esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurements and Calculations  
To compare deviations in three-dimensions in a full arch versus a sectioned arch, a mesh deviation of the full arch was calculated, followed by the average absolute value of deviations, maximum  
Figure 4: Illustration of superimposed models and division sequence using Geomagic VerifyTM. (**A**) Scanned data from the reference scanner with the Severe model; (**B**) Scanned data from iTero® with the Severe model; (**C**) Superimposed model using the best-fit algorithm between (**A**) and (**B**); (**D**) A reference plane set with resin-balls for cutting the base of the superimposed model; (**E**) Finished product of a superimposed model after cutting the base; (**F**) Colored representation showing deviations after superimposition. Red represents areas of high deviation (around 200 \(\upmu\)m). Blue represents areas of low deviation (around 0 \(\upmu\)m).  
Figure 5: Superimposed model divided into five Sections. LM: left molars, LP: left premolars, Ant: anterior, RP: right premolars, RM: right molars.  
deviation, and standard deviation. For the sectioned arch, the superimposed data was divided into five sections (LM, LP, Ant, RP, RM). Then, the average absolute value of deviations, maximum deviation, and standard deviation were calculated.  
Two lines were drawn to calculate intermolar widths. The first dividing line passed through the resin-ball points of the bilateral first molars to form a model that included the mesial surface of the left first molar to that of the right first molar. The second dividing line passed through the highest contour points of bilateral first molars parallel to the occlusal plane. Then, the resultant two edge points were measured (Figure 6). The intermolar width of the reference image and that of the scanned image were measured three times, and the average values were calculated.  
The same measurements were made a week after the first trial to evaluate the reliability of intermolar width measurements. The first measurements were named Group 1 and the second measurements, Group 2. A \(t\)-test revealed the difference between the two groups to be statistically insignificant (iTero(r) \(p\) = 0.97, Trios(r) \(p\) = 0.95). The first set of measurements, Group 1, was used to record the deviation.
## 3 Results and Discussion
### Strength Degradation Coefficient  
The strength degradation coefficient [31] was used to measure the degradation of the bearing capacity of the specimens in the same loading stage, and its expression is as follows:  
\[\lambda_{i}=\frac{F_{j}^{i}}{F_{j}^{i-1}} \tag{3}\]  
where \(F_{j}^{i}\) is the peak load of the i cycle at the j loading stage, and \(F_{j}^{i-1}\) is the peak load of the i - 1 cycle at the j loading stage.  
Figure 9 shows the strength degradation of each specimen at different loading levels. It can be seen that the strength degradation coefficient of each specimen was similar, and  
the strength degradation coefficient of the L-shaped shear wall under positive loading was basically larger than that in the negative direction, that is, the strength degradation in the negative direction was more serious than that in the positive direction. This was mainly due to the fact that the concrete damage of the straight-edge members of the specimen was more serious than that of the L-shaped-edge members. In general, the strength degradation coefficient at the third cycle of the peak load of each specimen was greater than that at the second cycle, indicating that the strength degradation tended to slow down with the increase of the number of cycles.  
It can be seen from Figure 9b that there were values with a degradation higher than 1. Based on the experimental phenomenon, at the loading displacement of 20 mm, the longitudinal reinforcement in the straight-edge member of the specimen has yielded. In the process of repeated loading, the reinforcement may undergo strain hardening, which will lead to the increase of the strength of the reinforcement. Therefore, the strength degradation coefficient of the specimen may be greater than 1, which indicated that the strength of the specimen was basically not degraded.
## 3 Methods
### Data Preprocessing
#### 3.1.2 Data Cleaning  
The original data were reviewed in different dimensions for cleaning in preparation for target analysis. The different dimensions applied to the data cleaning steps are explained as follows:  
**Charging information** The electric bus has a two-stage starter. In the first state, the power is on, but the motor is idle, and the motor temperature is preset to zero. In this case, the vehicle cannot be driven but only allows the signal handshake between the vehicle controller and the charger during charging. In the second stage, the motor is started, and the bus can be driven. This study focused on analyzing the driving behaviors that influence energy consumption as the vehicle is driven. This required that the charging state data be cleared.  
