esa-sceva/satcom-chunk-collection huggingface dataset:
# Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques
## 5 Conclusions  
Traffic accidents cause a large number of fatalities and enormous socioeconomic consequences every year in KSA. The current study proposes an application of machine learning-based algorithms and SHAP for modeling and interpreting traffic accident injury severity in the Al-Qassim province of KSA. Using two ensemble machine learning methods, random forest and XGBoost, as well as logistic regression, revealed that XGBoost outperformed the others by achieving an accuracy of 0.71 and an AUC of 0.87. Further analysis conducted by treating the target variable as a binary classification revealed that XGBoost achieved an accuracy of 0.94 and distinguished the class with an AUC of 0.98. SHapley Additive exPlanations can effectively extract the feature importance and explore the relationships with the help of dependence plots.  
Results revealed that the following factors primarily impact the accident injury severity: month of the year, road type, consequences of the accident, and day of the week. Furthermore, the presence of paint, cateyes and road conditions have the least impact on the severity of the injury. A precise analysis for each injury severity class revealed that PDO accidents mainly depend on the consequences and the number of vehicles involved. The main cause of accidents is the most important parameter for classifying an injury class, followed by road type and lighting conditions. For the death class, month of the year, day of the week, and road type were the most significant variables.  
The nature of the available data resulted in the present study having some limitations. The explanatory variables were categorical, except the number of vehicles involved. There was also an absence of some important variables, such as the physiology of the driver, age, and gender. The authors suggest the adoption of XGBoost and SHapley Additive exPlanations in injury severity classification studies and recommend investigating the implementation of DNN or hybrid neural networks in future research.  
[PERSON] was involved in data analysis, methodology, software, and manuscript writing. [PERSON]. ([PERSON]) was involved in conceptualization, data collection, supervision, and paper review. [PERSON] was involved in conceptualization, supervision, and manuscript writing. [PERSON], [PERSON] ([PERSON]) and [PERSON] were involved in conceptualization and paper review. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Highly confidential data that cannot be publicly shared due to the confidentiality agreement between the research and data sharing organization.  
The researchers would like to thank the Deanship of Scientific Research, Qassim University for funding the publication of this project. The authors also acknowledge the Ministry of Transport for data sharing.  
The authors declare no conflict of interest.
# Transmitter Diversity Gain Technique Aided Irregular Channel Coding for Mobile Video Transmission
## 6 Results and Discussion
### EXIT Chart Analysis of the Constituent Decoders
More specifically, the EXIT charts for REP, IREP-1, and IREP-2 schemes of 3, along with their actual decoding trajectories at \(E_{b}/N_{0}\) values of 1 dB and 2 dB, with a consistent overall code rate of 3/4, are plotted in Figures 4(b)-d. It can be observed from the decoding trajectories of Figures 4(b)-d that the IREP schemes have relatively better convergence behavior when compared to the REP scheme. This improvement is manifested, due to the provision of a relatively wider EXIT tunnel for the IREP scheme. Furthermore, it can be observed from the EXIT curves of Figures 4(c),d that the employed IREP-1 and IREP-2 have similar EXIT characteristic curves and decoding trajectories at \(E_{b}/N_{0}\) value of 1 dB and 2 dB, because the IREP-1 and IREP-2 have employed the same inner and outer codes, and the only difference is in the order of presentation of the coded stream to the iterative coding-decoding setup. Figure 5: _Cont._
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 5 Simulation Analysis
### IEEE 33-Node Distribution Network  
The IEEE 33-node distribution system is shown in Figure 14, which has 1 substation power supply, 1 circuit breaker, 32 intelligent switches, and 33 sections. The population size of the intelligent optimization algorithm was set to 50, and the maximum number of iterations was set to 30.  
