esa-sceva/satcom-chunk-collection huggingface dataset:
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 2 Designing DaaG
### Main Functions of DaaG  
In this section, the main functions of DaaG are described in detail. The main functions are composed of communication scheduling, smart data processing, path-planning and data transfer method selection. The communication scheduling function continuously runs during DaaG operation and communicates with the sensors and the actuators. The collected data from the sensors and the actuators are utilized by other functions. The smart data processing function runs after DaaG visits every sensor node. Then, the function generates a problematic sensor list (PSL), which contains problematic sensors. The path-planning function consists of two parts: path-planning in the sensor network and path-planning in the actuator network. The former part of the function plans the flight path of DaaG and runs until DaaG collects data from every sensor. The latter part of the function determines which actuator has to be visited immediately and runs after DaaG has finished collecting sensor data. The data transfer method selection function determines which data transfer method touse. This function continuously runs during the DaaG operation. If this function decided to change the data transfer method, then all of the operating DaaGs would change the data transfer method. A more detailed description of the functions is explained in the following subsections.  
```
1:\(\forall c_{i}\in C:c_{i,bf}=c_{i,bmx}=1\)
2:while isDaaGWWorking() do
3:for each\(c_{i}\) in \(C\)do
4:\(c_{i,bf}=c_{i,bf}-1\)\(\triangleright\) Update remaining backoff time of \(c_{i}\)
5:if\(c_{i,bf}\leq 0\)then
6:\(c_{i,flag}=\) runBeaconing(\(c_{i}\))\(\triangleright\) Broadcast beacon message with \(c_{i}\)
7:if\(c_{i,flag}>0\)then
8:\(c_{i,bf}=c_{i,bmx}=1\)
9:\(D=\) runCommunication(\(c_{i}\))\(\triangleright\) Collect data \(D\) from nearby nodes with \(c_{i}\)
10:else
11:\(c_{i,bf}=c_{i,bmx}=\min\left(b_{max},\alpha\ \times c_{i,bmx}\right)\)\(\triangleright\) Update backoff time of \(c_{i}\)
12:endif
13:endif
14:endfor
15:if DaaG moves more than reset boundary length then
16:\(\forall c_{i}\in C:c_{i,bf}=1\)
17:endif
18: sleep(\(t_{period}\))\(\triangleright\) Wait for \(t_{period}\)
19:endwhile
```  
**Algorithm 1** Backoff-based beaconing algorithm.
## 4 Experiments and Results
As shown in Figure 11, the results of the nighttime tests indicated that while the segmentation techniques used for object detection had some impact, the actual distance measurement yielded the same results. Ultimately, to accurately measure the distance to the aircraft after applying the segmentation technique, the inherent vibration characteristics of the jetbridge must be reduced. Figure 10: Jetbridge cabin degree and distance error between the jetbridge and engine (daytime). Figure 11: Jetbridge cabin degree and distance error between the Jetbridge (blue box) and engine (nighttime).
# Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting
## 4 Object Proposal Voting  
The object proposal algorithm presented in the previous section has difficulty in finding appropriate object regions, if severe background clutter exists. Moreover, small-sized object regions are rarely proposed in this algorithm. To overcome these problems, a new object proposal voting algorithm is proposed.  
Voting for a new object proposal consists of three steps:  
* Step 1: The object candidate area more voted by the object proposal baseline (OPB) increases the weight. The weight is initialized to zero for all areas, and then, a value of 10 is assigned to a specific area whenever that area receives one vote.
* Step 2: All the weights by voting are computed and are expressed in the form of an image with values ranging from 0 to 255. In this case, if the weight exceeds 255, it is expressed as 255. This is called the voting image. Figure 1b shows the voting image.
* Step 3: An object proposal using the OPB is performed on the voting image again, as shown in Figure 1d. As a result that the area with many votes is bright and the area with less votes is dark, as shown in Figure 1b, object areas can be proposed more accurately and robustly against the background.  
