esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application of Uncertainty Quantification Methods and Results
### Results and Discussion of Uncertainty Quantification
#### 3.3.1 Analysis of Quantified Uncertainty
This result shows that, similar to the tendency of ME, uncertainty decreased in the post-processing stage in the radar-based precipitation estimation, although it increased in comparison with the initial uncertainty. The final uncertainty also decreased compared to the initial uncertainty. It can be determined that the M-P relation was the major contributor to the increasing uncertainty in the precipitation estimation stage, because the UDM value of the M-P relation was larger than that of the WPMM. Table 3c and Figure 5c show that the M-FUM values of the ORPG and fuzzy algorithms in quality control were 0.45 and 0.39, respectively, the M-FUM values of the WPMM and M-P relations in precipitation estimation were estimated at 0.44 and 0.50, respectively, and the M-FUM values of the LGC and G/R ratio methods in the post-processing were 0.33 and 0.36, respectively. Although the final uncertainty (0.36) was smaller than the initial uncertainty (=0.45), the uncertainty in the middle stage of the entire process increased in comparison with the initial uncertainty. Assuming that the final uncertainty was 100%, as in the previous methods, the uncertainties of the quality control stage and the radar-based precipitation estimation stage were 126% and 140%, respectively. Hence, although the uncertainty of the subsequent stage increased in comparison with the initial uncertainty, the uncertainty then decreased in the post-processing stage. This change in uncertainty was identical to that of the MEM and UDM.
# Fatigue Performance Test and Numerical Analysis of Composite Girders with CSW-CFST Truss Chords
## 3 Analysis of Fatigue Performance Results
### Fatigue Failure Process
As can be seen in Figure 14b, the propagation rates along the fatigue crack length direction in the composite girder with CSW-CFST and CSW-ST truss chords were 3.81 mm/100,000 cycles and 7.60 mm/100,000 cycles during the fatigue crack propagation stage, respectively, and those at the fatigue failure stage were 35.59 mm/100,000 cycles and 112.38 mm/100,000 cycles, respectively. Therefore, it can be concluded that, as the fatigue crack transitioned from the propagation stage into the failure stage, although the propagation rates along the length direction of both the composite girder with CSW-CFST and CSW-ST truss chords were clearly elevated, the latter was still much higher than the former. In sum, concrete in the chord not only provided radial constraint for the chord steel, but also effectively lowered the neutral axis of the composite girder cross-section, reduced the axial stress in the chords, further reduced the hot-spot stress in the intersecting weld zone, and slowed the crack propagation rates along the depth and length directions in the CSW-CFST truss chords compared to those of CSW-ST truss chords during the fatigue crack propagation failure stages. The latter was also an important reason why the fatigue life of the composite girder with CSW-CFST truss chords was longer than that of the composite girder with CSW-ST truss chords chords. According to the results obtained by the non-destructive examination of the initial defect, no initial defect appeared in the structure; thus, the fatigue crack did not start from an internal defect. The fatigue failure mode of the composite girders with CSW-CFST truss chords are shown in Figure 15. The fatigue crack developed at the intersection with  
Figure 14: Comparison of fatigue crack propagation rates: (**a**) propagation depth-loading cycles curves; and (**b**) propagation length-loading cycles curves. the maximum hot-spot stress, and the fatigue fracture surfaces were flush and essentially parallel to the chord cross-section. Moreover, the failure modes were generally consistent with those of the composite girder with CSW-ST truss chords reported in [11], indicating that the fatigue failure modes and fatigue crack types of the composite girder with CSW-ST truss chords were not changed by concrete-filling the chord.
## 3 Results and Discussion
### Comparisons of the Different Networks  
We then compared the predictions of the MLP and LSTM networks under two new constraint conditions, as shown in Figure 7. The geological and fracturing reservoir parameters of the three cases are as follows: in case 1, the number of perforating clusters is 7, the half-length of hydraulic fracture is 120 m, the hydraulic fracture conductivity is 400 MD-m, and the matrix permeability is 400 nd; in case 2, the number of perforation clusters is 5, the half-length of hydraulic fracture is 60 m, the hydraulic fracture conductivity is 100 MD-m, and the matrix permeability is 100 nd. Figure 8 shows the relative error between the predictive value and true value of the MLP and LSTM networks. We can see that the number of relative errors of LSTM in this work is far lower than the number of relative errors of the MLP predictions.  
Figure 6: Relative error between the values predicted by the LSTM networks and the true values under different production times.  
Figure 7: LSTM and MLP predictions and actual values of the daily shale gas production under two constraint conditions.