Figure 14: IEEE 33-node distribution system.  
\begin{table}
\begin{tabular}{c c c} \hline
**Algorithm Number** & **Algorithm Type** & **Fault Location Method** \\ \hline Algorithm 1 & Algorithm in this paper & Algorithm in this paper. Construct a numerical computational model for fault location with the help of road vector, and apply DAC to locate the fault. \\  & & Assuming all possible fault scenarios in the distribution network, construct a solution space and apply the binary particle swarm optimization algorithm to locate the fault section in the solution space. \\  & & Partition the distribution network according to the branch structure, apply the bald eagle search algorithm to locate the fault branch first, and then determine the fault section by applying the exhaustive method in the fault branch. \\ Algorithm 4 & Linear integer programming algorithm & Construct a linear integer programming model for fault location using ascending dimension and apply the branch and bound method to locate the fault. \\ \hline \end{tabular}
\end{table}
Table 5: Fault location methods for various algorithms.
# Hardware Design of a High Dynamic Range Radio Frequency (RF) Harmonic Measurement System
## 5 System Test Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & \(\mathbf{D_{2,ref}}\) **(dBm\({}^{-1}\))** & \(\mathbf{D_{2,through}}\) **(dBm\({}^{-1}\))** \\ \hline Open circuit & \(-\)215.0 & — \\ N(m)-N(m) adaptor (new) & \(-\)210.0 & \(-\)210.0 \\ Mixer & \(-\)30.0 & \(-\)40.0 \\
10-dB attenuator & \(-\)153.6 & \(-\)155.4 \\ Circulator (terminated in 50 \(\Omega\)) & \(-\)121.3 & \(-\)104.0 \\ Circulator (open-circuited) & \(-\)108.4 & — \\ N(m)-SMA(f) adaptor (old) & \(-\)190.8 & — \\ Open SMA connector & \(-\)202.0 & — \\ Open SMA connector with solder & \(-\)196.5 & — \\ Open SMA connector with solder and steel & \(-\)174.9 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Harmonic generation results from various passive devices shown in Figure 12.
## 2 Materials and Methods
### Cross-Validation Performance  
Repeated 5-fold CV was conducted on the soccer group. Performance metrics included accuracy, balanced accuracy, macro F1-score, Cohen's kappa (\(\kappa\)), and Matthews correlation coefficient (MCC). Mean values across folds were calculated. Confusion matrices from each fold were aggregated to evaluate misclassification patterns. For the RF model, 95% confidence intervals for key metrics were estimated using 1000 bootstrap resamples.
## 3 Experiment Results  
We empirically evaluated our FS algorithm by comparing it with representative FS algorithms on three high-dimensional gene microarray datasets. This section is organized as follows: After a brief introduction of the datasets, we describe the experimental setup. We then compare our FS algorithm with the representative filter-based FS algorithms in terms of feature quality, classification accuracy, and computational complexity.
## 4 Discussion  
From our analysis in Sections 2 and 3, to achieve the best symmetry and higher efficiency of combating fiber nonlinearity, different Raman schemes have to be considered for different span lengths. A table (Table 1) summarizing the span symmetry with Raman pumping schemes at different span lengths is demonstrated below. It is shown that for the short span length of 25 km, the first-order bidirectional Raman pumping was sufficient to achieve 97% signal power symmetry, but the length of 25 km was very short for OPC-based application (e.g., long-haul transmission systems). In addition, for this short length, the symmetry changes will be relatively small when using different Raman schemes, and bidirectional Raman pumping will introduce significant RIN-replated penalty. For the span length of around 50 km, optimized dual-order Raman pumping is required to achieve 97% span symmetry. However, for longer span length, bidirectional second-order Raman pumping would be needed as the signal gain can be generated from the start of the span. Thus, the scheme based on a random fiber laser with bidirectional second-order pumping without introducing RIN penalty was demonstrated to achieve 97% span symmetry at 62 km. Further extending the span length to 100 km, higher order bidirectional pumping would be required, but given the RIN penalty introduced from conventional Raman pumps,  
Figure 16: Q\({}^{2}\) factors versus signal launch power in inline transmission systems without/with OPC using the optimized dual-order DRA scheme.  
we had to stick to RIN-penalty-free bidirectional pumping based on a random fiber laser. In this case, the optimum span symmetry dropped to 72% at 100 km. Alternatively, we could break the 100 km into two 50 km spans, and then the problem became how to leverage the loss between the two spans. We used the EDF with Raman pumps to account for the loss between spans and improved the span symmetry from 72% (single span) to 93% (two spans).