**Time interval** The BMS returns vehicle driving data every 10 s. However, the transmission of data is delayed due to the mobile network, creating a greater than 10 s interval in data transmissions. To maintain the consistency of the data trends, the data were cleared when received after a delay of more than 10 s.  
A total of 1,145,053 original records were collected during vehicle starts, among which there were 308,826 charging data records that accounted for about 27% of the total original data. After the charging data were cleared, the total number of driving data records was 836,227, among which 75,249 records had a time interval of more than 10 s (accounting for approximately 9% of the driving data). After data cleaning, a total of 760,978 data records remained for analysis. Accordingly, in total, we removed 33.5% meaningless records from the original records (\((308,826+75,249)/1,145,053)\).
# Field Trial and Performance Evaluation of Soybean-Based Bio-Fog Seals for Asphalt Rejuvenation
## 3 Results
### Stiffness and Relaxation  
The results in Figure 14 show the influence of bio-fog rejuvenators on asphalt mixture stiffness and relaxation behavior in the MnROAD 58 test sections. Compared to the control  
Figure 12: Horizontal pavement markings’ reflectivity before and after treatment.  
Figure 13: Field coefficient of permeability.  
section, the BioMAG treatment in the MnROAD 58-28S section resulted in a statistically significant decrease in mixture stiffness, and the SESO showed an increase in relaxation, as indicated by [PERSON]'s HSD mean comparison at a 95% confidence level. On the other hand, no notable difference was observed with the 58-34H treatment, likely due to the binder's lower stiffness compared to -28S.  
As asphalt binders age, their stress relaxation capabilities at low temperatures decrease, and stiffness increases. Therefore, the observed decrease in stiffness with BioMAG and the increase in m-value with SESO suggest potential improvements in the anti-cracking properties of the mixtures in the MnROAD 58-28S section. These findings are promising as they indicate that bio-fog seals might act beyond simple sealing and contribute to the rejuvenation of the binder's rheological properties, potentially enhancing overall pavement performance.  
Despite the potential of bio-fog seals to enhance binder performance, their observed impact was less than expected. This might indicate that an increase in the application rate of bio-fog seals may yield more substantial enhancement.
# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
## 4 Data Set Preparation
### No. of Line Segment of Combined Stripes Contour (\(N_{l}\))  
In 1973, the Douglas-Peucker algorithm developed by [PERSON] and [PERSON] splits a curve into line segments depending upon the specified precision (\(\epsilon\)). It determines the dominant point on the curve by digitizing the original curve and generates a similar curve of fewer points [34]. We have used this algorithm to relate the number of line segments with the no. of stripes. It is noticeable that, with the variation of LED on-off frequency and distance, getting accurate contour of the object is difficult. Thus, errors may be present in determining the number of stripes. Combining all stripes, we segmented  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline LED diameter & 10 mm \\ Camera exposure time & 15.6 ms, 3.90 ms, and 976.6 \(\upmu\)s \\ Camera frame rate & 30 fps \\ Camera image resolution & \(1280\times 720\) \\ Mark and space frequency & 4000 kHz and 2000 kHz \\ Learning rate & 0.001 \\ No. of epoch & 30 \\ Kernels & RBF, linear, polynomial, and sigmoid \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the implementation of the proposed model.  
the combined contour using the Douglas-Peucker algorithm based on the corner point of each stripe.  
The scatter plot of the selected features is shown in Figure 7. In the figures, the distribution of features shows a clear distinction among data transmitting LED and unwanted LED which affirms our motive to integrate an SVM classifier in the conventional receiver system.  
Figure 6: Example of a single LED image frame where all the generated stripes are combined together and a contour is formed.  
Figure 7: Scatter plot of selected features (**a**) area vs. no. of stripes per LED region; (**b**) area vs. perimeter of the combined stripes contour; and (**c**) area vs. no. of the line segment of the combined stripes contour.