## 2 Materials and Methods
### Data Collection and Preprocessing
These values were then used for model training and evaluation data. Over a monitoring period of 3 years, 8 months, and 5 days, or 1344 days, taking into account leap years, the dataset comprised \(1344\times 24=32,256\) complete hourly measurements, providing a high temporal resolution that is essential for short- and medium-term air quality forecasting. The dataset underwent a series of preprocessing steps to ensure consistency and model preparation. These included handling missing values and applying feature-wise normalization and standardization techniques to account for the unit and value range disparities across the input variables, as well as data partitioning at the minute level using linear interpolation (32,256 \(\times\) 60 = 1,935,360 total measurements).
# A Tailored Pricing Strategy for Different Types of Users in Hybrid Carsharing Systems
## 2 Literature Review  
### Pricing Strategy in Conventional Carsharing Systems  
Many studies showed that prices and demands are related, and a coefficient named price elasticity is widely used to describe the relationship between them [11; 16]. Price elasticity equals the demand variations divided by price variations, which is applied to describe the price sensitivity of different user clusters. Different user clusters have different reactions to the same price. [PERSON] and [PERSON] studied the trip pricing strategy in one-way station-based carsharing systems. A hybrid-integer nonlinear programming model was built to maximize the carsharing operator's profit [11]. Price and demands were also elastically connected, then a hybrid-integer nonlinear and nonconvex programming model was established to determine the trip price, fleet size, personnel size, and relocations simultaneously [16]. Further, [PERSON] et al. [18] constructed a logit model to represent the nonlinear carsharing demands. Results showed that pricing was a key factor impacting the operator's profit. A bi-level programming model was built by considering the profit of the carsharing operator and costs of all users and compared the advantages of relocation, pricing, and the combination of relocation and pricing. Results showed that the pricing strategy outperformed in saving costs for all users [20].  
Besides the connections between price and demands, many studies also performed price incentives to enhance the performance of conventional carsharing systems. Four price incentive strategies were proposed to help operators. For example, by encouraging users to book cars that should be relocated or parked in a region with low availability of cars, the operator can also set paid relocation tasks and hire users to perform or encourage users to share a car with others. These strategies were much more economical and environmental-friendly [21]. Similar research was done by [PERSON] et al. [22]. They applied pricing incentives to relocate sharing cars by users instead of relocations by staff. Different price incentives were given based on the priority of each relocation. This strategy worked well in balancing the number of cars in the carsharing system.  
Studies mentioned above demonstrated the importance of the prices of sharing cars. However, the characteristics of different users are still not considered in pricing strategies.
# Harmonized Autonomous-Human Vehicles via Simulation for Emissions Reduction in Riyadh City
## 7 Conclusions  
This research examined the impacts of integrating autonomous vehicles within human-driven traffic in Riyadh, Saudi Arabia by using detailed microscopic simulations. The results clearly indicate substantial benefits for urban mobility efficiency and environmental sustainability even at relatively modest levels of AV integration. With 50% AV deployment, carbon emissions dropped by nearly 15% and travel times were reduced by over 25% compared to baseline human-only scenarios. The study also provides a practical simulation approach for modeling AV communication and coordinated behaviors, highlighting the strengths and limitations of current microscopic traffic simulation tools. Future research directions include employing integrated traffic-communication simulation platforms to model network-level interactions and assess the impacts of real-world communication constraints. Overall, this study confirms the considerable promise of harmonized AV-HV traffic systems in achieving smart, sustainable urban mobility. It serves as a foundation for further research and practical application in Saudi Arabia and similarly structured urban environments globally. Despite these encouraging findings, several limitations should be acknowledged. This study was based on a simulation framework, and did not incorporate real-time wireless communication effects such as latency, signal interference, or packet loss. The simulated traffic network primarily included signalized intersections, which limits the generalizability of this study to uncontrolled or irregular road settings. Additionally, behavioral aspects such as public trust, compliance, and perception of AVs that were not modeled in this study may significantly affect AV-human interaction in practice. Finally, vehicle distributions and travel patterns were based on the most recent available datasets, which may not capture future variations or localized anomalies. Addressing these limitations in future work will help to improve the real-world applicability and policy relevance of the HARMONY-AV framework.  
A.L.: Conceptualization, A.L.; methodology, A.L.; software, A.L.; analysis, A.L.; writing--original draft preparation, A.L.; review and editing, H.L.; investigation, E.K. All authors have read and agreed to the published version of the manuscript.  
Printed: Prince Sattam bin Abdulaziz University project number (PSAU/2024/01/28710).  
Not applicable.  
**Data Availability Statement:** The data are well described in the manuscript and could be generated. However, datasets are available upon request from corresponding author [PERSON].  
**Acknowledgments:** The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2024/01/28710).  
**Conflicts of Interest:** There are no conflicts of interest associated with this work.
## 3 Results
### Computerized Tests
#### 3.1.3 Results of Test 3  
The number of correct responses and RT were divided into five variables on the basis of the stimulus location. Thus, the results of the first part of the test were represented by the parameter 'Center'.  
