esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Study Results
#### 3.2.7 User Experience  
In the header subjective tools of Table 1, the questionnaires that described users' experience of VR were also included. Eight studies evaluated their users' experience regarding VR via questionnaires. In particular, in their study, [PERSON] et al. [46] utilized the Modified Reality Judgment and Presence Questionnaire (MRJPQ) [7; 76] to assess the extent to which participants felt they were \"present\" in the VR environment, experienced the situation as real, and had an emotional impact. Furthermore, the participants filled out the Value of VR Questionnaire (VVR) [46] to assess how valuable they perceived VR. In their experiment, [PERSON] et al. [48] asked their participants to indicate their preferences concerning the environment while wearing VR glasses through a \"Yes/No/Hard to say\" response. [PERSON] et al. [49] evaluated the quality of the VR environment, but they did not provide further specification about the used instrument. In their study, [PERSON] et al. [51] used the Igroup Presence Questionnaire (IPQ) [77] to capture the sense of presence in VR and, also, the Simulator Sickness Questionnaire (SSQ) [78] to identify symptoms that occurred from VR exposure. Moreover, [PERSON] et al. [52] used the UCL Presence Questionnaire [79] to measure their respondents' perceived feeling of presence in VR. In the studies of [PERSON] et al. [54] and [PERSON] et al. [55], the participants completed a survey to assess three dimensions of the VR solution: comfort, level of immersion, and their general experience. Lastly, [PERSON] et al. [58] captured their respondents' feeling of connection with VR nature. Nevertheless, 50% of the sample did not assess the aspect of user experience at all [31; 35; 47; 50; 53; 56; 57; 59].
## 3 Research Methods
### Evaluation Tool
#### 3.2.2 Delphi Survey  
This study conducted a Delphi survey with a panel of experts to evaluate the content validity of the preliminary survey questionnaire [26]. The Delphi survey was conducted between 4 January and 23 January 2021. [PERSON] (1986) [27] suggested that 3 to 10 expert panelists are sufficient for evaluating content validity. In this study, four experts who have been in academia and industries related to DL technology and development for at least 10 years participated in the panel. Of the four experts participated in the Delphi, two respondents were classified as having a master's degree, and the remaining two as having a doctoral degree. In addition, they consist of one four-year university faculty, one principal researcher in a research institute, and two project managers with 15 years of industry and practice experience. The Delphi evaluation tool was constructed based on closed-ended questions using a 7-point Likert scale (e.g., one point = \"strongly disagree\", and seven points = \"strongly agree\") and open-ended questions. The closed-ended questions were created to evaluate whether the preliminary survey questionnaire could assess the sub-constructs that corresponded to each question. Furthermore, free opinions about the suitability, understanding, and consistency of the terms used in the questions were collected through open-ended questions in addition to the question's appropriateness. In the present work, the one-round Delphi study was conducted by email. The content validity ratio (CVR) of the response values to the closed-end questions was calculated to verify the degree of agreement of the expert opinion obtained from the Delphi survey. According to [PERSON] (1975) [28], the minimum acceptable CVR was 0.99 if the number of panelists was five. Thus, this study selected only the preliminary survey questions whose CVR was 1.0 to develop the questionnaire for the main survey. The excluded questions were not used in the main survey and final analysis. As illustrated in Table 1, for instance, the calculated CVRs for the measurement items of 1, 4, 6, 7, 11 through 14, 20, and 21 are less than 1.0, and these ten items were discarded for the final analysis. With respect to the ten pilot items for affective factors, calculated CVRs for the items of 1 through 4, 9, and 10 are less than 1.0 and again, these six measurement items were eliminated and not included in the final analysis (Table 2). Among the calculated CVRs for the measurement items of behavioral factors, the values for the items of 4 and 6 have CVRs less than 1.0 (Table 3). Thus, a total of nine measurement items for behavioral factors are considered for the final analysis. Figure 2: Developer Experience: conceptual framework (adapted from [PERSON], 2012).
# Quality Analysis of Unmanned Aerial Vehicle Images Using a Resolution Target
## 2 Theoretical Background  
### GSD Analysis Using the Bar Target  
We analyzed the GSD using a bar target in addition to the MTF to determine the image resolution and contrast. We compared the results to determine the necessity of MTF analysis. The spatial resolution analysis using the bar target is described below. As illustrated in Figure 2, the modulation function in an image can be represented by digital numbers (DNs), which are not continuous for each pixel in the original image [17].  
