esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions
### Technical Challenges  
One of the goals of the EC in practice is reducing computational complexity. When the blockchain is applied to EC, the required computational complexity further increases, owing to the PoW. If the difficulty level is high, the time spent on mining can be long, which can result in slow response of the EC. With this in mind, blockchained EC can improve the security and privacy of EC but at the cost of increased complexity, which is actually one of the critical issues for EC. Additionally, with competing miners in the blockchain, a duplicate uploading issue may occur. Specifically, if some of the competing miners finish their mining tasks within 2 ms, they would both upload to the corresponding participant without having time to terminate the other miner. There are several effective approaches to circumvent the above challenges. First, one very promising and effective solution to address the latency issue of blockchained EC is to lower the difficulty level for a much faster mining process than that of a higher difficulty level.  
Second, to solve the duplicate uploading issue, one can perform an additional process that can accurately identify the block. One possible approach for this is to verify the block that was created first and to delete the other block so that the correct block is added to the chain. There is a chance that the communication link between the device and the edge servers could be compromised, either through a malicious attack or some fault in the system. If this problem occurs, the device would not be able to send or receive data from the edge server. Using the blockchain when sending data is a proven good security measure where existing data cannot easily be edited and new data cannot easily be added to the chain. One possible solution to this problem is implementing a method that allows the device and edge server to hold onto the encrypted data until the communication link is back to normal and trust in the network is restored.
## 3 Results
### Question 6: What Is the Combined Effect of Individual IEQ Aspects on Overall IEQ Satisfaction?
#### 3.6.1 Linear Regression Analysis  
Multiple linear regression is the most widely used approach for establishing the relationship between the over-IEQ index (\(\theta_{IEQ}\)) and individual indexes. Most previous studies on IEQ model development have applied this method to determine the weightings of individual IEQ indexes [60]. A typical linear regression IEQ model that consists of four main IEQ aspects is shown in Equation (19) [13; 15; 54]:  
\[\theta_{IEQ}=\beta_{Ai}\theta_{Ai}+\beta_{Th}\theta_{Th}+\beta_{Ac}\theta_{Ac }+\beta_{VI}\theta_{Vi} \tag{19}\]  
where \(\theta_{Ai}\), \(\theta_{Th}\), \(\theta_{Ac}\), \(\theta_{Vi}\) represent the IAQ index, thermal comfort index, acoustic quality index, and visual quality index separately, and \(\beta_{Ai}\), \(\beta_{Th}\), \(\beta_{Ac}\), \(\beta_{VI}\) were their corresponding weightings, indicating the significance of each index to the overall IEQ performance.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis Benefit People Who Need to Travel for Business Purposes
#### Safety Features Flying Taxis Offer to Passengers  
Flying taxis are still in the development stage, so the specific safety features that they will offer to passengers are still being evaluated and determined. However, it is likely that flying taxis will be equipped with various safety features similar to those found on commercial airplanes, such as the following:  
* Advanced avionics systems to enhance navigation and avoid collisions.
* Autonomous emergency landing systems in case of mechanical failure or other emergencies.
* High-performance engines that are highly reliable and safe.
* Comprehensive and redundant onboard monitoring systems to ensure that all systems are functioning correctly.
* Air traffic management systems to enable safe operation in highly trafficked airspace.
* Advanced weather forecasting and monitoring systems to avoid hazardous conditions.
* Comprehensive maintenance protocols and inspections to ensure that the aircraft is safe to fly.  
Thus, safety will be a top priority for flying taxi manufacturers and operators, and they will be subject to strict safety regulations from the government [170].
## 3 Results and Discussion
### Investigating the Influence of SMA Pre-Strain  
Considering the pre-strain increment studied in this section determines the recovery stress by rising temperature, the effect of SMA pre-strain in the surface sheets for sandwich structures on natural frequency is demonstrated in Figure 10. Suppose that the volume fraction of graphite in the core layer was \(V_{g}=1\%\) and distributed in the form of \(p_{+}=1\). The representative volume fraction of the SMA volume fraction in the surface layer is 20% and 40%, respectively. A comparative study of the dimensionless frequencies was carried out, subject to the pre-strain values of 1%, 3%, and 5%.  
The results in Figure 10 illustrate the influence of the pre-strain of SMA on the dimensionless natural frequencies of a sandwich composite plate. As can be seen from Figure 10, neither the increment of the SMA volume fraction nor the increment of pre-strain had a significant effect on the natural frequency in the temperature range from 15 \({}^{\circ}\)C to 40 \({}^{\circ}\)C. In this temperature region, since the modulus of the SMA in the martensitic phase were about 25 MPa to 30 MPa, the increase in the volume fraction of the SMA from 20% to 40% hds a very weak influence on the stiffness. In addition, recovery stress generated by pre-strain was less than 80 MPa, which had no noteworthy effect on the stiffness factor  
Figure 10: A comparison of dimensionless natural frequencies between the SMA volume fraction of 20% and 40% subjected to pre-strain 1%, 3% and 5%.  