## 1 Introduction
Methods for obtaining cost and sizing equilibrium of reinforced concrete retaining walls have been the main objective of many studies over the years and continue to be relevant due to the developments in information and computer technologies. Metaheuristic algorithms such as the genetic algorithm (GA) [12; 26; 27; 28], big bang big crunch (BBBC) [29; 30], particle swarm optimization (PSO) [31; 32], firefly algorithm (FA) [33; 34], harmony search (HS) algorithm [35; 36], and colony optimization (ACO), bat algorithm (BA) [37], charged system search (CSS) [38], simulated annealing (SA) [39; 40; 41], biogeography-based optimization algorithm (BBO) [42], teaching learning-based optimization (TLBO) [43], java algorithm (JA) [44], and flower pollination algorithm (FPA) [45] can be counted as the applied new methods for the optimization process of retaining wall systems. In the present study, sizing optimization via cost control process is taken into account for RC cantilever retaining walls with the use of the HS provided with MATLAB software. It is the aim to investigate the effects and influence rates of different soil properties, excavation depths, loading conditions, and material cost change on the dimensional requirements of supporting systems due to the cost optimization. Not only have a huge number of (105,000) analyses been performed to control the optimum dimensions of the wall system, but also evaluations have been made to obtain the relationships between the variants via dimensions and costs. Besides, the geotechnical software Geo5 which is working with the combined logic of both theoretical [46] analytical methods and finite element method [47], has been used to verify the stability conditions for the suggested cross sections by the performance of the optimization process. At last, the original novelty of this study compared with the related literature can be listed as follows:  
* The most important parameters that can be effective for both safety and cost have been handled unprecedentedly by using a very wide parameter network for the design of RC-retaining walls. Both individual and dual effects of influencer design parameters are investigated. * Results of the optimization analyses are compared by interpretations against the traditional pre-design method. * Verification of the suggested solution method is conducted with a well-known geotechnical design software and the practicality of the usage of the HS algorithm is emphasized in terms of geotechnical engineering.
# Enhanced Scalability and Security in Blockchain-Based Transportation Systems for Mass Gatherings
## 4 Results and Findings
### Comparing the Cost of the Proposed System with Other References
Where the requirement for scalability is not excessive, our Baseline model offers sufficient throughput without the need for expensive infrastructure. As far as latency (s) is concerned, Rollups lead with the presentation of the lowest latency numbers, between 1.5 and 2.3 s, and after that come State Channels with latency ranging from 2 to 3.3 s. Nonetheless, the Baseline model is a viable solution to a host of applications not necessitated by ultra-low latency despite latency values of 5-6 s. The main thing that makes the Baseline rather unique is its cost-to-performance ratio: higher latency is traded off against a significantly reduced price. In the case of less time-constrained activities or domains that do not require high throughput, the performance of the Baseline model would be sufficient as a more economical alternative, without compromising the overall system's functionality. That makes it the clear winner in terms of cost-effectiveness, with adequate performance in many scenarios. Figure 8: Comparison of costs for scenarios such as baseline, State Channels, and Rollups with varying reference numbers.
## 4 Methodology and Data
### Econometric Models
#### 4.2.3 Variables and Data  
The dependent variable of this study is the CO\({}_{2}\) emissions (CO\({}_{2}\)) of port cities along the Yangtze River. The data on CO\({}_{2}\) emissions for port cities along the Yangtze River are derived from the CEADs database by [PERSON] et al. [59], considered the most comprehensive dataset for estimating carbon emissions at the urban level in China [60]. Following the approach of [PERSON] et al. [61], we extract the CO\({}_{2}\) emissions data for port cities along the Yangtze River from the CEADs database and take the logarithm to represent the level of CO\({}_{2}\) emissions of port cities along the Yangtze River.  
The core explanatory variable is port TFP (\(TFP\)). To calculate TFP, we follow the method proposed by [PERSON] and [PERSON] [3], initially using the global Malmquist index to estimate the TFP growth rate of each port from 2009 to 2019. Subsequently, taking the total factor productivity of 2009 as the base value (set to 1), we accumulate the TFP growth rates over this period to obtain the TFP index for each port.  
