esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
## 3 Comparing EDZL Schedulability Tests  
A schedulability test is a method to verify the schedulability of a task set prior to actual scheduling. If a schedulability test is too pessimistic, it can admit a small number of task sets and the processors may not be utilized enough. Hence, a schedulability test needs to admit as many task sets as possible.  
We evaluate and compare three EDZL schedulability tests--the slack-based test, the utilization-based test, and the demand-based test--in terms of the number of task sets admitted by each test. For evaluation, we generate all possible task sets in a restricted range of task parameters. The parameters are the number of tasks in a task set, the period of each task, and the execution time of each task in the domain of integers. The number of tasks in a task set is varied from 3 to 6, i.e., \(n=3,4,5,6\). The period of each task is varied from 2 to 13, i.e., \(p_{i}=2,3,\ldots,13\). And the execution time is varied from 1 to (_period_ \(-\) 1), i.e., \(c_{i}=1,2,\ldots,p_{i}-1\). The total number of task sets generated in this range is 406,478,384.  
Each task set was tested with the three EDZL schedulability tests, varying the number of processors from 2 to (\(n-1\)). We excluded test instances (test instance: the combination of task set and the number of processors) whose task set utilization exceeds the number of processors. The total number of test instances is 1,000,752,406.
_Article_  
Using Social Media to Identify Consumers'  
Sentiments towards Attributes of Health Insurance  
during Enrollment Season  
[PERSON] *\(\copyright\) and [PERSON]  
Center for Health Services Research, The Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +1-(802)-656-2722; Fax: +1-(802)-656-8577  
Received: 27 April 2019; Accepted: 13 May 2019; Published: 17 May 2019  
**Featured Application: The health insurance choice literature has found that financial considerations, such as premiums, deductible, and maximum out-of-pocket spending caps, are important to consumers. But these financial factors are just part of the cost-benefit trade-off consumers make. Publicly available datasets often do not include these other factors. Researchers in other fields have increasingly used web data from social media platforms, such as Twitter and search engines to analyze consumer behavior using Natural Language Processing. NLP combines machine learning, computational linguistics, and computer science, to understand natural language including consumer's sentiments, attitudes, and emotions from social media. This study is among the first to use natural language from an online platform to analyze sentiments when consumers are discussing health insurance. By clarifying what the expressed attitudes or sentiments are, we get an idea of what variables we may want to include in future studies of health insurance choice.**  
This study aims to identify sentiments that consumers have about health insurance by analyzing what they discuss on Twitter. The objective was to use sentiment analysis to identify attitudes consumers express towards health insurance and health care providers. We used an Application Programming Interface to gather tweets from Twitter with the words \"health insurance\" or \"health plan\" during health insurance enrollment season in the United States in 2016-2017. Word association was used to find words associated with \"premium,\" \"access,\" \"network,\" and \"switch.\" Sentiment analysis established which specific emotions were associated with insurance and medical providers, using the NRC Emotion Lexicon, identifying emotions. We identified that provider networks, prescription drug benefits, political preferences, and norms of other consumers matter. Consumers trust medical providers but they fear unexpected health events. The results suggest that there is a need for different algorithms to help consumers find the plans they want and need. Consumers buying health insurance in the Affordable Care Act marketplaces in the United States choose lower-cost plans with limited benefits, but at the same time express fear about unexpected health events and unanticipated costs. If we better understand the origin of the sentiments that drive consumers, we may be able to help them better navigate insurance plan options and insurers can better respond to their needs.  
social media; Twitter; text mining; sentiment analysis; word association; health insurance; provider networks
# Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping
## 4 Tunnel Digital-Mapping-Based Online Rock Mass Evaluation  
### Challenges  
Manual tunnel face mapping often yields rock mass information with low reliability relative to the time and effort invested. The variability in results can be considerable, largely depending on the individual skills and preferences of the geologist, which complicates the attainment of accurate and consistent evaluations of rock mass quality. This inconsistency underscores the need for more standardised methodologies in rock mass assessment.  