Figure 9: Dimensionless natural frequencies vs. the SMA volume fraction subjected to different temperatures.  
\(K_{33}\). As the temperature rises above 40 \({}^{\circ}\)C, the natural frequency increases with increasing SMA volume fraction and pre-strain. Simultaneously, the critical buckling temperature could be enhanced by pre-strain increment. This is due to the martensitic phase of SMA gradually transforming to the austenitic phase during the increase of temperature, so the recovery stress generated by pre-strain increases, and the increase of volume fraction of SMA enhances the in-plane force effect, thus increasing the overall stiffness of the sandwich composite plate.
# Concept of Virtual Reality Training of Facility Management Employees Using Eye Tracking
## 4 Research and Dataset Analysis
### Dataset  
The research was conducted on a group of 100 employees. The number of training participants was selected based on the number of people participating in similar research conducted in the VR environment and described in the articles cited in the Introduction section--usually 60-90 people [14; 19; 30; 32]. The number of people who completed all three VR scenarios and took three exams was 91. The obtained results from three exams were averaged for each VR scenario. In the learning VR scenario, participants completed the test only after a positive completion of each task. In the case of incorrect task performance, they received information on stage that the error occurred, and the participant had to repeat the task until they passed it. This approach aimed to teach the participant to perform the task correctly at each stage because skipping some stages will result in a lack of skills in operating the device, and thus may cause damage to the device or even injury to the person operating it in the actual operation of the device. A user-assistance system was used to guide and supervise participants during training through a tablet available in a VR environment (Figure 5). This approach differed from the typical approach in participant training. In addition, separating the learning date from the examination date allows the study of the influence of learning on long-term memory.  
\begin{table}
\begin{tabular}{c c c} \hline
**Id** & **Number of Tests** & **Focus Success Rate** \\ \hline
1 & 11 & 72.73\% \\
2 & 10 & 100.00\% \\
3 & 10 & 90.00\% \\
4 & 10 & 90.00\% \\
5 & 12 & 83.33\% \\
6 & 10 & 100.00\% \\
7 & 15 & 80.00\% \\
8 & 10 & 80.00\% \\
9 & 10 & 100.00\% \\
10 & 11 & 90.91\% \\
11 & 10 & 90.00\% \\
12 & 10 & 100.00\% \\ \hline \end{tabular}
\end{table}
Table 1: Eye-tracking functionality test success rate for each participant.  
Figure 5: _Cont_.  
Surveys accompanied the VR research on basic information and information on employment and experience in the FM industry. The group was divided into two subgroups based on their experience in facility management. The first group of employees had practical knowledge gained during work, and the second group acquired the required skills only through VR training. Each employee participated in all three VR training and evaluation modules. An expert evaluated the results of the skills tests considering the results of eye movement. A large group of training participants passed and failed the tests (Table 2).
# Development of a Magnetorheological Damper with Self-Powered Ability for Washing Machines
## 5 Experimental Validation
### Performance of the Energy-Harvesting Part  
From Equations (6)-(9), it is shown that the induced voltages of two contiguous coils express the properties of the EH part. To validate the efficiency of the power generation, a harmonic excitation of 2.4 Hz frequency and 30 mm amplitude is applied to the prototype self-powered MR damper. This is the frequency when the first resonance often appears in most commercial washing machines. Figure 13 presents the comparisons between the theoretical modeling and experimental measurement of the induced voltages of coils 1 and 2. As shown in the figure, it is observed that the experimentally measured induced voltages are slightly lower than those obtained from theoretical analysis. This mostly results from the measurement noises and magnetic flux leakage. The average value of the produced voltages of coil 1 and coil 2 are about 93.7% and 93.8% of the simulated value, respectively. Thus, the experimental data matches well with the simulation results. Moreover, numbers of peaks are found in one excitation period of the generated voltages, due to the effect of the induced voltage frequency multiplication [17]. This would increase the overall harvested power and thus is very advantageous to the EH part.  
Figure 11: Prototype of the optimized self-powered MR damper (**a**) and its disintegrated components (**b**).  
Figure 12: Experimental setup for testing the performance of the prototype self-powered MR damper.  