## 6 Related Work  
This section provides an overview of the research related to _model transparency_, in particular _features selection_, _association rule mining_, and discusses approaches based on _combining_ association rule mining and regression analysis. A summary of these approaches with respect to the desiderata in Section 2 is given in Table 9.  
The increasing adoption of classifiers to support human decision-making processes has led to an increasing importance of the transparency of the models generated by machine learning techniques. A recent survey on this topic [23] identifies two categories of problems related to model interpretability, i.e., _a black-box explanation problem_, where decisions returned by a black-box classifier are analyzed to construct an explanation, and a _transparent box design problem_, where the goal is to develop interpretable, white-box classifiers. Our work is related to approaches in the first category, which can be further refined in three subgroups: _model explanation_ aims to provide human-interpretable models capable of mimicking the behavior of the original classifiers [24, 25, 26]; _outcome explanation_ aims to build local models explaining predictions made on single instances [27, 28]; _model inspection_ aims to provide a human understandable representation of some specific properties of the model and/or its predictions [29, 30]. Our work is mainly related to model explanation, particularly so-called _agnostic_ approaches (i.e., approaches that are not tailored to a specific classifier), which usually provide explanations in terms of features ranking. This problem overlaps with the _feature selection_ problem, whose goal is to identify and remove those features that either do not have an impact on the classification or are \"redundant\", i.e., they are correlated to other features [31, 32, 33, 34]. These methods can be grouped into three main categories. _Filter_ methods
## 4 Experiments
### Performance Evaluation  
For the first experiment, a real-world clickstream data set was collected containing records of 977 students who took an online course. There were eight features for each student in the data set and 16 monitoring weeks. The following eight features were used as data input for this experiment: assignment, file, forum, homepage, label, page, quiz, and URL. Meanwhile, the final student grade category (i.e., pass and fail) was employed as the classification label. Table 2 presents the statistical analysis of the real-world data set in terms of the normalized standard deviation (NSD), normalized mean (NM), skewness, and kurtosis. The NSD measured how close the data distribution was to the mean value. Skewness measured data asymmetry around the mean value. Normal distribution, ideally symmetric, had a zero skewness. Meanwhile, kurtosis indicated the distribution susceptibility to outliers. The ideal kurtosis value of the normal distribution was close to 3. Based on statistical analysis, the NSD and NM of the class label were the highest, with a value of 0.332847 and 0.873503, respectively. From here, the data set was imbalanced with most decisions going to the majority class (i.e., grade: pass). On the other hand, similar data characteristics were observed for the eight features. The features had a positive value of skewness. The kurtosis of the features also deviated from 3 when compared with the class label. Because the kurtosis was higher than 3, the distribution of the eight features had a heavier tail and a sharper peak than that of the normal distribution.  
Correspondingly, this imbalanced data set was investigated in terms of performance comparison, as depicted in Table 3. The imbalanced data set will become a major problem, for example, for an imbalanced data set with a 10% at-risk rate. Hence, a classification model may successfully make a prediction with a 0.9 accuracy for all students. However, in this case, the classification model failed to predict any at-risk students. To make detailed investigations, the baseline approaches of LR, SVM, and LSTM were evaluated. Then, a hybrid sampling technique was performed on the data set. This technique synthesized new examples from minority classes using the SMOTE algorithm. SMOTE worked by selecting minority examples being close in the feature space, drawing a line between the examples, and then determining a new sample at a point along that line. After that, a random under-sampling was performed on the majority class. This hybrid (i.e., SMOTE-RUS) sampling technique achieved better results, as stated in [36]. Table 3 signifies that the hybrid sampling method improved the performance of the prediction model, especially on precision, recall, and F1-score matrices, increasing to more than 90% than those without any sampling strategy. Meanwhile, accuracy was comparable for both with and without hybrid sampling.
