.7 m from M1 to M3 (\(p\) = 0.0021, d = 0.98). The pace at V4 (s-km\({}^{-1}\)) reduced (i.e., improved) by 25.2 s-km\({}^{-1}\) from M1 to M2, and 25.6 s-km\({}^{-1}\) by M3, respectively. Body weight showed a noticeable reduction, with a decrease of 2.3 kg from M1 to M2. The fat mass, both in percentage and kilograms, also decreased. From M1 to M2, the fat mass percentage dropped by 1.8%, and by 3.1% by M3. Likewise, the fat mass in kilograms decreased by 1.8 kg from M1 to M2, and by 2.9 kg by M3. Conversely, fat-free mass increased over the same period. There was an increase of ~1.9% from M1 to M2, and a 3.1% increase by M3, respectively. In terms of specific skinfold measurements (Table 5), the subscapular skinfeld decreased by 0.9 mm from M1 to M2 and by 0.7 mm by M3. The suprailiac skinfold showed a larger decrease of 4.4 mm from M1 to M2, and 7.1 mm by M3. Similarly, the abdominal skinfold decreased by 3.0 mm from M1 to M2, and by 4.3 mm by M3. A reduction was also observed in the geminal skinfold, which decreased by 1.5 mm from M1 to M2. Aerobic power (VO2 peak in L-min\({}^{-1}\)) showed improvement by 0.7 L-min\({}^{-1}\) from M1 to M2, and by 1.1 L-min\({}^{-1}\) by M3, respectively. The increase from M2 to M3 was ~0.5 L-min\({}^{-1}\). The relative VO2 peak (i.e., in mL-kg\({}^{-1}\)-min\({}^{-1}\)) improved by 8.6 mL-kg\({}^{-1}\)-min\({}^{-1}\) from M1 to M2, and by 15.0 mL-kg\({}^{-1}\)-min\({}^{-1}\) by M3. The increase from M2 to M3 was 6.4 mL-kg\({}^{-1}\)-min\({}^{-1}\). Figure 2 illustrates the mean percentage changes from baseline to the preparatory and transition periods of a competitive season. Figure 2 elucidates well that male kayakers achieved significant improvements in physiological performance and body composition during the preparatory and transition phases elite, which were mainly determined by rises in aerobic power and advantageous changes in body composition. Figure 1: Study design.
# One-Photon-Interference Quantum Secure Direct Communication
## 5 Conclusions  
In summary, we have proposed an OPI-QSDC protocol, and its security has been analyzed when using practical coherent light sources. This protocol has the following advantages compared to previous QSDC protocols: (1) It has a longer transmission distance and a higher secrecy rate. It provides a new option for the application scenarios of QSDC; for example, the OPI-QSDC protocol can play a role in long-distance intercity quantum communication. (2). The property of MDI further improve the safety of the system when using devices with imperfections. (3). It is more practical because it uses coherent state light sources.  
The performance analysis of our protocol shows that, compared to the DL04 and MDI-QSDC protocols, it has a longer transmission distance which could achieve 443.5 km with a light intensity of 0.046 when using standard optical fiber. Additionally, it surpasses the PLOB bound when the transmission distance exceeds 228 km. In the future, the OPI-QSDC protocol has the potential to be put into practical application and may find applications in the global quantum Internet.  
Conceptualization, X.-J.L.; Software, X.-J.L.; Formal analysis, X.-J.L.; Writing--original draft, X.-J.L.; Writing--review & editing, M.W., X.-B.P. and Y.-R.Z.; Supervision, G.-L.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under Grants: 11974205, 12205011 and 62401324; the Young Elite Scientists Sponsorship Program by China Association for Science and Technology: 2022 QNRC001; the Key R&D Program of Guangdong province: 2018B030325002; Beijing Advanced Innovation Center for Future Chip (ICFC); Tsinghua University Initiative Scientific Research Program.  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.