The procedure outlined earlier can be formulated as follows. The vote grids \(\left\{g_{k}\right\}_{k=1}^{|\mathcal{V}|}\) are determined by dividing an input image into a regular grid, where each grid has the \(5\times 5\) size of pixels in all the experiments. A vote histogram \(\mathcal{V}\) has multiples bins \(\left\{b_{k}\right\}_{k=1}^{|\mathcal{V}|}\), in which each bin corresponds to each grid. Then, \(b_{k}=\left[g_{k};w_{k}\right]\) with \(g_{k}\in\mathbb{R}^{2}\) as the \(k\)-th vote grid and \(w_{k}\in\mathbb{R}\) as the corresponding vote weight. If the center position of the object candidate area, which is obtained by the OPB, belongs to a particular bin \(g_{k}\), we increase the corresponding vote weight \(w_{k}\) by an amount of a predefined value \(c\).  
\[w_{k}\leftarrow\max[255,w_{k}+c], \tag{4}\]  
where \(c\) is set to 10 for all the experiments and all vote weights \(\left\{w_{k}\right\}_{k=1}^{|\mathcal{V}|}\) are initialized to zero. The voting image \(I_{b}\) is constructed by assigning each vote weight for each bin as a value of the corresponding image grid, in which the value cannot be larger than 255 due to (4).  
\[I_{v}\sim I(g_{k})=w_{k},\text{ for }k=1,\cdots,|\mathcal{V}|, \tag{5}\]  
where \(I(g_{k})\) assigns the value of \(w_{k}\) to grid \(g_{k}\) of an image. As the last step, the closed boundaries in \(I_{v}\) are found using \(Contour\) in (3):  
\[\mathcal{O}_{v}=\big{\{}O_{j}\big{\}}_{j=1}^{|\mathcal{O}_{v}|}\sim Contour(I_ {v}). \tag{6}\]
## 2 Materials and Methods
### Methods
#### 2.4.2 Modeling of OTCI-Driven Models  
Three types of OTCI-driven models in combination with different input variables were established. Model 1 used the OTCI alone to estimate the GPP directly (GPP = \(f\) (OTCI)), according to the fact that GPP is directly related to chlorophyll content, which is also closely associated with OTCI. Therefore, OTCI is regarded as a proxy for LUE and APAR [60]. Model 2 combines OTCI and the incoming shortwave radiation that uses wavelength in the 400-700 nm region to estimate GPP (GPP = \(f\) (OTCI \(\times\) PAR)), according to [PERSON] and [PERSON] [61], who assumed a direct linear relationship between GPP and the product of photosynthetic efficiency and incoming shortwave radiation over a period of the growing season. Therefore, OTCI related to canopy chlorophyll content represents the photosynthetic efficiency here. Model 3 (GPP = \(f\) (OTCI \(\times\) PAR \(\times\) FAPAR)) introduces the actual incoming shortwave radiation by considering only absorbed PAR (APAR = PAR \(\times\) FAPAR) to be functioning for photosynthesis. Therefore, this model theoretically overcomes the limitation of the previous two models and is expected to obtain the highest potential for GPP simulation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Biome Types** & **DBF** & **CSH** & **OSH** & **WSA** & **GRA** & **CRO** \\ \hline \(\epsilon_{\text{max}}\) (g C m\({}^{-2}\) d\({}^{-1}\)) & 1.165 & 1.281 & 0.841 & 1.239 & 0.860 & 1.044 \\ \hline \(\text{TMIN}_{\text{min}}\) (\({}^{\circ}\)C) & \(-\)6.00 & \(-\)8.00 & \(-\)8.00 & \(-\)8.00 & \(-\)8.00 & \(-\)8.00 \\ \hline \(\text{TMIN}_{\text{max}}\) (\({}^{\circ}\)C) & 9.94 & 8.61 & 8.80 & 11.39 & 12.02 & 12.02 \\ \hline \(\text{VPD}_{\text{min}}\) (Pa) & 650.0 & 650.0 & 650.0 & 650.0 & 650.0 & 650.0 \\ \hline \(\text{VPD}_{\text{max}}\) (Pa) & 2300.0 & 4700.0 & 4800.0 & 3200.0 & 5300.0 & 4300.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Biome properties lookup table for the MODIS GPP algorithm.
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
## 2 Problem Description  
As shown in Figure 1, an EOS can maneuver in the direction of three axes (roll, pitch and yaw) for transitions between every two sequential observation tasks. Usually, the mobility of the roll angle represents the slewing maneuverability of the EOS. The maneuvering of the pitch angle enables the targets to be observed in advance or over time. Observation targets are accessible within a period of a specific VTW, which is determined by the maximum off-nadir angle. The observation window (OW) defines the start and the end time for observing target in the VTW. Therefore, the task scheduling algorithm enables an EOS to conduct certain operations for the transformation between two sequential observation tasks, such as slew maneuvering and payload switching. Simultaneously, observation tasks are restricted in a specific time interval, and the observations must be carried out continuously and completely within the VTW [37].  