## 2 Selection of Anchorage Angle  
Before the static load tests, it is necessary to define the range of the anchorage angle \(\theta\) (i.e., the angle between the stay cable direction and the normal direction of the anchor point), which affects the magnitude of the normal component \(F_{\rm N}\) and tangential component \(F_{\rm v}\) of the cable force \(F\) at the connection in the anchorage zone at the bottom of the girder. The normal component is positively correlated with the pressure on the contact surfaces and the lifting force of the girder, while the tangential component is positively correlated with the shear on the contact surfaces and the shear resistance required for the connection. As shown in Figure 2, the anchorage angle \(\theta\) depends on the tower height \(H\) (i.e., the vertical distance from the anchor point of tower to the deck) and the distance \(x\) between the anchor point and symmetrical center. For partially CSS-enhanced bridges, the ratio of tower height \(H\) to the maximum span \(L\) ranges from \(L/12\) to \(L/4\). The tower height \(H\) should be maximized (\(H=L/4\)) to increase the cross-sectional height of box girder. The maximum value falls in the distribution range of anchor points (i.e., \(L/8\)-3\(L/8\) away from the center line of pier) specified in the _Guidelines for Design of Highway Cable-stayed Bridge_ (JTG/T D65-01-2007) [26]. The statistics on 152 PSC-CBGBs (\(L\geq\)100 mm) around the world show that the anchorage angles vary between 36.41 and 55.01\({}^{\circ}\). Therefore, the anchorage angle of the specimens was set to the range of 35-60\({}^{\circ}\) in the tests, and the corresponding range of shear-compression ratio is 0.70-1.73.
## 4 The Application of Artificial Intelligence in the Design of Basic Materials for Sports
### Ceramic Materials  
The application of ceramic materials in the field of sports is mainly due to their high hardness, wear resistance and high temperature resistance--for example, wear parts for high-performance sports equipment, such as zirconia ceramic ski edges and bicycle bearings.  
In 2023, [PERSON] et al. (2023) proposed a fatigue life analysis method for composite materials based on Artificial Neural Networks. This method takes material parameters and load parameters as the network input and fatigue life as the output to construct a model of the relationship between the input parameters and fatigue life. Three models for predicting the fatigue of 2D braided ceramic matrix composites were established under the condition of small samples. The results show that Elman Network (ENN) and Convolutional Neural Network (CNN) can maintain high prediction accuracy under different training data scales. Their prediction errors gradually increase with the decrease of the training sample size, demonstrating excellent generalization ability, and their prediction results are in good agreement with the experimental values. However, the prediction accuracy of Generalized Regression Neural Network (GRNN) has always been difficult to meet the requirements of engineering applications.  
In 2024, [PERSON] et al. (2024) proposed a combination of machine learning and optimization programs to fine-tune the electrical properties of lead-free (1 - x) Na\({}_{0.5}\) Bi\({}_{0.5}\)TiO\({}_{3-\chi}\)CaTiO\({}_{3}\) piezoelectric ceramics. A comprehensive set of dielectric measurement data was used to train ML models that accurately predict the dielectric constant (\(\epsilon\)) and dielectric loss (tan8) as functions of Ca\({}^{2+}\) concentration (x% Ca), temperature, and frequency.  
NNs show remarkable results in predicting the life and properties of complex ceramic materials. The life and properties of ceramic materials are usually affected by many factors, including microstructure, composition, preparation process and use environment, etc., and the relationship between these factors is often complex and non-linear. Through its powerful data learning and pattern recognition ability, neural networks can extract these complex relationships from many experimental and practical data, to accurately predict the fatigue life, wear performance and failure mechanism of ceramic materials.
