esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Photodetector Electrical Structures
### Avalanche PD  
The simplest avalanche photodiode (APD) has a similar device structure to a p-i-n photodiode. However, a voltage close to its breakdown is usually applied to APD for detection of low power signal with high sensitivity. Under sufficiently higher external bias, electrical field in the photodiode's depletion region becomes high enough to initiate impact ionization which is responsible for carrier multiplication. Therefore, one absorbed incoming photon does not only generate one electron/hole pair but rather a large number of EPHs leading to a quantum efficiency potentially large than unity. The most important performance indices for APD is excess noise factor quantified by effective ratio of electron and hole ionization rate (k\({}_{\text{eff}}\)), gain-efficiency product and sensitivity.
## 2 Results and Discussion
### Effect of PAW Treatment on the Main VOCs in Rocket Leaves
#### 2.1.5 Alcohols  
Alcohols may be formed by the decomposition of fatty acid hydroperoxides or the reduction in aldehydes [34]. They are used as a defensive mechanism of plants and are often responsible for the 'cut grass' aroma found in leafy vegetables [1; 35]. In this study, a significant increase was observed in total relative abundance of alcohol with PAW-2 and PAW-10 samples. The content of hex-3-en-1-ol, which was detected as a major alcohol in Eruca spp [5; 16; 19], increased after PAW application. Additionally, PAW treatment increased the content of hexan-1-ol. These two compounds are typical green leaf volatiles (GLVs) produced naturally in plants. They are derived from linoleic acid through the lipoxygenase (LOX) enzymatic route [36; 37]. The pathway produces hexanal and hex-3-enal by the oxygenation of linoleic acid through the catalysis of LOX, which, by further reduction in the aldehydes, produces hexan-1-ol and hex-3-en-1-ol. According to our results, an increase in both compounds was observed with all PAW treatments when compared to controls, with a non-significant decrease in hex-3-en-1-ol in PAW-5 and -20. These results agreed with the previous study that reported the increase in the contents of hexan-1-ol and hex-3-en-1-ol observed in camu-camu pulp. The results were explained that either the LOX enzyme was activated by plasma application or the oxidation of linoleic acid was catalysed by the reactive oxygen species formed during plasma generation [38]. Further, 1-penten-3-ol, which is significantly correlated with sweet attributes in rocket [16], was increased in all PAW-treated samples, with a significant increase in PAW-2. Moreover, the significant increase in the content of pentan-1-ol and phenylethyl alcohol in PAW-2 samples was also observed. Octan-1-ol was significantly increased in PAW-20 samples. On the other hand, phenylethyl alcohol was not detected in PAW-5 samples.  
Interestingly, profiles of VOCs in all four PAW-treated rocket samples showed some differences, such that hierarchical clustering analysis (HCA) was performed on the data of the 52 compounds detected in all samples: control, PAW-2, PAW-5, PAW-10, and PAW-20, in order to have a conclusive idea as to the effect of PAW treatment on the volatile profile. As shown in the dendrogram (Figure 4), the five samples were sorted into three logical classes. PAW-10 was first grouped with the control sample with a 99.7% similarity level. The PAW-2 sample was then isolated but still grouped closely with a high similarity level (99.1%), which confirms that PAW did not induce significant changes in the volatile profile of these two samples. On the other hand, the profiles of PAW-5 and PAW-20 samples were grouped together, but were distinct from that of the control (similarity level 82.7%). However, the results indicated a non-significant effect of the PAW-treatment on rocket-salad's volatile profile. Further research is needed to better understand the effect of PAW-processing time on the volatile profile of rocket samples.
## 2 Materials and Methods
### Sample, Data Collection and Procedure  
The academic and administrative staff of the University of the Aegean in Greece was invited to participate in this survey, considering that adults are more likely to participate in many social groups and to support, due to their age, plenty of social roles. The total research population is consist of 747 members. In total, 123 members of the staff yielded the questionnaire; thus, 10 cases were excluded. Therefore, a total sample of 113 participants was included in our survey, giving a response rate of 15%. Before the distribution of the questionnaire to the research population, the instrument was tested for its form, language, clarity, difficulty and responsiveness to respondents' interests in a pilot study addressed to 20 members of the staff, in order to identify possible design problems and to revise items where it was necessary. The questionnaire was implemented through Google forms and its link was sent in the professional e-mails of the staff of the University of the Aegean. The procedure and the purpose of the survey were explained with clarity in the online questionnaire's introductory note, as well as ethics was plainly described. The instrument was also tested for its validity and reliability (values of Cronbach's Alpha index were >0.7 for each section).