It is noted that targets outside the observation range are invisible, and are be seen as invalid, as shown in Figure 1. An EOS could observe multiple targets simultaneously, and the observation task of the merged targets is defined as a clustered task in this study. Task clustering belongs to preprocessing for EOS task scheduling, which has gained more and more attention as it enables an EOS to finish more tasks at the cost of relatively few optical sensor opening times and satellite maneuver times. To clearly explain the EOSSP, herein, a summary of the most important notations in this paper is given in Table 1.  
Figure 1: EOS observes targets in orbit.
## 5 In-Structure Response Spectra
### Decoupling Criteria
where  
\[b=(1-e_{B})^{2} \tag{30}\]  
and \(\omega_{l}\) is the natural frequency of the structure with lumped equipment mass in Model B; \(\mu_{B}\) is the modal mass ratio calculated for the structure with the lumped equipment mass in Model B; \(e_{B}\) is the acceptable error in the natural frequency predicted by Model B compared to that calculated by the coupled model C. Equations (27) and (29) represent the relationship between the frequency ratio and the modal mass ratio for the acceptable error in the prediction of the coupled natural frequency using decoupled Model A and B, respectively. Figure 18 was plotted for \(e_{A}=e_{B}=0.1\), as adopted in ASCE/SEI 4-16 [4], assuming that the error between the frequency of the coupled model and the frequency predicted by model A or B shall be less than 10%. The structural response error from the modeling technique selected using Figure 18 may be large, but it always yields conservative results according to ASCE/SEI 4-16 and [PERSON] (1986) [5; 37]. Considering the indirect relation between ASCE/SEI 4-16 criteria and actual equipment response, additional decoupling criteria were investigated in this study. US NRC adopted decoupling criteria in NUREG-75/087 (1975) based on [PERSON] and [PERSON]'s criteria with \(e_{A}=e_{B}\)= 0.05, which is stricter than ASCE/SEI 4-16 [22]. In addition, US NRC simplified [PERSON] and [PERSON]'s criteria using linear lines as shown in Figure 19 and combined Region A, B and C as a single coupling-required region. As a result, the coupling-required region that begins at the mass ratio 0.01 was extended compared to ASCE/SEI 4-16 criteria. It is noted that US NRC decoupling criteria do not specify a model type between A to C in Figure 19 required for coupled analysis. [PERSON] et al. (2018) proposed new criteria based on a 5% change in the spectral displacement of the primary system between the uncoupled and coupled models, of which both primary and secondary systems are SDOF systems [23]. The decoupling criteria separate the plane of mass ratio and frequency ratio into four regions as shown in Figure 19 like ASCE/SEI 4-16 criteria, but using solid lines. However, region C where explicit coupled modeling is required is extended remarkably compared to ASCE 4-16 criteria.
# Blind Image Quality Assessment with Deep Learning:
## 3 Experimental Setup
### Evaluation Metrics  
The performance of BIQA algorithms are mainly assessed based on two metrics [1; 2; 9]: the Spearman Rank-order Correlation Coefficient (SRCC) Equation (1) and the Pearson Linear Correlation Coefficient (PLCC) Equation (2).  
\[SRCC=1-\frac{6\sum_{i}^{N}d_{i}}{N\times(N^{2}-1)}, \tag{1}\]  
where \(d_{i}\) is the difference between the \(i\)th image ranks in subjective and objective evaluations in an image database of size \(N\). SRCC can assess the prediction monotonicity of an BIQA method [1]:  
\[PLCC=\frac{\sum_{i}(q_{i}-\overline{q})\times(o_{i}-\overline{o})}{\sqrt{\sum _{i}(q_{i}-\overline{q})^{2}\times(o_{i}-\overline{o})^{2}}} \tag{2}\]  
where \(o_{i}\) is the subjective score (MOS) of the \(i\)th image, and \(q_{i}\) is the predicted objective score.
