esa-sceva/satcom-chunk-collection huggingface dataset:
# Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques
## 3 Materials and Methods
### Methods
#### 3.3.8 Model Evaluation  
In modeling a classification problem, the metrics to test the model's performance are the same for statistical and machine learning. Figure 4 presents the four types of outcomes that can occur when performing classification predictions.  
When an observation is predicted to belong to a class and does belong to that class, it is considered a true positive (TP). True negatives (TN) occur when a prediction of an observation does not belong to a class, and it actually does not belong to one. A false positive (FP) occurs when an observation is predicted to belong to a class when it actually does not. In false negative (FN) situations, the prediction of an observation does not belong to a particular class, but in reality, it does. Figure 4 presents a confusion matrix with a commonly used binary classification system. The true positive ratio (TPR) quantifies the proportion of positives that are correctly identified, while the false positive ratio (FPR), using Equation (15), quantifies the proportion of negatives that are incorrectly classified as positives:  
\[False\ Positive\ Ratio\left(FPR\right)=\frac{FP}{FP+TN} \tag{15}\]  
Accuracy, precision, F1-score, and recall were used to evaluate the performance of the classification model. Accuracy is the percentage of correct predictions for the test data using the following equation:  
\[Accuracy=\frac{Co\text{\small{\it{correct\ predictions}}}}{All\ predictions}=\frac{TP+TN}{TP+FN+TN+FP} \tag{16}\]  
The precision is the percentage of true positive examples among all the examples predicted to belong to a particular class using Equation (17). Low precision indicates a high number of false positives (FPs):  
\[Precision=\frac{TP}{TP+FP} \tag{17}\]  
The sensitivity/recall of an algorithm determines its completeness using Equation (18). A low recall indicates the presence of a large number of FNs:  
\[Recall=\frac{TP}{TP+FP} \tag{18}\]  
Figure 4: Typical confusion matrix scenario for classification problems.  
By taking the harmonic mean of a classifier's precision and recall, the F1-score is calculated. The F1-score comes into play when one classifier has a high recall, while the other has high precision. The F1-scores of both classifiers can be used in this case to determine which classifier produces better results via Equation (19):  
\[F1-score=\frac{2(Precision\times Recall)}{Precision+Recall}=\frac{TP}{TP+\frac{1}{2}( FP+FN)} \tag{19}\]  
Moreover, the area under the receiver operating characteristic (AUC-ROC) curve is used for evaluating the model performance and is one of the most important metrics, indicating how well the model distinguishes between injury classes. If the AUC is high, the model is more accurate in predicting severity classes to label injury as injury and death as death. The ROC curve is plotted with TPR on the y-axis and FPR on the x-axis.
## 1 Introduction
Although the use of the above internal curing agents can reduce the shrinkage development of cementitious materials, it may lead to negative effects such as reduced strength of cement-based materials [22; 23; 24; 25], unstable volume development, and poor economy. Therefore, it has been a major challenge in the field of engineering materials to achieve a balance between reducing the shrinkage development of cementitious materials and ensuring that the incorporation of internal curing agents does not reduce the mechanical properties of cement-based materials. Diatomaceous earth (DE), as a low-cost and easily accessible mineral resource, has a unique natural macropore/mesopore structure, which enables it to have good water absorption and release properties, making it a potential high-quality internal curing agent. The main component of diatomic is silicon dioxide [26], which has a certain pozzolanic activity and can participate in hydration reactions. Common lime mortar can also be used to restore ancient buildings through pozzolanic reactions, which can improve the surface strength of buildings. Therefore, to a certain extent, it can offset the decrease in mortar strength caused by the introduction of DE internal curing agent [27; 28; 29]. This article mainly uses local materials such as ordinary Portland cement (OPC), diatomaceous earth (DE), and river sand (RS) to design low water-binder ratio cement mortar. Part of the cement is replaced with DE, which can achieve internal curing effects with simple processing and low cost and is conducive to popularization and application. Additionally, the use of DE as a 1% cement substitute in the production of cement mortar reduces carbon dioxide emissions by 1.962 tons per 1000 m\({}^{3}\) of brick masonry project, thus mitigating the environmental impact caused by partial cement replacement with DE [30]. Therefore, the purpose of this study is to determine the effects of replacing OPC with DE as an internal curing agent on the mechanical properties, shrinkage properties, crack resistance, and microstructure of low water-binder ratio cement mortar.