# Predictive Scheduling with Markov Chains and ARIMA Models
## 3 Production Scheduling under Technological Machine Failure Constraint
### Basic Mathematical Notation of the Problem  
Prior to formulating the problem of robust job scheduling under uncertainty in the job-shop system, we need to define the elements of the production process:  
* Set \(M\) is a set of \(m\) machines (workstations) processing jobs: \[M=\{M_{1},M_{2},\ldots,M_{m}\}.\] (1)
* Set \(J\) is a set of \(n\) jobs (tasks) to process \[J=\{J_{1},J_{2},\ldots,J_{n}\}.\] (2)  
Processing job \(J_{i}\) on machine \(M_{j}\) constitutes an operation, which is called operation \(j\) of job \(i\) in the following. Therefore, it is necessary to define:  
* \(MO\)--a matrix of \(m\) columns and \(n\) rows describing the technology (the job order): \[MO=\begin{bmatrix}o_{ij}\end{bmatrix},\] (3) where \(o_{ij}\)--the order position of the operation \(j\) of job \(i\), which is \(o_{ij}=0\) when the job is not processed on the machine; and \(o_{ij}=\{1,\ldots,m\}\), when it is.
* Matrix \(PT\)--a matrix describing processing times of operations: \[PT=\begin{bmatrix}pt_{ij}\end{bmatrix},\] (4) where \(pt_{ij}\)--the processing time of operations \(j\) of job \(i\); for each \(o_{ij}=0\), \(pt_{ij}=0\).  
* Set \(FT_{MI}\) of potential machine failure times: \[FT_{MI}=\left\{ft_{M_{1}},ft_{M_{2}},\ldots,ft_{M_{2}}\right\}\text{, }l\in< 1;m>,\] (5) where \(ft_{M_{1}}\)--time to failure of the machine \(l\); where \(z\) is a natural number representing the \(z\)-th machine failure.
* Set \(TB_{MI}\) of time buffers to include in the nominal schedule (for machine \(l\)) to obtain a robust schedule: \[TB_{MI}=\left\{tb_{M_{1}},tb_{M_{2}},\ldots,tb_{M_{2}}\right\}\text{,}\] (6) where \(tb_{M_{2}}\)--the size of the time buffer in the schedule at the failure time \(ft_{M_{1}}\); where for \(ft_{M_{2}}\
eq 0\), \(tb_{M_{2}}\
eq 0\).
# A Novel Impact Rotary-Linear Motor Based on Decomposed Screw-Type Motion of Piezoelectric Actuator
## 4 Experimental Tests and Discussions
### Prototype Motor
#### 4.2.1 Rotary Motion of the Prototype Motor with No Load  
When adjusting the axial prepressure generated by spring to about 1 N and adjusting the radial prepressure generated by snap ring to about 14 N, the prototype motor realizes rotary motion under the saw-shaped driving voltage. Meanwhile, the measured friction torque between the PA and coupler is about 0.0003 N\(\cdot\)m and the measured friction force between the coupler and shaft tested is about 4.2 N in axial direction.  
When the driving voltage is 720 V\({}_{\text{P-P}}\) with a duty ratio of 0%, and the driving frequency is 200 Hz, 500 Hz, and 1 kHz, the output rotary angles and linear displacement of the prototype motor in the two directions are shown in Figure 7. Assuming this driving voltage can produce the negative movement of the motor at this driving voltage, when the duty ratio of the driving voltage is 100%, the motor will realize positive movement. According to Equations (1)-(3), when the compensation factor is ideal and is equal to 1, the calculated maximum rotary angle of the PA is 1280 \(\upmu\)rad and the maximum linear distance is 1.98 \(\upmu\)m.  