## 4 Predictive Analysis
### Screening Key Indicators  
#### 4.2.1 Data Selection  
According to the data from the official websites of WTO, WIPO, KOF, and the China Statistical Yearbook, we have compiled the data of the influencing factors evaluation indicators in Table 1 from 2010 to 2019. The results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **2010** & **2011** & **2012** & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** \\ \hline X\({}_{1}\) & 1.71 & 1.78 & 1.91 & 2.00 & 2.02 & 2.06 & 2.10 & 2.12 & 2.14 & 2.23 \\ X\({}_{2}\) & 4.62 & 7.19 & 8.24 & 8.90 & 10.35 & 10.08 & 11.87 & 14.53 & 17.94 & 20.48 \\ X\({}_{3}\) & 6.86 & 9.41 & 10.65 & 12.53 & 13.93 & 16.12 & 18.11 & 20.03 & 22.80 & 24.48 \\ X\({}_{4}\) & 46.43 & 45.40 & 44.66 & 46.57 & 47.47 & 50.57 & 52.54 & 53.06 & 54.82 & 53.28 \\ X\({}_{5}\) & 16.69 & 18.64 & 18.43 & 18.87 & 19.79 & 19.18 & 18.18 & 19.86 & 22.26 & 22.79 \\ X\({}_{6}\) & 157.78 & 189.84 & 204.87 & 220.90 & 234.23 & 227.35 & 209.76 & 226.33 & 248.67 & 249.95 \\ X\({}_{7}\) & 49.6 & 45.9 & 41.9 & 40.5 & 36.9 & 32.6 & 28.3 & 30.2 & 30.3 & 30.8 \\ X\({}_{8}\) & 1.66 & 1.71 & 1.77 & 1.81 & 1.86 & 1.91 & 1.95 & 1.98 & 2.03 & 2.17 \\ X\({}_{9}\) & 10.88 & 11.77 & 11.33 & 11.87 & 11.97 & 12.63 & 12.60 & 13.10 & 13.50 & 13.81 \\ X\({}_{10}\) & 61.83 & 62.31 & 62.55 & 62.65 & 63.42 & 63.88 & 63.3 & 63.61 & 64.27 & 64.28 \\ \hline \end{tabular}
\end{table}
Table 4: Data of influencing factors of international competitiveness of China’s computer information service industry.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Rank** & **Countries** & **Exports** & **Imports** & **Total** & **Net Exports** \\ \hline
1 & Ireland & 1244.41 & 57.88 & 1302.29 & 1186.53 \\
2 & United States & 556.57 & 437.20 & 993.77 & 119.37 \\
3 & Germany & 421.89 & 418.63 & 840.52 & 3.26 \\
4 & China & 537.85 & 268.61 & 806.46 & 269.24 \\
5 & India & 649.33 & 96.21 & 745.54 & 553.12 \\
6 & Netherlands & 280.82 & 187.76 & 468.58 & 93.06 \\
7 & United Kingdom & 297.25 & 133.49 & 430.74 & 163.76 \\
8 & French & 191.50 & 227.85 & 419.35 & 36.35 \\
9 & Swiss & 108.42 & 152.68 & 261.10 & 44.26 \\
10 & Singapore & 147.90 & 144.94 & 292.84 & 2.96 \\ \hline \end{tabular}
\end{table}
Table 2: World ranking of computer information service trade in 2019 (Unit: 100 million U.S. dollars).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Year** & **China** & **French** & **Germany** & **India** & **Ireland** & **Singapore** & **Swiss** & **UK** & **USA** \\ \hline
2010 & 0.72 & 0.85 & 1.15 & 4.22 & 4.97 & 0.43 & 1.07 & 0.87 & 0.57 \\
2011 & 0.82 & 0.76 & 1.13 & 4.01 & 4.90 & 0.49 & 1.01 & 0.84 & 0.55 \\
2012 & 0.94 & 0.79 & 1.19 & 3.92 & 4.64 & 0.61 & 1.10 & 0.84 & 0.59 \\
2013 & 0.94 & 0.76 & 1.16 & 4.11 & 4
# Design and Experiment of a Moving Magnet Actuator Based Jetting Dispenser
## 5 Experimental Results and Discussion
### Operating Frequency  
The third experiment investigates the effects on the droplet diameter by the operating frequency, as shown in Figure 10c. There are eight injection frequencies of 10, 20, 30, 40, 50, 60, 70, and 80 Hz, corresponding to the pulse widths of 100, 50, 33, 25, 20, 16.67, 14.28, and 12.5 ms, and average droplet diameters of 1.91, 1.55, 1.47, 1.35, 1.25, 1.16, 1.11, and 1.04 mm, respectively. It is clear that the dot size reduces when the working frequency increases. When the dispenser operates under a high frequency, the opening stage time decreases, and the needle stroke is limited. Thus, the gap between the needle top and nozzle-seat decreases. As a result, the fluid volume jetted out of dispenser is reduced, and the droplet size becomes smaller. In this experiment, the maximized achievable operating frequency is 80 Hz at a backpressure of 0.2 bar. If the frequency is over 80 Hz, the fluid cannot adequately fill in the chamber; the jetted fluid is discontinuous and interrupted, and the number of droplets is unsatisfactory. Another defect is small satellite droplets generated around the primary dots.