The second part of the test was divided into three parameters on the basis of the stimulus appearance location:'vertical' if it appeared above or below; 'horizontal' if it appeared to the left or right; and 'diagonal' if it appeared in one of the four diagonals. Therefore, the entirety of the second part of the test was represented by the parameter 'Peripherical'.  
With respect to correct responses (Figure 7A), it was under the red lighting condition where worse results were obtained compared with the other two types of lighting, with statistically significant differences (\(p\leq\) 0.05) observed when the stimulus appeared in peripheral and vertical locations. There were no significant differences in the number of correct responses between the results obtained under white and blue light. Furthermore,  
Figure 6: The comparison of the correct answers (**A**) and reaction times (**B**) for each contrast level (C1–C8) of Test 2 between each type of lighting. Each \(p\) value is represented on the bars, where * and bold letters indicate statistically significant differences (\(p\leq\) 0.05).  
no significant differences between lighting conditions were found in any case when the stimulus appeared in the center.  
Under red ambient lighting (Figure 7B), significantly lower values than under white light were obtained for the RT at all stimulus appearance locations. When red light was compared with blue light, statistically significant differences were found only in the second part of the test, except for the 'horizontal' parameter (\(p\) = 0.219).  
In contrast, under white light, significantly faster RT values were achieved at all stimulus positions, except for the 'horizontal' location (\(p\) = 0.778), than under blue light.
## 2 Materials and Methods
### Instruments
#### 2.2.2 Signal Analysis Process  
Figure 2 summarizes the process from when the data is obtained until the reading is allowed.  
Firstly, movement is detected through the inertial sensors that make up the device. The information recorded by the accelerometer, gyroscope, and magnetometer is saved in the memory stick. Once the activity is completed, the recorded data is downloaded to the software. Prior to reading the signal by the algorithm, it is possible to adjust the minimum CoD duration time (ms), the minimum peak intensity (G) at the moment of CoD, and a circular mean filter (%) which allows noise reduction. The algorithm used in the detection and calculation of the angles of rotation is directly implemented in the SPro \"Change of Inertia\" module, taking advantage of the modular system of plug-ins (Monitors) available with the software SPro 974. Each module accesses the data from the sensors/channels generated by each WIMU device. The algorithm obtains information from the data channels Euler X (subject's orientation degrees in respect to north) and Earth X, Y (components of acceleration on the horizontal plane in respect to the earth) using magnetometers (Figure 3a,b). Thus, the Earth X and Earth Y values are used to calculate the mediolateral and anteroposterior acceleration (Figure 3c). These accelerations are related to changes in footing or changes of inertia (COIs) by players. To make changes to the coordinate system of the horizontal acceleration vector (from the Earth coordinates to the subject coordinates), the Earth X and Earth Y data are re-projected using the Euler X channel data that had been calculated in the previous step, and the angle and module formed by the new vectors are calculated (Figure 3d). Once each COI is detected, an  
Figure 1: Location of the WIMU devices with the use of the vests.  
automaton-based unification algorithm is applied to identify multiple COI-type moves in a single CoD-type action.  
Figure 3: (**a**) Euler X data channel (degrees of orientation of the subject with respect to north). (**b**) Earth X and Y acceleration channels (components of acceleration in the horizontal plane with respect to the earth). (**c**) Conversion of Earth X and Y acceleration to anteroposterior and mediolateral acceleration. (**d**) New module formed from the anterior posterior and mediolateral signals, together with the CoD detected during the test. (**e**) Speed signal (km/h) from the GPS channel.  
Figure 2: The principal steps of the proposed signals analysis for change of direction detection.  
This is because a single CoD can be made up of several intense COIs, since several changes in footing may be necessary to make the desired change in direction. In this way, and considering the size of the module, the duration of the CoD (ms) is calculated. Finally, the orientation of the subject before and after the movement is analyzed to estimate the angle of rotation in the entry and exit direction of the CoD.
# Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems
## 3 Definitions and Algorithms
### Airspace Operational Volumization
Overlap offers a buffer in case the UAS flies faster or slower than expected. Note that climb and descent segments utilize multiple staircase geofences so that previously occupied staircase geofences can be removed sequentially.