Figure 13: An RMR report using an automated rock mass classification system.  
Digital tunnel mapping can mitigate these challenges by allowing geologists to capture images of the tunnel excavation face using devices such as tablets or mobile phones. Thereafter, the geologists relocate to a position that does not obstruct subsequent work activities. The recently developed system enables geologists to input geological information directly onto the excavation face image within minutes, thereby streamlining the mapping process and facilitating the automatic generation of rock mass evaluation reports.  
The transition to a digitised approach minimises the errors and inaccuracies often associated with subjective differences in the skills and opinions of geologists during mapping and assessment. This process enhances the efficiency and accuracy of mapping, report writing, and information delivery.  
While the current system markedly improves the mapping process, the biases and skill disparities among geologists cannot be entirely eliminated without implementing methods that incorporate automatic geological information assignment through deep-learning techniques. The integration of such advanced analytical methods will represent the next steps in the development of more robust geological evaluation systems.
# Uncovering Insights for New Car Recommendations with Sequence Pattern Mining on Mobile Applications
## 3 Method
### Measures  
The sequence rules were assessed using three metrics: Support, Confidence, and Lift. Support (XY) or Support (X\(\rightarrow\)Y) indicates the percentage of times that \"X followed by Y\" happens out of the total members. The left-hand side represents X, and the right-hand side represents Y. The minimum support count refers to the number of members corresponding to the support percentage. Various sequences can be created when considering the sequence pattern, such as Support (XYZ), which indicates the occurrence of \"X followed by Y and then Z.\" Equations (1)-(3) exemplify the three measures for the sequences X\(\rightarrow\)Y\(\rightarrow\)Z.  
\[\text{Support (X\rightarrow Y\rightarrow Z) = (number of members containing X\(\rightarrow\)Y\(\rightarrow\)Z)/(total number of members)} \tag{1}\] \[\text{Confidence (X\rightarrow Y\rightarrow Z) = Support (X\rightarrow Y\rightarrow Z)/Support (X\rightarrow Y)}\] (2) \[\text{Lift (X\rightarrow Y\rightarrow Z) = Support (X\rightarrow Y\rightarrow Z)/[(Support (X\rightarrow Y)\times Support (Z))]} \tag{3}\]  
Confidence (XY) measures the association between two items and represents the percentage of times that {Y} is observed when {X} is present. This is a straightforward calculation when considering only one {X} and {Y}, but it becomes more complex when multiple steps (e.g., XYZ) are involved. Therefore, Confidence is typically not calculated if the rule includes more than two sequences. However, if the last item in the XYZ sequence is regarded as the right-hand side while the preceding items are considered the left-hand side, Confidence can be used to measure the rate at which the rule is true. For example, Confidence (XYZ) is calculated as the percentage of time that {Z} occurs if {X then Y} occurs.  
Similarly, Lift (XYZ) measures the strength of the relationship between two sets (left- and right-hand side). It is calculated by dividing the support of the combination of {X then Y} and {Z} by the product of the support of {X then Y} and the support of {Z}. This metric indicates the likelihood of observing the simultaneous occurrence of {X then Y then Z} compared to the probability of the two events occurring independently.
# Numerical Simulation Analysis Method for Rockburst Control in Deeply Buried Caverns
## 3 Numerical Analysis Method for Rockburst Control with Anchor Rod Reinforcement  
According to the strength theory of rockburst, a rockburst may occur when the strength of the rock mass is damaged to the extent that it cannot resist the stress concentration after the excavation of the underground cavern. Therefore, using anchor rod support to increase the integrity of the surrounding rock improves its stiffness and reduces the strength damage and stress concentration, which is the most effective measure to prevent rockburst. Due to the need to compare the anchor bolt schemes during the design process, multiple models need to be established according to conventional methods and the difficulty in coordinating anchor rod elements with rock mass elements in numerical calculations; this paper proposes to use implicit anchor column elements to simulate the restraining effect of anchors on rockburst.