## 4 Discussion
### NDVI Seasonal Variability and GPP Seasonality  
These results support previous studies of deciduous forest biomes, which are typified by a strong dependence of photosynthetic activity on leaf area expansion, that showed MODIS and in situ NDVI data patterns correspond with dynamics of net ecosystem exchange [(35)] and GPP [(19; 44)]. Furthermore, the production and the expansion of the new leaves in spring needed for activating the photosynthetic process provoke an evident spectral shift that has a clear effect on canopy greenness [(16; 19)]. For that reason, the start of growing seasons of DBF can be estimated by remotely sensed indicators of the canopy greenness [(17; 45)].  
In contrast, we found a weak relationship between the two NDVIs in evergreen forests. However, our results agree with previous studies where the start of season of DBF is more successfully detected than for evergreen needleleaf and broadleaf biomes [(44)]. In fact, in evergreen biomes, the expansion of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Observations** & **R\({}^{2}\)** & **RMSE** & **NMB** & **Slope** & **Y-int** & **R\({}^{2}\)cv** \\ \hline  & n & (-) & (days) & (days) & (-) & (days) & (-) \\ \hline NDVI\({}_{is}\) & 44 & 0.53 \({}^{\star}\) & 27.78 & \(-\)0.009 & \(-\)21.28 & 0.80 & 0.53 \\ NDVI\({}_{MD}\) 0.5 km \(\times\) 0.5 km & 44 & 0.34 \({}^{\star}\) & 32.90 & \(-\)0.010 & \(-\)23.30 & 0.74 & 0.34 \\ \hline \end{tabular} \({}^{\star}\)Correlation coefficients with \(p<\) 0.05.  
\end{table}
Table 6: Relationship between the start of season (SOS) of GPP derived from eddy covariance data (days) and the start of greenness derived from NDVI\({}_{is}\) and NDVI\({}_{MD}\) at a spatial resolution of 0.5 \(\times\) 0.5 km. Observations: Number of available days; R\({}^{2}\): Root mean square error; RMSE: Root mean square error; NMB: Normalized mean bias; Slope: Slope of the linear model; Y-int: \(y\)-intercept of the linear model; R\({}^{2}\)cv: Cross-validated coefficient of determination.  
leaves is unrelated to the start of spring photosynthetic activity and GPP and uncoupled with changes in spring greenness [46]. In addition, in boreal evergreen biomes, the annual phenological cycles are related more to snow and snow melt than changes in needle canopy greening dynamics in boreal climates, as shown in the work of [PERSON] et al. [47]. Given that the change in greenness of ENF at the start of the growing season may be decoupled from the start of seasonal carbon uptake, and hence may be independent of ecosystem physiological activity [48], and the view that NDVI may not be an effective proxy for the estimation of seasonal changes in GPP phenology [17], the chlorophyll content index (CCI; [10]) has been proposed as a more accurate remote sensing based proxy to estimate GPP phenology and seasonal dynamics in ENF [27].