From the results shown in Figure 7, the piezoelectric motor can realize continuous rotary motion. The tested maximum rotary angle of the PA is about 1000 \(\upmu\)rad and the maximum linear distance is about 1.03 \(\upmu\)m. Due to the imperfect processing technology of the piezoelectric tube, manufacturing error, and installation accuracy, the compensation factor \(k_{1}\) is 0.8 and the compensation factor \(k_{2}\) is 0.5. With the increase of driving frequency, the slip angle becomes smaller and smaller, and the rotary motion becomes more and more smooth.  
When the driving frequency continues to increase and other conditions remain unchanged, the prototype motor will keep rotary motion until the driving frequency reaches 4 kHz. By changing the duty ratio, the motor can rotate in reverse direction. The two directions of the average rotary velocity of the prototype motor are tested when the driving frequency ranges from 200 Hz to 4 kHz, and the results are shown in Figure 8. The results show that the prototype motor can realize rotary motion from 200 Hz to 4 kHz, and the prototype motor can achieve the maximum rotary velocity of about 9.9 \(\times\) 10\({}^{5}\)\(\upmu\)rad/s at 2 kHz under the driving voltage of 720 V\({}_{\text{P-P}}\) with a duty ratio of 0%.  
Figure 6: Schematic diagram of experimental testing system of prototype motor.
# Monocular Vision Guidance for Unmanned Surface Vehicle Recovery
## 2 Materials and Methods
### Visual Localization of the USV  
The positioning of USVs on the water surface is achieved by measuring the distance and angle between the camera and the artificial marker using monocular vision. The distance obtained from visual localization is the linear distance between the camera and the artificial marker, while the angle refers to the angle between the camera's optical axis and the artificial marker. By calculating the distance and angle, the USV can be guided back to the mother ship.  
The centers of the four circular rings in the artificial marker are coplanar, and none of these three points are collinear. Based on the obtained four center points, the PNP method is utilized for visual localization. Given the known pixel coordinates of the coplanar four points and their corresponding world coordinates, a unique solution can be obtained. The camera's captured scene is illustrated in Figure 6.
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 4 Results
### Scalability to Long Records  
One desirable feature of a seismic-based system for traffic monitoring is its ability to operate continuously with minimal supervision, which means the system needs to deal with long records (e.g., several weeks or months). For that reason, we evaluated  
Figure 8: (**a**) A continuous seismic record 20 min long. (**b**) Detail of (**a**) showing a data window 1 min long divided into 5 s waveforms (red boxes) with gaps of 1 s between them. (**c**) The probability of vehicle types during the window in (**c**) is estimated using DNN, (**d**) CNN, and (**e**) RNN. Events during the window include the passage of (**f**) a bus, (**g**) a motorcycle, and (**h**) a car in mixed traffic. The vibrations recorded at times of pictures (**f**), (**g.h**) are displayed on panel (**b**).  
Figure 9: (**a**) A video frame documenting several vehicles passing the receiver at time 16:33:43. (**b**) The seismic noise generated a 10 s window. (**c**) Vehicle type probabilities estimated by CNN at intervals of 1 s during the window in (**b**) contain 10 interpretation points with 80% overlapping.  
the computational cost of the three models, ignoring their accuracy and focusing on the scalability of networks to handle large records. We chose 1 h of data to measure running time and memory usage, then repeated the measurements after successively doubling the size of the dataset to a maximum of 1024 h (nearly 43 days) (Figure 10). CNN interpreted a month-long (720 h) record in 70 min, a computation time 10% faster than DNN. CNN also had the lowest memory usage, requiring 40% less memory than RNN. In terms of computational cost for long records, CNN was more efficient than DNN and RNN.