###### Abstract  
Backscatter communication (BC) is a promising technology for low-power and low-data-rate applications, though the signal detection performance is limited since the backscattered signal is usually much weaker than the original signal. When the detection performance is poor, the backscatter device (BD) may not be able to accurately detect and interpret the incoming signal, leading to errors and degraded communication quality. This can result in data loss, slow data transfer rates, and reduced reliability of the communication link. This paper proposes a novel approach to improve the detection performance of backscatter communication systems using evolutionary deep learning. In particular, we focus on training deep convolutional neural networks (DCNNs) to improve the detection performance of BC. We first develop a novel hybrid algorithm based on artificial bee colony (ABC), biogeography-based optimization (BBO), and particle swarm optimization (PSO) to optimize the architecture of the DCNN, followed by training using a large set of benchmark datasets. To develop the hybrid ABC, the migration operator of the BBO is used to improve the exploitation. Moving towards the global best of PSO is also proposed to improve the exploration of the ABC. Then, we take advantage of the proposed deep architecture to improve the bit-error rate (BER) performance of the studied BC system. The simulation results demonstrate that the proposed algorithm has the best performance in training the benchmark datasets. The results also show that the proposed approach significantly improves the detection performance of backscattered signals compared to existing works.  
backscatter communication; detection performance; bit-error rate; deep convolutional neural network; hybrid artificial bee colony +
Footnote †: journal: Electronics Electronics
###### Abstract  
This work aims to reveal the full-field strain evolution characteristics and failure mechanisms of anisotropic coal samples under creep loading. A series of compression tests combined with digital image correlation (DIC) monitoring were employed to characterize the strain evolution process of coal specimens with bedding angles of 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\). Testing results show that the peak strength, peak strain, and the creep loading stage of coal are significantly influenced by the bedding angle. The peak strength initially decreases and then increases as the bedding angle increases. In addition, the creep failure of coal manifests as a process of instantaneous deformation, decelerating creep, steady-state creep, accelerating creep, and failure. Under graded creep loading conditions, coal specimens exhibit distinct creep characteristics at high stress levels. Moreover, the bedding angle significantly influences the strain field evolution of the coal samples. Finally, for coal specimens with bedding angles of 0\({}^{\circ}\) and 90\({}^{\circ}\), the final macroscopic fracture pattern upon failure is characterized by longitudinal tensile splitting. In contrast, coal samples with bedding angles of 30\({}^{\circ}\) and 60\({}^{\circ}\) tend to exhibit failure along the bedding interfaces, forming tensile-shear fractures. The results of this study will provide theoretical guidance for the prevention, early warning, and safety management of coal mine disasters.  
coal sample; anisotropy failure; DIC; creep loading; full-field strain Article
## 3 Results
### Classification Method Comparison
#### 3.1.2 Hollywood Movie Success  
Figure 4 presents the same information as Figure 2 for the Hollywood movie success dataset. A movie was considered successful if the revenue was greater than two times the initial budget for the movie. The results presented here use the features listed in Table 3 only, not including the additional cast, crew, production, and director information. WoC-Bots performed very similarly to a DNN implementation, with 81.1% accuracy compared to 81.3% for the DNN. WoC-Bots outperformed the DNN with a precision of 84.0% compared with 82.9% and were outperformed on recall with 79.7% for WoC-Bots and 80.8% for the DNN. The XGBoost and Random Forest methods again outperformed the other classification methods on the accuracy and precision metrics.  
Figure 3: Runtime Comparison for a Single Prediction for All Methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interval** & \(\mathbf{n}\) = 483 & **\% of \(\mathbf{n}\)** & **Accuracy (\%)** \\ \hline Very High Confidence & 3 & 0.62 & 100 \\ High Confidence & 154 & 31.9 & 93.1 \\ Medium Confidence & 224 & 46.4 & 82.3 \\ Low Confidence & 102 & 21.1 & 64.7 \\ \hline Very High + High + Medium & 381 & 78.9 & 86.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confidence Interval Distribution and Accuracy—WoC-Bots for Breast Cancer Metastasis Status.  
The meta-swarm was again tested, outperforming all other methods with an accuracy of 83.7%. Additionally, only the XGBoost method had a better recall value at 82.9% compared with the meta-swarm's recall of 82.6%. Confidence intervals were tested with this dataset, but applied to the output of the meta-swarm instead of directly to WoC-Bots. Table 6 shows the results of this analysis, with 61.4% of samples captured in the \"Very High\", \"High\", and \"Medium\" confidence intervals with a combined accuracy of 87.8%. Additionally, the \"Very High\" interval was more useful for this dataset, capturing 1.4% of samples with an accuracy of 96.1% and the \"High\" interval captured over 25% of samples with an accuracy of 91.6%.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 3 Background of Middleware
### Publish/Subscribe Middleware
#### 3.1.3 Example of Publish/Subscribe Middleware  
Table 4 shows some prominent publish/subscribe middleware with possible applications in ITS. The first column is the source of the middleware, the second is its name, the third shows whether the middleware is Free and Open Source Software (FOSS), and the fourth describes the possible application of the middleware according to the developer. The table also includes the MQTT protocol because it is common in ITS and other  
Figure 7: A conceptual model of middleware integration in ITS architecture.  
embedded systems. Middleware is a complete messaging infrastructure, while a protocol is a communication standard that defines how messages are formatted and exchanged between publishers and subscribers [16]. Thus, middleware solutions often implement one or more protocols.