When a graph of numbers in non-continuous points is fitted by the method of least squares, the curve is expressed as a sine function in periodic form, such as Equation (1):  
\[\mathrm{Y}=\mathrm{a}+\mathrm{b}\mathrm{sin}\,\omega(x-x_{0}), \tag{1}\]  
where a is a coefficient for the digital numbers (DNs) in a pixel from which a curve begins before it moves toward the \(y\)-intercept; b represents the amplitude of the sine curve, i.e., the difference between the maximum and minimum values; \(\omega\) represents the period of the sine function and is related to the image GSD measurement; \(x\) represents the pixel sequence; and \(x_{0}\) is the distance moved in parallel toward the \(x\)-axis, causing a phase change that determines the form of the sine function. Hence, an accurate image GSD can be obtained by measuring the size of the black and white lines in the imaged target and dividing the size by the spatial frequency represented in the sine function with the coefficients calculated from Equation (1).
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.20 Greedy Perimeter Stateless Routing (GPRS)-Modified (GPSR-M)  
The general idea of the GPSR forwarding process is to select the neighbor closest to the destination as the next hop. Due to the mobility of VANETs, that idea may not always be optimal. For this reason, in [44] GPSR-M was proposed that included a mechanism based not only on position, but also on speed, direction, and link quality. In the enhancement, two processes were also included, future-position prediction for establishing if nodes are moving in the same road and direction; and next-hop weight calculation.  
Figure 11: Example of three potential paths in reliable intervehicular routing (RIVER).  
The improved mechanism also incorporates a future-position prediction process (getFuturePos), a next-hop weight-calculation process (CalculateW) and a novel process that determines if nodes are moving in the same road and direction (inSameRD). Equation (24) shows how the future position is calculated.  
\[FutPos_{x}=Pos_{x}+Vel_{x}*dt(speed) \tag{24}\] \[FutPos_{y}=Pos_{y}+Vel_{y}*dt(speed)\]  
where \(dt()\) is a mapping function that returns the time from 1.0 up to 4.0 s based on the speed parameter. The returned period decreases if the speed increases.  
To review if two vehicles follow the same road and have the same direction, a process calculates the vehicle-velocity vector angle, their dot product, and their line distance. With this information, the algorithm computes if they are moving on the same road. GPSR-M calls a _Forward_ procedure when packet transmission is needed. The best next-hop decision is made through the _BestNeighbor_ procedure that unchains the _CalculateW_ procedure. The resumed process is presented in Figure 12.
## 1 Introduction  
An optical frequency comb (OFC) emits an evenly spaced ultra-short pulse train with a broad spectrum consisting of discrete, narrow lines with uniform mode-spacing [1; 2]. The absolute frequency of each mode can be expressed as:  
\[f_{m}=mf_{\text{rep}}+f_{\text{coo}}, \tag{1}\]  
where \(f_{\text{rep}}\) is the repetition rate, \(f_{\text{coo}}\) stands for the carrier-envelope-offset frequency, and \(m\) is the mode order. When \(f_{\text{rep}}\) and \(f_{\text{coo}}\) are stabilized referencing a frequency standard, OFC becomes an ultra-precise ruler in the space, time, and frequency domain [3; 4; 5]. Therefore, it is useful for absolute distance measurement.  
In the past decade, numerous methods based on OFC have been proposed to measure absolute distance with high precision. These methods can be categorized into several groups according to the measurement principle and include: using the inter-mode beat signals of the comb [6; 7], applying dispersive interferometry [8; 9; 10; 11; 12; 13], using the pulse separation distance as a ruler [14; 15; 16; 17; 18; 19], and the dual-comb method [20; 21; 22; 23; 24; 25]. In order to suppress the effect of intensity noise, heterodyne interferometry has been introduced into the OFC distance measurement system [26]. The OFC heterodyne interferometry displays excellent results in temporal coherence interferometry, two-color correction of the refractive index of air [27; 28], and pulse-to-pulse alignment [29]. Therefore, it is a powerful tool for distance measurement.  
In a traditional laser heterodyne interferometer, the accuracy of the wavelength is very important for the distance measurement. However, in an OFC heterodyne interferometer, the light source has a wide optical spectrum; the spectral width of an optical frequency comb is normally tens of nanometers.  