# Vibration Fatigue Life Reliability Cable Trough Assessment by Using Weibull Distribution
## 3 Proposed Random Vibration--Stress Method  
### Random Vibration Stress Analysis  
Step 1. Collect input data.  
From the applied PSD determine the vibration requirements. Based on Equation (3) [18], determine the corresponding sample size \(n\) to be used in the Weibull analysis.  
\[n=\frac{-1}{\ln(R(t))} \tag{3}\]  
Step 2. Perform the vibration testing according to the vibration requirements of step 1. The vibration testing can be performed by using a vibration system, or by simulation. Step 3. Determine the acceleration response.  
Once the vibration testing is completed, the acceleration responses [8] are determined as  
\[A_{res}=\frac{2\pi^{2}F^{2}D}{G} \tag{4}\]  
where \(F\), is the frequency, \(D\) is the displacement and \(G\) is the gravity constant.  
Step 4. Determine the dynamic factor.  
The vibration dynamic factor [3] is calculated as  
\[\sigma_{dyn}\ =\ \left(\frac{Km_{e}LC}{I}\right)G \tag{5}\]  
where \(K\) is the stress concentration factor in the component, \(C\) is the distance to the neutral axis, \(\hat{L}\) is the distance from the fixed point of the component to the point of application of the mass, \(G\) is the gravity constant. \(I\) is the moment of inertia and \(m_{e}\) is the effective mass, they are given by Equations (6) and (7) [3], respectively.  
\[I=\frac{HB^{3}-hb^{3}}{12} \tag{6}\]  
\[m_{e}\approx 0.225\rho L+m \tag{7}\]  
In Equation (6), from the structural geometry (hollow beam), \(H\) and \(h\) are the heights, and \(B\) and \(b\) are the widths. In Equation (7), \(\rho\) is the density of the component's material, \(L\) is the length, and \(m\) is the mass applied to the component.  
Step 5. Determine the vibration stress and the maximum \(\sigma_{1}\) and minimum \(\sigma_{2}\) principal components values.  
Once the acceleration response from Equation (4) and the dynamic factor from Equation (5) are known, the vibration stress is calculated by Equation (8) [3].  
\[\sigma_{vib}=A_{res}*\sigma_{dyn} \tag{8}\]  
From the results obtained, take the maximum \(\sigma_{vib}\) value as \(\sigma_{1}\) and the minimum \(\sigma_{vib}\) value as \(\sigma_{2}\). The diagram with the steps to determine the vibration stress \(\sigma_{vib}\) is shown in Figure 1.  
Now, let us use the above data to perform the corresponding Weibull analysis, based on which the corresponding reliability indices are determined.
## 4 Assessment
Figure 17 presents a graphic depicting the workload of the different controllers before and after the introduction of controller 2. The scalability assessment evaluates the time the D&D takes to decide the new distribution (Step 2) and the time needed by the architecture to reconfigure the system (Step 3). This depends on the number of automation software modules to be reconfigured as well as on the number of PLCs that may run the involved software modules. For this test, all need to be relocated and the reconfiguration is launched when the control software modules operate in a non-critical state. Table 1 presents the results of this test. The first column represents the time in milliseconds the D&D takes to calculate the new distribution of the _PlantAsset_ software module; while the following columns present the time it takes to reconfigure each. Figure 16: Sequence diagram detailing the triggering of the Workload Optimization process and the redistribution of active PSs among the controllers: when Controller 2 joins the system, CA2 informs the QoS monitor that its workload is too low, and after confirmation, the QoS Monitor sends a reconfiguration event to the D&D (Step 1). The D&D requests information to the MM and after receiving it, it triggers the Workload Optimization Process (Step 2). As a result of this optimization, the D&D stops PSA3.1 at the first non-critical situation, changes the state of PSA3.1 and PSA3.2 to “wait”, and later changes the state of PSA3.2 to “active” and the state of PSA3.1 to “tracking” (Step 3). Figure 17: Workload of controller 1 and controller 2 before and after the reconfiguration: the orange box (reconfiguration process) corresponds to the for loop shadowed in blue in Figure 16. As a result of the reconfiguration process, the workload of Controller 1 decreases (now it has two active Production Services), and the workload of Controller 2 increases (it has one active Production Service). The following figures illustrate how the execution time of the re-distribution algorithm (Figure 18) and the overall reconfiguration time (Figure 19) increase with the number of software modules to be reconfigured. However, the time to make a decision is negligible in comparison with the overall reconfiguration time, as it is computed within the D&D and it does not include negotiation. It is also remarkable that the time for reconfiguring each software module is approximately the same, around 615 ms.