## 1 Introduction
Due to the limited number and spatial distribution of FLUXNET sites, VPP from GPP measures cannot provide adequate information for a study at the global level. Predicting VPP by upscaling FLUXNET measurements using remote sensing data is a feasible technique to obtain VPP estimates across a range of spatial and temporal scales [7]. Previous studies have confirmed that a combination of LSP or vegetation indices and land surface temperature (LST) derived from MODIS data can be used to explain the VPP values for deciduous and evergreen needle forests at the site scale [7,24]. However, the effectiveness of this method in modeling global VPP and comparing LSP and VPP effects on GPP estimation is still limited. In this study, FLUXNET site-level VPP observations were extracted from GPP estimates derived from FLUXNET measurements. Then, the global VPP products were estimated based on the relationship between site-level VPP observations and MODIS LST (MOD11A2) and enhanced vegetation index (EVI, MOD13Q1) products in 2001-2017 using a statistical regression method. The VPP estimates were then compared with LSP estimates from MODIS products (MCD12Q2, hereafter referred to as MODIS-LSP). Within this context, the objectives of this study are: (1) to construct a series of statistical regression models for estimating the global VPP of different PFTs and map long-term global VPP products; (2) to compare the discrepancies between VPPest and MODIS-LSP estimates across different PFTs and climatic regions; and (3) to investigate whether VPP is superior to LSP in estimating global vegetation photosynthetic carbon uptake.
## 2 Additive Manufacturing Techniques  
Metallic AM techniques can be broadly classified according to feedstock type, powder-fed or powder-bed fusion processes, and heat source type (laser or electric). Laser AM employs a gas or solid-state optical device with a focusing lens that produces a laser energy beam through rays. The advantages of a laser heat source include a small heat-affected zone, but high reflectivity is a drawback [8]. AM processes such as selective laser melting (SLM), direct metal deposition (DMD), and wire-based fusion employ laser heat energy to melt the feedstocks. Feedback in additive manufacturing can be either wire-based or powder-based. Examples include wire arc additive manufacturing (WAAM) for wire-based processes and selective laser melting (SLM) for powder-based processes.  
* Powder-bed fusion is an additive manufacturing process in which a layer of metallic powder is deposited and selectively melted using a laser or electron beam. Common techniques within this category include direct metal laser sintering (DMLS), electron beam melting (EBM), and selective laser melting (SLM).  
Powder-fed fusion is an additive manufacturing process in which both the feedstock and laser energy are delivered through the same source. Common examples of this technique include direct metal deposition (DMD), laser additive deposition (LAD), and thermal spraying. Figure 2 shows a comparison of machining using different additive manufacturing techniques, obtained from a systematic literature review. However, the cited reference does not specify the criteria used for article selection, which represents a limitation of the paper. Different metallic AM techniques are described in Figure 3.  
Laser-based AM processes are subject to a significant degree of induced residual stresses [9; 10], which in Ti6 Al4V AM are typically \(5\times 10^{4}\) K/cm [11]. The maximum stress value always occurs at or just below the surface, and the ultimate residual stresses will rise as the number of layers increases. According to the free surface of the final deposited layer, the residual stress profile is composed chiefly of tensile stresses at the top section of the material, the value of which is equivalent to the yield strength of a component [12]; adding a new layer causes compressive stress in the layers below the previous one to increase and tensile stress in the top layers to change into compressive stress [13; 14]. Also, as the scan length advances, so do residual strains [10] representing various stresses of a different material at some selected tool geometry, cutting speed, and nose radius.
## 2 Materials and Methods
### Multi-Criteria Decision Analysis (MCDA)
#### 2.2.2 GIS-Based Derivation of Criteria and Constraints  
Slope  
The slope map of a terrain identifies the steepness at each raster cell. The terrain becomes steeper as the slope value is increased and vice versa [29]. As mentioned in Section 2.1.2, the most favourable terrain for installing a CR is a surface with less than 10 degrees of steepness. The slope criterion was created from DTM and categorized into  
Figure 6: The produced point cloud from the acquired images (one nadir and two obliques).  
Sentinel-1 satellite mission is a near-polar, sun-synchronous orbit, performing C-band synthetic aperture radar imaging [31]; both ascending and descending pass directions are available in Cyprus. In particular, ascending refers to the satellite's pass from South to North and descending from North to South, where observations are performed from West and East, respectively [32]. Hence, installing the CRs (one for each pass), the aspect direction of East is the most favourable value for the descending pass and West aspect values for the ascending. Flat areas having no downslope direction are also suitable for both passes.