Figure 14 shows the comparison between the numerical analysis and experimental results of the generated power with respect to the velocity excitation. As shown in the figure, the produced power almost exhibits a parabolic relation to the velocity excitation. The average error between the theoretical and experimental generated powers is about 8.4%. As a result, the experimental data of the generated power matches well with the simulation results.
## 5 Optimization Techniques for Routing in Wireless Sensor Networks
### Data Aggregation  
Data collected in sensors are derived from common phenomena so nodes in a close area usually share similar information. A way to reduce energy consumption is data aggregation. Aggregation consists of suppressing redundancy in different data messages. When the suppression is achieved by some signal processing techniques, this operation is called data fusion.
## 3 Results and Discussion
### Thermal Performance of BTMS
#### 3.1.2 Effects of Air Flow Rate on Temperature Difference  
Investigating the performance of the battery pack for temperature difference between cells, the average temperature of cells in each row (one through four) is determined for all the individual unique experiments. This measure taken reduces the measured temperature output from sixteen cells to four cells classified by their positions in the battery pack (see Figure 14, page 11 above). After classification of all sixteen cells into four rows, the maximum temperature in each row is determined. Lastly, the absolute difference in T\({}_{\text{MAX}}\) between all the combinations of rows is presented in Figures 17 and 18.  
In Figure 17, it is observed that for the lower current rate of 0.5 C tested, the majority of temperature differences between rows measured in the battery pack is below 5 \({}^{\circ}\)C. The maximum temperature difference measured during experiments conducted under the 0.5 C current rate was recorded to be 8 \({}^{\circ}\)C during a charge cycle under 1.4 m/s and 6.37 \({}^{\circ}\)C for a discharge cycle under the same flow rate which occurred between cells in Row 1 and Row 4.  
For flow rates of 2.4 and 3.4 m/s tests under 0.5 C, the temperature difference between all interacting rows in the BP was kept well below 5 \({}^{\circ}\)C.  
Figure 16: Effects of air increasing air flow rate on maximum temperature.  
In Figure 18, which plots the cell temperature difference for experiments conducted under a 0.75 C current rate, the temperature differences between Row 1 and Row 2, Row 1 and Row 3, Row 1 and Row 4 and Row 2 and Row 4 were observed to be above the 5 \({}^{\circ}\)C threshold of under air flow rate of 1.4 m/s during the charge and discharge experiments.  
As the air flow rate increases to 2.4 m/s, the temperature difference which occurred under the charge cycle is reduced from three to two interactions (Rows 1 and 3, Rows 1 and 4) similar to results obtained for the 0.5 C charge experiment under 1.4 m/s.  
The maximum temperature differences between Rows 1 and 4 during the charge and discharge cycle under 0.75 C were 13.81 \({}^{\circ}\)C and 12.42 \({}^{\circ}\)C respectively for 1.4 m/s flow rate. These values reduced to 6.0 \({}^{\circ}\)C and 5.25 \({}^{\circ}\)C under an air flow rate of 3.4 m/s.  
Figure 17: Effects of air flow rate on temperature differences (0.5 C).  
Figure 18: Effects of air flow rate on temperature differences (0.75 C).
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
## 6 Performance Evaluation  
In this section, we evaluate the performance of the HT-MAC scheme and compare it with those of other protocols. With Equations (20)-(22) and (24)-(27), the theoretical mean queue length, mean cycle time, and mean upper bound of the frame delay are obtained for the HT-MAC scheme. A computer simulation model of HT-MAC based on Network Simulator-2 (NS-2) was established to verify the theoretical results and evaluate scheme performance. NS-2 is an object-oriented discrete event simulator that covers a large number of network types, elements, protocols, applications, traffic models, and energy models. HT-MAC performance was evaluated in two parts: clustering performance and data transmission performance.  
In the simulations, we deployed \(N\) = 100 ordinary nodes and \(M\) = 10 superior nodes that were randomly distributed in a 500 m \(\times\) 500 m field. Ordinary nodes consisted of picture- and sound-monitoring nodes, and these two types of nodes each accounted for 50% of the total number of ordinary nodes. Without loss of generality, we assume that the base station is located at the center of the sensing field. In addition, the link rate was 11 Mb/s, the token frame length was 8 bytes, and for the superior and ordinary nodes, the mean data frame length was 256 bytes.
## 5 The Proposed WHT-LWT GFDM Waveform
### Computational Complexity Analysis  
Computational complexity must be known at each iteration time to examine the advantages and disadvantages of different communication systems against each other. In this study, computational complexity is defined as the number of multiplications and additions taken to process the data. Each number of additions and multiplications are calculated separately.  