## 2 Materials and Methods
### 3 Synthesis of gold nanoparticles (AuNPs)
The pellet was washed four times with 5 mM HEPES buffer (pH 7.6) and centrifuged to remove any unbound DNA strands. The final DNA-AuNP conjugate was redispersed in 100 uL of 5 mM HEPES buffer (pH 7.6) for further use. **Construction of pure and DNA hydrogel composites. ** To make hydrogels using the self-assembled DNA tiles or DNA/nanomaterials conjugates, and spacers, desired volumes of crosslinker and spacer stocks were combined on a piece of parafilm using the concentrations and ratios listed in Table S2. For example, 10 uL of Y-shaped DNA tiles stock and 10 uL of spacer stock were added on a piece of parafilm and immediately mixed. DNA hydrogels were formed within one minute, and the DNA gel samples were immediately tested. **TEM and analysis. ** The dimensions and morphology of AuNPs, DNA-AuNP conjugates, DNA-CNT conjugates, and dehydrated DNA hydrogel composites were imaged using an FEI Talos F200X scanning/transmission electron microscope (Hillsboro, OR, USA) at an accelerating voltage of 200 kV. CNT samples were drop cast and dried onto 300-mesh copper grids with lacey formvar support film reinforced by a heavy coating of carbon ([PERSON], 01883, Redding, USA). AuNP samples were prepared on 200-mesh copper grids with a formvar film covered with a light layer of carbon ([PERSON], 01800-F). Dimensions of the imaged samples were measured with ImageJ software (Bethesda, MD, USA). **Measurement of rheological properties of hydrogels. ** A TA Instruments DHR-2 stress-controlled rheometer (New Castle, DE, USA) was used to perform small-amplitude oscillation frequency sweeps at room temperature. An aluminum plate of 20-mm diameter was used as the top plate. We prepared DNA hydrogel samples by pipetting the components onto parafilm; following gel formation, the samples were transferred to the rheometer plate without pipetting, to avoid shearing the gels. For a \(\sim\)30-\(\upmu\)L hydrogel sample, the gap distance was set as 35 \(\upmu\)m. The applied strain was set to 1%, while the angular frequency was decreased from 100 to 0.1 rad/s. Five points were collected per decade. **Electrodes and electrical measurement setup. ** Gold electrodes of parallel lines with defined widths and gap distances were fabricated via thin film vapor deposition. To perform current-voltage (IV) curve measurement, the sample was connected to a socket board in a Faraday cage (Hewlett Packard Test Fixture Analyzer 16058A, Palo Alto, CA, USA) connected to a 2-channel (medium power) source/monitor unit module (Agilent Technologies E5272A, Santa Clara, CA, USA).
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 3 Methodology
### Model Calibrations and Validation
#### 3.8.2 Average Travel Time Validation  
To validate the accuracy of simulated travel times, a sample of 20 vehicles was randomly selected for each direction, considering different signal phases. Unlike the queue validation, these vehicles were chosen randomly during various signal times to capture a more diverse set of traffic scenarios. The travel time for each vehicle was estimated using a stopwatch between two fixed points spaced 150 m apart. The comparison between the estimated and simulated travel times is presented in Table 11 and illustrated in Figure 12.  
The north- and west-bound directions exhibited accuracy differences of approximately 3.87% and 2.84%, respectively, indicating a relatively close alignment between the estimated and simulated travel times. Similarly, the east- and south-bound directions displayed accuracy differences of approximately 4.96% and 4.86%, respectively, as shown in Figure 12. These findings signify the effectiveness of the simulation in replicating real-world travel time dynamics, demonstrating its capability to provide accurate representations of traffic behavior under varying signal conditions.  
The contradiction between longer queues and shorter travel times can be explained by differences between real-world human driving and the simulated model. In the simulation, longer queues may form due to conservative driving behaviors, with larger gaps or slower reactions at intersections. However, once vehicles start moving, they tend to accelerate more efficiently in the simulation, leading to smoother traffic flow and shorter travel times. This explains why, despite longer queues, vehicles clear the intersection faster in the model compared to real-world conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vehicle No.**} & \multicolumn{4}{c}{**Estimated Travel Time (sec)**} \\ \cline{2-5}  & **North-Bound** & **East-Bound** & **South-Bound** & **West-Bound** \\ \hline No.1 & 57.23 & 79.23 & 67.23 & 63.23 \\ No.2 & 51.37 & 82.43 & 55.37 & 58.37 \\ No.3 & 47.84 & 83.81 & 57.84 & 62.84 \\ No.4 & 46.43