## 8 Simulation Setup and Results
### Execution Time  
We analyzed the execution time in the route discovery phase with the help of two online software development communities, namely Pretty good privacy PGP and Advogato [43, 44]. [PERSON]'s opinion is used to build the honesty of [PERSON] in the previously mentioned software development. This trustworthiness among connected users is categorized as given in Table 4. The execution time of these two software development communities is analyzed with AT [11] and TW algorithm.  
In PGP and Advogato datasets, the edge weights are given in ordinal values. To convert these values into real numbers on a scale of 1, we used linear transformation techniques [45, 46]. Table 5 lists enumeration of these datasets trust values to trust levels.  
The accuracy of the AT and TW algorithm is checked in addressing the execution time of the trust assessment problem efficiently. We used Python 3.7 [47] on Window-7-64 bit operating system. We run these algorithms on the sub-graphs containing the edge from truster to trustees. The PGP and Advogato datasets are divided into three parts based on the users. The user's category in Advogato and PGP are (\(<\)1K, 1K-3K, and \(>\)3K), (\(<\)7K, 7K-15K, and \(>\)15K). After that, we plot the execution time of AT and TW algorithms based on the above-mentioned user categories in Figure 8.  
For highly dense network AT algorithm need to execute \(O(n^{K})\) times to solve massive trust assessment problem, which corresponds to a larger searching depth and becomes extremely slow. On the other hand, TW is much efficient for trust assessment in VIoT, as execution time converges to \(O(n^{2})\). Combining and discounting operation in TW is a little complex as compared to summation and multiplication. However, considering both convergence time and accuracy, TW is an optimal solution to the efficient trust assessment problem in large scale VIoT.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Vertices’s** & **Edges** & **Ave. Deg** & **Diameter** \\ \hline Advogato & 6542 & 51,227 & 19.5 & 4.83 \\ PGP & 10,682 & 24,315 & 24 & 4.52 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of the Advogato and PGP datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Trust Level** & **Trust Opinion** & **Trust Value** \\ \hline
1 & (0.08,0.82,0,0.1) & 0.08 \\
2 & (0.26,0.64,0,0.1) & 0.26 \\
3 & (0.63,0.27,0,0.1) & 0.63 \\
4 & (0.85,0.05,0,0.1) & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of data-sets into real number values using four trust levels.
## 4 Algorithm Flow  
### Basic Flow  
As shown in Figure 5, the flow of TPSPOM can be divided into three parts. * **Part 1**: Initial data. Convert the circuit to all-HVT/all-SVT/all-LVT states separately, perform STA, and obtain the timing arc, derate, and power consumption information. Then obtain the initial path collection with the circuit at all-HVT. * **Part 2**: Sub algorithm. Obtain the result of the threshold voltage assignment based on the given path collection. The first path collection is provided in **Part 1**, and the subsequent one is in **Part 3**. * **Part 3**: Top algorithm. Apply the threshold voltage assignment result provided by **Part 2**, and perform STA. If the STA timing results do not meet the timing constraints, add new paths to the original path collection. Then the new path collection is provided to **Part 2**, repeat until the results of STA meet timing constraints. The details of the steps of the above process are implemented in Algorithm 1 (sub algorithm) and Algorithm 2 (top algorithm). Figure 5: Flow diagram of triple-threshold path-based static power optimization methodology (TPSPOM). When the path collection is given, the result of the assignment of cell thresholds is obtained according to the Algorithm 1 as follows. * Step 1: Initialize data. According to Equations (2)-(5) and (7), get \(t_{slack}\), \(t_{repairable_{i}}\) and \(R_{i}\) of each path \(i\) and get \(WL_{j}\), \(WS_{j}\) of each cell \(j\). \(WS_{j}\) denotes the weight brought by the replacement of the cell from HVT to SVT, and \(WL_{j}\) denotes the weight brought by the replacement of the cell from SVT to LVT. For the same cell, \(WS_{j}\) is greater than \(WL_{j}\), according to Equation (1). Swapping a cell to LVT is considered only after it has been swapped to SVT. \(W_{j}\) equal \(WS_{j}\) when cell is in at HVT, while equal \(WL_{j}\) when in SVT and equal 0 when in LVT. * Step 2: Iterate to find replacement cells. Find the most critical path \(m\) with the largest \(R_{m}\), then find the cell \(n\) with the largest weight \(W_{n}\) on the path. After that, swapping the cell \(n\) from HVT to SVT or SVT to LVT. * Step 3: Update data during iteration. When a cell is replaced, update all paths through it, and update the cells' weight on those paths. Algorithm 2 calls Algorithm 1 and keeps updating the path collection to find the optimal solution until the STA results meet the timing constraints.