Additionally, the optical spectra of the beams in the two arms of the interferometer can be different, and thus, it is ambiguous to determine the center wavelength. Determining the effective center wavelength of the interference signal is a basic but essential question for the OFC heterodyne interferometry which has not yet been investigated in detail.  
In order to resolve this problem, this paper introduced a method for calculating the effective center wavelength of the OFC heterodyne interference signal in an equal-arm interferometer included in a commercial interferometer. The method was then verified by simulation and experimental results.
# Impact of a Robot Manipulation on the Dimensional Measurements in an SPC-Based Robot Cell
## 3 Materials and Methods  
### Measurement Equipment  
For our study, we used an adaptive robotic cell (ARC) for automated SPC dimensional measurements developed in our laboratory in collaboration with the company Kolektor. The whole robotic cell consists of five modules, a module with a robot, a module with the Equator, a module for optical inspections, an accessory module with a storage area, and a module for the supply of subjects on measuring pallets. The ARC, therefore, makes possible contact and non-contact dimensional measurements. For our research, a subset of a module with a robot, a supply module, and a module with the Equator were used (Figure 2). The immediate observation was that the robot manipulation has a more significant influence on the variance of contact dimensional measurements than the non-contact dimensional measurements; therefore, non-contact methods were off the study.  
In the ARC and this study, we used a collaborative robot UR5e manufactured by Universal Robots, with an attached CRG 30-050 collaborative servo gripper from Weiss Robotics. For the contact measurements, the comparator principle-based measurement system Renishaw Equator 300 was used. The primary task of the robot is to tend the Equator with measured objects into a fixture in the Equator's working area. The robot was replicating the operator in traditional and manual measurements.
# Cross-Layer Protocol Based on Directional Reception in Underwater Acoustic Wireless Sensor Networks
## 5 Simulation Results
### Performance Evaluation of CLIC  
The CLIC protocol focuses on low interference and congestion. From (7), we know that \((\alpha,\beta,\gamma,\lambda)\) has important effects on the CLIC, which needs to be well investigated. Here, effects of \((\alpha,\beta,\gamma,\lambda)\) on energy consumption, end-to-end delay, congestion, and interference are investigated with settings of \((\frac{1}{4},\frac{1}{4},\frac{1}{4},\frac{1}{4})\), \((\frac{3}{4},0,0)\), \((0,1,0,0)\), \((0,\frac{1}{4},\frac{3}{4},0)\), and \((0,\frac{1}{4},0,\frac{3}{4})\), respectively.  
Figure 6 shows the end-to-end delay of CLIC under different parameters. It can be seen that the end-to-end delay of the network is mainly affected by network congestion and collision. The effect of distance on the end-to-end delay is enhanced when there are a sufficient number of nodes in the network. The emphasis on energy in relay selection is not conducive to reducing end-to-end delay.  
From Figure 7, it can be found that the highest PDR can be obtained when energy, distance, congestion, and interference factors are also considered in relay selection. The network also achieves a high PDR when only the distance factor is considered because packets are always delivered in the direction of the sink, reducing deto  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of sensor nodes & 55\(\sim\)75 \\ Simulation area & 3 km \(\times\) 3 km \(\times\) 1 km \\ Transmission range & 1 km \\ Initial energy & 30,000 J \\ Transmission power & 8 W \\ Receiving power & 0.8 W \\ Idle power & 20 mW \\ Data packet size & 256 bytes \\ Packet generation rate & 0.001 (packets/s)\(\sim\)0.0055 (packets/s) \\ Transmission rate & 2000 bps \\ Simulation time & 20,000 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: System symbols.  
Figure 6: End-to-end delay of CLIC.  
transmission failure probability. The network also achieves a high PDR when the weight of the interference factor is increased.  
As shown in Figure 8, relay selection that considers all four factors together achieves better energy efficiency. Lower energy consumption can also be achieved if the focus is only on distance advancement. Relay selection that considers energy, congestion, and interference factors alone does not achieve high energy efficiency as it leads to detour routes.  
A relatively balanced weight distribution is conducive to ensuring the stability of the network. According to the above simulation analysis, we set \((\alpha,\beta,\gamma,\lambda)=(\frac{1}{4},\frac{1}{4},\frac{1}{4})\) to obtain optimal network performance.