## 3 SMTRPM Design
### Software System
#### 3.3.3 Virtual Patient File (Augmented reality)
Each variable is associated with a marker ID. Both markers are pasted on the patient's headboard. The MAR application requires the following stages. Initially, a camera calibration process is performed in which the fundamental parameters of the camera are determined. These parameters determine where a 3D point is projected onto a 2D point (_i.e._, a pixel). In an ideal camera, a 3D point \((X,Y,Z)\) in space will be projected onto a pixel according to Equation (2). \[x=X\frac{fx}{Z}+cx;\ \ y=Y\frac{fy}{Z}+cy \tag{2}\]  
where \((fx,fy)\) is the focal length of the camera lens in the two dimensions and \((cx,cy)\) represents the optical center of the sensor; both values are expressed in pixels. DroidAR uses a camera calibration method similar to that proposed in ARUCO [35]. After the camera calibration process has been performed, the system is ready to use. For example, when the nurse approaches a bed, he or she executes the application. Instantly, live video from the smartphone camera is acquired, and the nurse points to the markers. Then, the application performs the following computer vision algorithms. The images from the video stream are converted to binary images by a simple thresholding process described by Equation (3) [36]. \[g(i,j)= \begin{cases}0,&f(i,j)\leq P\\ 1,&f(i,j)>P\end{cases}  \tag{3}\]  
where the function \(f(i,j)\) is the input image (_i.e._, the brightness of the pixels), \(P\) is the threshold and \(g(i,j)\) is the processed image. If there is a marker in the scene, it is identified. Next, the positions and orientations of the markers relative to the camera coordinates are calculated. The symbol inside the marker is matched with templates, for which the only two possibilities are the marker for BT and the marker for HR. If a different figure is observed, it is discarded and the streaming continues. Finally, the position and orientation of the marker are computed. This is performed to align the virtual object with the marker. If the VPF detects the BT marker, a 2D thermometer image is superimposed onto the real scene in the position and orientation defined by the marker. In the case of the HR marker, a 2D heart image is superimposed. Additionally, there are two buttons (one at the top left and the other at the bottom left) in the application display used to request the status of a variable at any time. A touch event was programmed into the 2D images. Using this event, the nurse obtains the variable status in real time by touching its 2D image. Figure 12 shows the VPF application running. All the data obtained are stored in a database to maintain information about each patient. Because the VPF application was developed with a commercial license for DroidAR, there are some limits of the AR environment. For example, only five markers can be detected in the same scene, and the VPF application cannot handle occlusion.
# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 4 Experiments
### Qualitative Results  
Figure 5 shows a visualization of some typical qualitative results generated by our method and some other competitors. The weighted feature image frames used in BA by our E\({}^{2}\)-VINS on three test sequences from the VIODE dataset [42] (city_day, city_night, and parking_lot) and four sequences from the ECMD dataset [43] (Dense_street_day_easy_a, Dense_street_day_easy_b, Dense_street_night_easy_a, and Urban_road_day_easy_a) are shown on the upper side. In the figure, features with low weights (from dynamic objects) are plotted as red points, while features with high weights (from static objects) are shown as green ones. From Figure 5, it can be seen that E\({}^{2}\)-VINS effectively recognized dynamic objects, such as moving trucks, cars, buses, and pedestrians, and reduced their feature weights, even in relatively dim nighttime sequences. The trajectories of the compared algorithms and E\({}^{2}\)-VINS on the challenging sequences (Dense_street_day_medium_a and Dense_street_night_easy_a) of the ECMD dataset [43] is illustrated on the lower side. The former is a complex multidynamic object scene (a large number of large trucks waiting for red lights under overpasses), while the latter is a dim nighttime street scene. All other algorithms, except ours, either lost track or produced noisy trajectories in the dynamic and nighttime environments.