Via the STIRPAT model, we have selected the following variables as control variables: (1) technological progress (\(T\)): the natural logarithm of the number of patent inventions, which reflects the impact of technological advancement [62]. (2) Population (\(P\)): The natural logarithm of the population size. Population is considered a key factor influencing urban CO\({}_{2}\) emissions. (3) Affluence (\(A\)): we measure affluence using the natural logarithm of per capita GDP, which is another key factor affecting urban CO\({}_{2}\) emissions [63].  
Table 2 presents the descriptive statistics of the variables. Apart from the port TFP data, which the authors calculated, all other city-level data were obtained from the China City Statistical Yearbook, the China Statistical Yearbook for Regional Economy, and the statistical yearbooks of various port cities.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 4 Challenges of PV-DT
### Cybersecurity  
As the energy sector embraces digital transformation, the intersection of cybersecurity, solar energy, and digital twins (DT) is becoming increasingly important. While a DT offers significant benefits to enhance the efficiency and economic viability of energy systems, it simultaneously exposes the system to potential cyberattacks. The more energy plants that are digitalized, the more vulnerable cyber nodes will be created.  
However, the digital twins' architecture can be designed to include cybersecurity monitoring capabilities. Classical scenarios were investigated in [132; 133], where DTs were implemented to make it easier to detect cyber-attacks. This is not to say cyber-attacks were non-existent in conventional systems; they are only a wake-up call for more cyber-resilience. The authors of [134; 135; 136] proposed a framework incorporating a digital twin for cybersecurity monitoring in smart grids, demonstrating how such systems can enhance the resilience of energy infrastructures against cyber threats. This approach aligns with the findings of [10], which discussed the potential for digital twins to facilitate data sharing and control of security-critical processes in industrial settings. By leveraging machine learning and advanced analytics, digital twins can detect anomalies and potential security breaches in real-time, thereby safeguarding solar energy systems from cyber threats. The intersection of cybersecurity, solar energy, and digital twins creates a new set of opportunities and challenges for stakeholders in the energy industry to improve performance and operational effectiveness, while effectively managing the significant cybersecurity concerns that come with digital transformation.
## 3 Analysis of the Individual Factors Affecting the Stability of the Skid-and-Mud-Return Line
### Effect Analysis of Reverse Torque and Vibration on Mud Pumps  
At the rated voltage, six sets of data corresponding to reverse torsion and amplitudes at speeds of 0, 300, 600, 900, 1200, and 1500 rpm in Table 2 are selected as the analysis data, as shown in Table 12. They are used to analyze the effects of reverse torque and vibration on the stability of the skid-and-mud-return line. The analysis results are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rotational Speed (rpm)** & **Reverse Torque (N-m)** & **Amplitude (mm)** \\ \hline
0 & \(-\)320.4 & 0.76665 \\
300 & 228.27 & 0.9315 \\
600 & 667.98 & 1.1281 \\
900 & 1332.79 & 1.2728 \\
1200 & 2327.27 & 1.4643 \\
1500 & 3602.80 & 0.0977 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Reverse torque and vibration corresponding to different speeds at the rated voltage.  
Figure 8: Effects of sea current disturbances on the stability of the skid-and-mud-return line in different recurrence periods: (**a**) equivalent stress, (**b**) displacement, (**c**) rotation angle, and (**d**) bending moment.  
The analysis results indicate that the stability of the skid-and-mud-return line is most affected by reverse torque and vibration at a speed of 1200 rpm. Due to the effect of reverse torque and vibrations on the mud pump, significant changes in various stability parameters of the skid-and-mud-return line take place at a depth of 1300 m. The stress in the skid-and-mud-return line increases relatively steadily from a depth of 0 m to 1300 m. It decreases sharply at 1300 m and then gradually decreases with an increase in the depth of the mud return pipeline. The displacement of the skid-and-mud-return line is relatively stable overall. But it suddenly increases at 1300 m and then rapidly decreases. The rotation angle of the skid-and-mud-return line increases with the depth from 0 m to 1300 m and then remains stable after 1300 m. The bending moment of the skid-and-mud-return line is relatively stable from 0 to 1300 m, decreases sharply around 1300 m, and then remains stable. The maximum equivalent stress is 1.48 MPa, 1.05 MPa, 3.08 MPa, 6.15 MPa, 10.74 MPa, and 0.06 MPa, as shown in Figure 9a. The maximum displacement is 0.77 mm, 0.93 mm, 1.13 mm, 1.27 mm, 1.46 mm, and 0.0005 mm, as shown in Figure 9b. The maximum rotation angle is 0.2 rad, 0.14 rad, 0.41 rad, 0.82 rad, 1.43 rad, and 0.07 rad, as shown in Figure 9c. The maximum bending moment is 0.29 kN\(\cdot\)m, 0.21 kN\(\cdot\)m, 0.60 kN\(\cdot\)m, 1.20 kN\(\cdot\)m, 2.10 kN\(\cdot\)m, and 0.01 kN\(\cdot\)m, as shown in Figure 9d. It can be observed that reverse torque and vibration mainly affect the rotation angle of the skid-and-mud-return line. The effects from the equivalent stress, displacement, and bending moment of the skid-and-mud-return line are relatively minor compared with other factors.  