# Landslide-Tunnel Interactions and Control Countermeasures under an Orthogonal System
## 4 Research on the Application Effect of Anti-Slide Piles  
Tunnel deformation and landslide deformation are interrelated and influence each other. Currently, deformation control technology for tunnel-landslide systems is divided into two kinds of reinforcement means. This study focuses on the reinforcement effect of anti-slide piles [24; 25; 26]. Through a sensitivity analysis of the parameters such as the form of anti-slide piles, pile-tunnel spacing, pile diameter, and the depth of piles embedded in bedrock, we can provide theoretical guidance for controlling the stability of rock surrounding tunnels.
# Parametric Investigation of Die-Sinking EDM of Ti6 Al4V Using the Hybrid Taguchi-Rams-RATMI Method
## 3 RAMS-RATMI Method
\right] \tag{14}\]In a similar manner, a second matrix, '\(G_{i}\)', is constructed that contains components of the experimental trial:  
\[G_{i}= \begin{bmatrix}U_{ik}&0\\ 0&U_{ik_{i}}\end{bmatrix} ;i=1,2,\ldots,m \tag{15}\]  
Now, matrixes 'F' and '\(G_{i}\)' are multiplied to create the matching matrix '\(T_{i}\)':  
\[T_{i}=A\times B_{i}= \begin{bmatrix}T_{11;i}&0\\ 0&T_{22;i}\end{bmatrix} ;i=1,2,\ldots,m \tag{16}\]  
The trace of the matrix '\(T_{i}\)' above may be used to determine the MCRAT score, as demonstrated below:  
\[t(T_{i})=T_{11;i}+T_{22;i},\ i=1,2,\ldots,m \tag{17}\]  
Step 9: The RAPS method ranks alternatives based on their perimeter similarity, with the optimal alternative's perimeter representing the right angle's base and perpendicular side:  
\[P=Q_{k}+Q_{h}+\sqrt{Q_{k}^{2}+Q_{h}^{2}} \tag{18}\]  
Every alternative's perimeter is computed in the same manner. \[P_{i}=U_{ik}+U_{ih}+\sqrt{U_{ik}^{2}+U_{ih}^{2}} \tag{19}\]  
Alternatives are sorted in descending order based on their perimeter similarity, which is the ratio of each alternative's perimeter to the ideal one:  
\[PS_{i}=\frac{P_{i}}{P};i=1,2,\ldots,m \tag{20}\]  
Step 8: The RAMS method ranks alternatives based on their median similarity like the RAPS method (Step 7) where the ideal alternative's median is represented by an expression:  
\[M=\sqrt{\frac{Q_{k}^{2}+Q_{h}^{2}}{2}} \tag{21}\]  
Likewise, the following formula is used to determine the median for every experimental trial:  
\[M_{i}=\sqrt{\frac{U_{ik}^{2}+U_{ih}^{2}}{2}};\ i=1,2,\ldots,m \tag{22}\]  
The median of each trial is divided by the median of the ideal response values to determine the RAMS score (or \"MSi\") for each experimental trial. The ratio between the optimal answer and the alternative's perimeter is known as median similarity  
\[MS_{i}=\frac{M}{M_{i}};i=1,2,\ldots,m \tag{23}\]  
The RAMS score is used to rate the experimental trials in descending order, while alternatives are ranked in descending order based on the '\(MS_{i}\)'. Step 10: The RATMI score (Ei) is a ranking method that calculates the combination of the MCRAT and RAMS scores. \[E_{i}=v\frac{t(T_{i})-mint(T_{i})}{maxMS_{i}-minMS_{i}}+(1-v)\frac{MS_{i}-min MS_{i}}{maxMS_{i}-minMS_{i}},\ i=1,2,\ldots,m \tag{24}\]  
The majority index 'Ei' between MCRAT's strategy and RAMS's strategy is determined by assigning a weight '\(v\)' to each strategy, with \(\alpha\) ranging from 0 to 1. The RATMI score's declining values are used to rate the experimental trials from best to worst.