In the proposed study, WHT is used. To calculate the WHT conversion, a total of \((K\log_{2}K)\) real multiplication operations on the transmitter side and \((K\log_{2}K)+K\) real multiplication operations are required on the receiver side. Here, \(K\) is the sub-carriers. To calculate the LWT transformation, a total of \(K(P+U)/2\) real multiplications and \(K(P+U)/2\) real addition operations on the transmitter side and \(K(P+U)/2\) real multiplications and \(K(P+U)/2\) real addition operations are required on the receiver side. Here, \(P\) is the length of prediction coefficients sequence given by Equation (17) and \(U\) is the length of update coefficients sequence given by Equation (19). Therefore, the computational complexity of the proposed method varies according to the length of the filter coefficients (mother wavelets such as Haar, lazy and bior) used in the Update and Predict stages. If these calculations are adapted to the GFDM waveform, the computational complexity of the proposed method can be obtained as in Table 1. In Table 1, \(M\) shows the sub-symbols and \(L\) indicates the span of receiver filter in the neighborhood of each subcarrier band [8]. The number of addition operations are about the same number of multiplication operations.  
Figure 4: The effects of different roll-off values on the BER performance of GFDM waveform in AWGN channel.
## 2 Observation Systems and their Integration
### Example Forest Fire Observation Systems
Further, we adhere to OGC standard service interfaces, such as Web Map Service (WMS) offering visualization functionality for geospatial content [24], Web Feature Service (WFS) providing querying and access capabilities for geospatial objects [25], and Web Processing Service (WPS), as the common OGC mechanism for encapsulating geospatial algorithms and simulations as a service [26]. Approaches relying on remote sensing imagery would follow a similar pattern. Comparable to the workflow used in the European Forest Fire Information System (EFFIS) [27], a satellite's sensors measure electromagnetic waves, generated either passively as reflected or emitted natural radiation (e.g., Infra-Red), or actively as emitted and reflected energy (e.g., Radar). These signals are usually stored as grid cells (or pixels of an image). Subsequent processing steps include the removal of artifacts, geo-referencing, and the addition of meta-data, similar to the calculation that the CONAVI Analyzed performs. Further analysis steps commonly derive a geophysical variable from the original raw data, e.g., the temperature at a location, similar to an integrated quality score for each sensation from the Analyzer. Similar to the CONAVI Clusterer module, a collection of pixels and images can be analyzed for clusters of significantly higher temperatures, resulting in the detection of hot spots that might be caused by forest fires. Further cross-validation with other data, such as land cover or risk indices, might lead to a classification of the hotspot as forest fire, with dissemination of this information to relevant authorities and issuance of alerts. We will use these two examples for illustration in the remainder of this paper. For the EFFIS related case, it should be noted that--for reasons of simplicity--we describe an abstracted system that is close to, but not identical with the current EFFIS implementation.
## 4 D-Leach  
In this section, we discuss in detail the proposed D-LEACH to improve the energy efficiency of WSNs. D-LEACH is an improved LEACH protocol that reduces unnecessary energy consumption caused by data transmission in the reverse direction relative to the BS. The sensor node of D-LEACH calculates the relative distance using the strength of the signal and determines the transmission  
Figure 5: Steady-state phase of LEACH.  
Figure 6: Steady-State phase of a multi-hop LEACH (MH-LEACH).  
direction. The BS sends signals to each sensor node. Each sensor node calculates the relative distance to the BS from the strength of the signal. The sensor nodes then determine the direction of transmission by comparing the relative distances of adjacent nodes. D-LEACH uses direct transmission, single-hop transmission, and multi-hop transmission.  
The D-LEACH algorithm has four phases.  
Step 1. Layer configuration phase.  
The BS collects the relative distance information for each sensor node in the WSNs region.  
The WSN consist of layers according to the relative distance of each sensor node.  
Step 2. CH probability calculation for the layer.  
The selection probability of each sensor node for the CH depends on its layer.  
Step 3. Set-up phase.  
In this step, the cluster is configured. The sensor node is selected as the CH using the probability. Sensor nodes not selected as the CH become member nodes of the CH by comparing the relative distance. A sensor node that fails to select a CH that meets the conditions becomes a normal node. Clustering consists of a CH and member nodes.  
Step 4. Steady-state phase.  
In this step, data are transmitted from the sensor node and from the CH to the BS. The member nodes send data to the CH. The CH merges and compresses the data and sends them to the upper CH near the BS. Data are sent to the BS via the upper CH. The normal node transmits directly to the BS.  
Steps 1 and 2 are performed once in the early stages of WSNs, and Steps 3 and 4 are repeated at regular intervals until all sensor nodes die. Figure 7 shows the steps of the D-LEACH. We explain each step in detail.  
Figure 7: Steps of Direction-based LEACH (D-LEACH).