# Centralized Energy Prediction in Wireless Sensor Networks
## 2 Related Work
#### 3.2.1 Sensing Module  
The sensing modules operate mostly using two states: active and sleep mode. In the active state, the module takes the measurement and transmits it to the MCU. In the sleep mode, it turns off most of its submodules to reduce the energy consumption and remains waiting for a signal to acquire the next measurement. Considering both states and the transitions between them, the sensing module energy consumption (represented as \(E_{Sen}\)) is calculated using Equation (6). The term \(E_{sen-act}\) represents the active state, \(E_{sen-sleep}\) the sleep mode, \(E_{sen-on}\) the turning on transition, and \(E_{sen-off}\) the turning off transition.  
\[E_{Sen}=E_{sen-act}+E_{sen-sleep}+E_{sen-on}+E_{sen-off} \tag{6}\]  
To calculate the energy consumption for a certain period, the terms \(E_{sen-act}\) and \(E_{sen-sleep}\) are expressed in terms of power consumption and time. Then, we calculate the transitions energy consumption by multiplying the average transition energy consumption by the total transitions counted in this period of time. Thus, \(E_{Sen}\) is expressed as shown in Equation (7), where \(P_{sen-act}\) and \(P_{sen-sleep}\) are the active and sleep mode states average power consumption; \(T_{sen-act}\) and \(T_{sen-sleep}\) are the time spent on the active and sleep mode states; \(e_{sen-on}\) and \(e_{sen-off}\) are the average energy consumption of one turning on and turning off transitions; and \(N_{on}\) and \(N_{off}\) are the number of transitions.  
\[E_{Sen}=P_{sen-act}T_{sent-act}+P_{sen-sleep}T_{sen-sleep}+N_{on}e_{sen-on}+N_ {off}e_{sen-off} \tag{7}\]
# The Thermal Analysis and Heat Dissipation Structure Optimization of a Propeller Driver System
## 3 Thermal Analysis and Optimization of Heat Sink Based on Sealed Cabin
### The Influences of Fin Length on Thermal Performance  
The influences of fin length on thermal performance are studied from cases with the optimal base thickness of 40 mm, 2 mm fin thickness, and 18-fin system. While the fin length is larger than 36 mm, two rows of fins will contact each other. Therefore, the max value of fin length in the studied cases is 36 mm. Figure 16 illustrates how the lengthening of fins can help to decrease the device's temperature. The temperatures of devices U1, U2, U3, and U4 all drop down as the fin length increases from 4 mm to 36 mm. The temperature decreased by approximately 2.2%, 4.7%, 9.9%, and 15% for U1 to U4, respectively. This is due to the fact that the lengthening of fins can increase their surface area for heat convection and so strengthen the heat transfer process. But, when the length of the fin is larger than 32 mm, the space between two rows of fins is very small. That causes the weakness of heat convection inside the rectangular hole of the heat sink. Then, the strengthening effect of increased fin area is canceled. Therefore, the optimal fin length for this cylindrical heat sink  
Figure 14: The temperature field of the cylindrical heat sink and driver.  
is about 32 mm. At this time, the temperatures of U1, U2, U3, and U4 monitoring points are 38.95 \({}^{\circ}\)C, 42.24 \({}^{\circ}\)C, 46.27 \({}^{\circ}\)C, and 46.89 \({}^{\circ}\)C.
## 3 Results and Discussion
### Group Criteria Weights  
The individual responses were then aggregated using the approach described in Section 2.6. This resulted in a group weight for each criterion. Figure 5 shows the dispersion of the individual expert weights and the final weight for each criterion.  
The variable weights show a considerable spread among the experts, with the smaller differences (considering the 25 th-75 th percentile interval) found in the criteria S, B, P1, P2, P3, E3, S2, S3, P12, E11, E12, and E13. The black dot (final weight of the criteria group) is included in the 25 th and 75 th percentile of the individual weights, except for the criteria S12 (number of inhabitants with high school education) and E13 (percentage of forest land use), and for some criteria it almost corresponds to the median. These results support the use of the proposed approach, as all the answers of the experts have been taken into account, but the final weight for each criterion has a mathematical analysis instead of a simple average of the individual weights.  
\begin{table}
\begin{tabular}{c c c} \hline
**Statistics** & **CR** & **ED** \\ \hline Minimum & 0.00 & 0.00 \\ Maximum & 0.73 & 20.79 \\ Median & 0.08 & 2.63 \\ Quartile 75 & 0.16 & 4.44 \\ Percentile 90 & 0.28 & 7.69 \\ \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics of the pair-wise matrix consistency criteria CR and ED.