## Appendix A Dataset Description  
### Multi-Class ROC Analysis with RANs Modeling  
This study is carried out by two processes, first the input true-labels are transformed into a separate vector of binary labels, individually for all Abstract nodes (i.e., 1 for class c1, 0 for all other classes), second, calculating the confidence score for each instance of the input data (or test-data). Both processes are described as follows:  
1. **Node-wise binary transformation of True-Labels**: For example, suppose there are three classes (c1, c2, c3) represented by three abstract nodes (n1, n2, and n3) in RANs model at Layer-1, and let true-label be [c1, c2, c2, c1, c2, c3, c3] for 7 test instances, then for node n1 label will be [1, 0, 0, 1, 0, 0, 0] where 1 represents class c1, and 0 depicts others (i.e., c2, and c3).
2. **Node-wise confidence-score calculation**: This is calculated by averaging activation-value and confidence-indicator of activation for an input instance at an Abstract node. Activation-value is an individual activation of an activation vector obtained by propagating up the data using UAP mechanism of RANs whereas, confidence-indicator is calculated by min-max normalization operation of activation vector. For example, after UAP operation each node (n1, n2, and n3) receives activation [0.89, 0.34, 0.11] (a vector of activation), and confidence-indicator is min-max ([0.89, 0.34, 0.11]) = [1.0, 0.29, 0.0]. and the confidence-score for nodes n1 = (0.89 + 1.0)/2.0 = 0.95, n2 = (0.34 + 0.29)/2.0 = 0.32, and n3 = (0.11 + 0.11)/2.0 = 0.05. **Table A5.** Dataset specific configuration details of models.
## 5 Experiments and Results  
To validate the multi-objective optimization model for PI construction, the wind power data sampled every 10 min from the Alberta interconnected electric system in 2015 are applied. A total of 6000 datasets from 1 January are chosen as the research data with 80% for training and the other 20% for validating and testing, respectively. Firstly, the datasets are normalized in \([0,1]\) to adjust the parameters of WNN and avoid the influence from different magnitudes of the datasets. Then the structure of WNN using the Morlet wavelet as the basic wavelet is determined. It is necessary to balance the complexity and the learning capacity of WNN. The C-C phase space reconstruction and Kolmogorov theorem are adopted to choose the optimal WNN structure as 4-8-2. Finally, the parameters of WNN and EKMOABC will be set as mentioned in Section 4.  
The CWC for PI construction is a single-objective optimization problem, while PIMOC is a multi-objective optimization problem. To valid the experiment results, the basic ABC algorithm in [21] is used to optimize the CWC, and the MOABC algorithm in [22] to optimize the WNN for PIMOC. The food source number of ABC and MOABC are both set to 40, the maximum iteration time is set to 200, the maximum number of solutions in the archive is 20, and the maximum number of obsolete scout bees is 50.
## 3 Measuring Course Effectiveness
### Operational Outcomes  
Ultimately, the best means for measuring the extent to which the course has impacted the incorporation of ocean satellite data into operational oceanography is to document how graduates have applied the satellite data to their management activities. Unfortunately, operational users of satellite data do not necessarily publish all results in peer-reviewed scientific papers. Instead, results are made available via internal reports and whitepapers. As part of the February 2019 survey, past course participants were asked for literature citations of their work that used satellite data and have made a significant contribution towards addressing NOAA's operational goals. [PERSON] (class of 2006), studying the shrimp fishery, used satellite data to identify factors influencing early life survival of shrimp [27]. [PERSON] (class of 2006) used sea surface temperature and chlorophyll data to provide oceanographic context for NOAA's Marine Protected Areas (MPAs) along the California Coast [28]. MPAs are established by NOAA to protect ecosystems, preserve cultural resources, and sustain fisheries production ([[https://oceanservice.noaa.gov/facts/mpa.html](https://oceanservice.noaa.gov/facts/mpa.html)]([https://oceanservice.noaa.gov/facts/mpa.html](https://oceanservice.noaa.gov/facts/mpa.html))). [PERSON] (class of 2013) assessed the impact of the Deep Water Horizon oil spill on marine fish [29]. [PERSON] (class of 2007) developed a model to forecast stream temperatures that are important to the salmon fishery [30; 31]. [PERSON] and [PERSON] (both class of 2017) used satellite data to describe the habitat preferences of bluefin tuna in the California Current [32]. Post-course collaboration between course instructors and participants has resulted in the development of habitat models [33; 34], and the use of satellite chlorophyll measurements to assess the potential influence of harmful algal blooms on the decade-long increase in Southern Right Whale mortality [35].