## 3 Results and Discussion
### Accuracy of the Segmentation Algorithm  
In order to measure the accuracy of the segmentation algorithm, the 8390 input images of the dataset were previously analyzed, obtaining 45,306 objects; 27,687 correspond to apples (61.1%) and 17,619 to the background (38.9%). The segmentation step was then applied to the images, comparing the obtained results with the ground truth. Table 4 shows the confusion matrix and accuracy achieved in identifying background objects and apples over the test set.  
Based on this table, it can be observed that 98.51% of all samples belonging to the apple class and 98.31% of the background objects were detected correctly, producing a total accuracy of 98.43% in the segmentation process. This is a very remarkable result, allowing a potential use of the algorithm in practice under real environment conditions. Although these results are slightly worse than those reported in [25], with a 99.12% accuracy, they are still better than other state-of-the-art methods, as applied in [34; 35], which achieved 92.5% and 95.72%, respectively, in segmentation of different agricultural products. Thus, this approach by itself can be applied to other problems of color segmentation for irrigation management, such as the study of crop canopy [36]. Moreover, since the purpose of the present research is to measure the overall ripeness state of the fruits, an error of only 1.5% false apples should not greatly affect the final result.
## 3 Experiments and Results
### Ablation Studies
#### 3.5.2 Policy Architecture  
We evaluated the contribution of the policy architecture introduced in our method (see Section 2.2.2) by comparing it to a policy with a simple neural network (NN) with two fully connected layers, as used in previous work [24]. We trained both policies on the cuboid-peg insertion task in the simulation and compared their learning performances. Figure 11 shows the learning curves of both policy architectures for a training session of 70,000 time steps. From the figure, is clear that, with our newly proposed TCN-based policy, the agent was able to learn faster and exploit better rewards. The TCN-based policy learned a successful policy (25,000) about 15,000 steps faster than the simple neural-network (NN)-based policy did (40,000). Additionally, the TCN-based policy converged to a higher cumulative reward than that of the simple NN-based policy.
## 5 Discussions
### A Working Example  
Without loss of generality, take the UAV trajectory as a curve. In the following, we take the scenario as presented in Figure 6, which has one UAV and multiple sensors, as an example. As shown in Figure 6, the data center is set at the original center point of the path, sensors are deployed in the front part of the predefined path and move along the path. The UAV takes off from the data center and flies to the given height \(h\), and then, flies along the path with a given speed \(v\) to collect data from sensors.  
The main energy consumption of such scenario is the energy consumption of the battery [7]. Thus, the network lifetime depends on the UAV flying time. We set this as 300 s in the simulation.  
We used periodic beacon mechanism to synchronize the network. The UAV sends a beacon message to its surrounding coverage to announce its coming. The sensors that received the beacon send a join message which contains the location and the speed to theUAV. After the reception of the join messages from the sensor nodes, the data collection phase starts. The interval between adjacent beacons is named as synchronization intervals, and denoted as \(T_{sync}\).  
Figure 8 presents the scalability and the data collection performance of the network in different synchronization intervals and network size. It shows that the shorter the \(T_{sync}\), the larger the \(N_{node}\). This is because the shorter \(T_{sync}\) brings larger number of nodes that are detected. Thus, the number of nodes that participate the communication increased. It is also able to enhance the fairness of the network when appropriate \(T_{sync}\) was selected. This is because the appropriate \(T_{sync}\) brings the communication opportunity of those nodes that will never be detected in other case.