## 5 Conclusions and Discussion  
An improved three-step method has been proposed for identifying and tracking individual active bed-load particles in complex backgrounds. The first two steps, while similar to those used in the classic method, introduces a new algorithm for locating accurate particle centers and Kalman filter for matching particles. The third step serves to find broken chains and to connect them into a complete chain. Locating accurate particle centers reduced the errors caused by high-frequency measurement and facilitates subsequent tracking of particles. The Kalman filter keeps tracking the moving particles, and global connection greatly enhanced the effectiveness of particle tracking.  
The present method was validated against simulated images with different parameters. The results of number of active particles, velocities, fight time and length show that the new method successfully solves the new problems caused by frequency and complex background.  
The present method has also been used to process real bed-load transport images. Results show that while the classic method tends to underestimate the flight length and time, the new method can provide more accurate information (parameters) on bed-load dynamics than previous methods. As these parameters, e.g., \(N\), \(L\) and \(T\), affect not only the transport rate, but also the advection and diffusion characteristics of bed-load transport, the present method shows promise as being a more reliable tool for gaining deeper insight into the physics of bed-load transport.  
While the present three-step method proposed can accurately track multiple particles moving above complex background, its performance in practice is affected by factors, such as camera specification, sampling frequency, and size of the observation window. For instance, if the observation window is small, it is impossible to capture the entire flight process of a bed-load particle, and thus the measurements would underestimate the real flight length and durations of moving bed-load particles. Thus, application of the new approach requires more experience and optimization of parameters like the distribution of entrained, gliding and deposited particles.  
Conceptualization, W.M. and Q.Z.; methodology, W.M.; data curation, W.M.; writing--original draft preparation, W.M.; writing--review and editing, W.M.; supervision, D.L.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
The research reported herein is funded by National Nature Science Foundation of China (No.51879138 & No.51809268).  
There are no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results
### Impact of Factors on the LUEmax  
#### 3.2.1 Impact of Factors on the LUEmax Across Different Vegetation Types  
The factor detector in the Geodetector model was used to analyze the extents of the impacts of ten factors (EVI, LSWI, LAI, TA, TS, VPD, P, SWC, CO2_F, and NETRAD) on the LUEmax. The results show the explanatory power of each factor for the spatial differentiation of the LUEmax (Figure 5). All environmental factors influenced the spatial variability of the LUEmax at a statistically significant level (\(p<0.05\)). The variability in the LUEmax impact factors was significant across different vegetation types.  
For ENF, the most significant factor for LUEmax was the NETRAD, followed by TA and TS, indicating their sensitivity to atmospheric and climatic conditions. The LUEmax of DBF is mainly affected by the NETRAD and TA, reflecting their temperature dependence. EVI is the key driving factor of the LUEmax of GRA, while TS and TA also play important roles. For WET, the LUEmax is mainly influenced by the SWC, EVI, and TA, emphasizing its dual dependence on vegetation cover density and temperature conditions. For CORN, EVI is the primary factor. Meanwhile, the LSWI and LAI are also important influence factors.  
These findings underscore that significant differences exist in the influence of various factors on the LUEmax among different vegetation types. The impact of the NETRAD is more prominent for ENF and DBF, while it is relatively small in other vegetation types. TS has a more significant influence in ENF, DBF, and CORN. The influence of CO2_F is relatively small among all vegetation types. Other factors, such as SWC, P, VPD, TA, LAI, LSWI, and EVI, have effects of varying extents on the LUEmax among different vegetation types.  
Figure 5: The q values of the factors across different vegetation types.  
The interaction detector was utilized to gauge the extent to which factor interactions can explain the spatial variation in LUEmax across various vegetation types, as illustrated in Figure 6. When the q value of the interaction between two factors exceeds the higher q value of either factor (X1 or X2) but remains lower than their sum, this indicates a mutual enhancement effect. If the q value of their interaction surpasses the sum of both individual q values, this suggests a nonlinear enhancement between the two factors. The results of this study indicate that the q value for any two-factor interaction in the spatial variation in LUEmax exceeded that of a single factor, suggesting that the explanatory power of individual factors increases when they interact. Moreover, the combined effect of two factors on the LUEmax's spatial variation was stronger than that of either factor alone.