Figure 9: Effects of reverse torque and vibration on the stability of the skid-and-mud-return line: (**a**) equivalent stress, (**b**) displacement, (**c**) rotation angle, and (**d**) bending moment.
## 2 Experimental Section
### GC-MS Condition for Carbonyls Analysis  
The SPME fiber was introduced to the injection port of the GC instrument for thermal desorption after on-fiber derivatization. An Agilent 6890N GC system was equipped with Agilent 5975C VL MSD. Samples were separated with a 60 m \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m SGE BP-21 column. The GC injection port temperature was 250 \({}^{\circ}\)C. The oven temperature was raised from 120 \({}^{\circ}\)C to 180 \({}^{\circ}\)C at a 4.5 \({}^{\circ}\)C/min ramp, and then to 220 \({}^{\circ}\)C ramped at 15 \({}^{\circ}\)C/min, holding for 3 min. Helium was the carrier gas and the flow rate was 1.7 mL/min. The scanning range of the MS detector was _m/z_ = 33 to 352. Temperatures of the MS source and MS quadrupole were 230 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. A selective ion monitoring (SIM) mode was run simultaneously with total scanning mode for quantification. The following ions were used for quantification for ethanal, hexanal, 2-furfurral, and benzaldehyde: 209, 239, 291 and 301, respectively. The full list of SIM ions of carbonyl-PFBHA derivatives is listed in a previous study of the ozonation of colorants [5].
## 5 Experimental Validation and Benchmarks
### Quality of Service (QoS) Management  
AI plays an essential role in managing Quality of Service (QoS) in communication networks by ensuring that service priorities are maintained and congestion is minimized [128]. QoS management is critical in networks where various applications, such as voice, video, and data services, have differing bandwidth, latency, and reliability requirements. AI models help optimize the distribution of network resources to meet the specific demands of these applications, ensuring that high-priority traffic, such as real-time communication or critical business services, is given preferential treatment over less time-sensitive data [129].  
Machine learning algorithms can dynamically analyze network traffic in real-time to detect congestion, packet loss, and latency issues. By continuously monitoring network performance, AI can predict potential bottlenecks and adjust resource allocation proactively, ensuring smooth network operation even during peak usage times. For example, AI can prioritize traffic flows based on application needs, adjusting routing paths to reduce latency for voice or video calls while ensuring data-heavy applications receive adequate bandwidth without overwhelming the network [127].  
In addition to proactive traffic management, AI-driven QoS systems can adapt to changing network conditions and user demands. By learning from past network behavior, AI can fine-tune QoS policies over time, improving the accuracy and efficiency of resource allocation. These systems are capable of adjusting parameters such as traffic shaping, load balancing, and congestion control automatically, reducing the need for manual intervention and improving overall network performance [129].  
AI also plays a significant role in multi-user environments, where managing QoS for a diverse set of users and applications is particularly challenging. AI can implement fairness algorithms that ensure equitable resource distribution among users while meeting the QoS requirements of each application. This approach is particularly important in 5G and next-generation networks, where multiple devices and services compete for limited resources [130].  
By integrating AI with QoS management, communication networks can achieve enhanced performance, reduced latency, and improved user experience, making them more efficient and reliable in delivering high-quality services to users.