## 5 Results
With that method, results could be obtained for the accuracy range from (1e-1) to (1e-8) or even higher, and for discretization, from \(N\geq 5\) to \(N\geq 40\), but the advantage is to take into account reasonable processing time. The results comparison of the RKfixed, RK45, and Gear BDF methods in different accuracies and discretizations for the calculated averaged temperatures of the modeled fluid (water) \(T_{f}\) (Figure 7), calculated averaged adsorption bed temperatures \(T_{a}\) (Figure 8), as well as averaged water vapor uptake \(a\) on the silica gel adsorbent (Figure 9) shows small differences in the course of the curves, particularly in the area of undesired oscillations. There was one main exception; the results obtained from RKfixed (\(N20\), time step 1 s) differed from the other results by about 10 K for adsorbent temperature and about 9% absolute difference for the uptake. This means that the time step 1 s is overly large and the results are overly rough. Figure 7: Results comparison of the RKfixed, RK45, and Gear BDF methods in different accuracies and discretizations, calculated averaged temperatures of the modeled fluid (water) \(T_{f}\).
# Free Vibration Analysis of a Thermally Loaded Porous Functionally Graded Rotor-Bearing System
## 6 Results and Discussions
### Effect of Porosity on Material Properties  
The material properties of the FG shaft are the functions of the power law parameters, temperature and volume fraction of porosity. The temperature of the inner core is considered to be 300 K, whereas the outer layer is considered to be 900 K. The variation in the Young modulus of the porous FG shaft through the radius is shown in Figure 5a,b for volume fractions with porosities of 0.1 and 0.2. Young's modulus of the FG shaft reduces due to the presence of porosities in the FG shaft. Similarly, other material properties such as density and Poisson's ratio are affected due to the presence of porosities in the FG shaft. The effect on material properties is more prevalent with the increase in the volume fraction of porosity.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Shaft** \\ \hline Length (L) & 0.5 m \\ Diameter (D) & 0.02 m \\ \hline \multicolumn{2}{c}{**Disc**} \\ \hline Location & Mid-span \\ Mass (m) & 2 kg \\ Polar moment of inertia (I\({}_{\text{p}}\)) & 0.0024 kg m\({}^{2}\) \\ Diametral moment of inertia (I\({}_{\text{d}}\)) & 0.0012 kg m\({}^{2}\) \\ Bearing Stiffness & \(10^{5}\) N/m \\ \hline \hline \end{tabular}
\end{table}
Table 6: Data used in the present work.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Nodes**} & \multicolumn{3}{c}{**0 RPM**} & \multicolumn{3}{c}{**4000 RPM**} \\ \cline{2-7}  & **[46]** & **Present** & **Error \%** & **[46]** & **Present** & **Error \%** \\ \hline
1 BW & 13.79 & 13.76 & 0.217 & 13.59 & 13.56 & 0.221 \\
1 FW & 13.79 & 13.76 & 0.217 & 13.97 & 13.94 & 0.215 \\
2 BW & 43.66 & 43.75 & 0.206 & 40.07 & 40.19 & 0.299 \\
2 FW & 43.66 & 43.75 & 0.206 & 46.90 & 46.96 & 0.127 \\
3 BW & 114.08 & 115.32 & 1.087 & 95.52 & 96.76 & 1.298 \\
3 FW & 114.08 & 115.32 & 1.087 & 131.63 & 132.67 & 0.790 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Backward and forward whirl (BW and FW) frequencies of a steel double-disc rotor.
# Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
## 2 Parameter Estimation with Kalman Filter
### Design of Adaptive Fuzzy Fading Kalman Filter  
The forgetting factor of AFKF is determined as the ratio of the calculated innovation covariance and the estimated innovation covariance in the \(k\)-step. If any component fault occurs abruptly, it causes a step-like change in the parameter value, and then the AFKF undergoes a significant uncertainty of the state transition matrix \(A_{k}\) instantaneously due to the incomplete model of Equation (5). In this case, AFKF will increase the forgetting factor to compensate for the effect of incomplete information. However, a large forgetting factor may result in the poor estimation performance, such as a longer estimation transition, in the transient phase of the filter, and eventually, the FTC based on the parameter estimation will be unstable. Therefore, it is necessary to regulate the forgetting factor to improve the estimation transition and its convergence time.  
In this paper, in order to improve the convergence property of AFKF, a fading algorithm based on the fuzzy system is proposed. Since \(\alpha_{k}\) is the random variable which is subject to uncertainties, it is necessary to use an approach that takes into account such uncertainties to derive relevant \(\lambda_{k}\). This can be achieved with a fuzzy logic approach with rules of the kind [15,16]:  
IF <_property_> THEN <_action_>where _property_ and _action_, are the form of \(\chi\in\mathrm{O}_{i},\kappa\in\Lambda_{i},i=1,2,\ldots\), respectively. Further, \(\chi\), \(\kappa\), \(\mathrm{O}_{i}/\Lambda_{i}\) are input variable, output variable, and fuzzy set, respectively.  
To implement the fuzzy fading algorithm, \(k\)-step \(\alpha_{k}\) and the change of \(\alpha_{k}\) (i.e., \(\Delta\alpha_{k}=\alpha_{k}-\alpha_{k-1}\)) are used. The input variable and \(\Delta\lambda_{k}\) are defined as the output of fuzzy inference system (FIS). \(\Delta\lambda_{k}\) denotes the adjust term as follows:  
\[\lambda_{kf}=1+\Delta\lambda_{k} \tag{18}\]  
Figure 2 depicts the algorithm of the proposed adaptive fuzzy fading Kalman filter (AFFKF). The fuzzy rule for the forgetting factor is defined in Table 1. The FIS is implemented using three fuzzy sets for \(\alpha_{k}\): S = Small, M = Medium, B = Big, three fuzzy sets for the change of \(\alpha_{k}\): S = Small, M = Medium, B = Big, and four fuzzy sets for \(\Delta\lambda_{k}\): Z = Zero, S = Small, M = Medium, B = Big. The membership functions, designed using a heuristic approach, are shown in Figure 3. The proposed AFFKF is an extension of AFFK with the fuzzy inference system that determines the appropriate forgetting factor.
## 4 SSB Signal Generation and SSBN Mitigation Techniques
### Iterative Linearization Filter Approach  
Another viable solution for improving the Rx-DSP performance is the iterative linearization filter and Rx-EDC implementation. The iterative linearization filter scheme can further help enhance the SSLF and two-stage linearization filter performance. In this approach, the detected signal waveform is initially stored in the memory as illustrated in Figure 6(c). Subsequently, the SSBN is determined according to the sideband-filtered signal. Then, the resulting signal can be subtracted from the stored signal waveform so as to alleviate the SSBN [57; 58]. The procedure will then be iterated several times with the intention of accomplishing a maximum compensation gain. Furthermore, based on the iterative linearization filter, an identical BER performance can be realized with the Rx-EDC and Tx-EDC. Although significant compensation performance improvement can be offered by the scheme, it presents additional DSP complexity compared to the single- and two-stage linearization filters methods. This can be mainly attributed to the required multiple iterations [29; 118]. Besides the significant latency that can be incurred, the associated iterations can considerably increase the DSP power consumption [50]. One means of addressing this is by combining a tunable frequency gap between the data sideband and the optical carrier. This will help alleviate the estimation burden of the iterative detection and flexibly improve the system performance within reasonable iterations [118]. However, this could be at the expense of the SE.
# An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent
## 1 Introduction
### State of the Art
#### 1.2.5 Hybrid Architectures and Metaheuristics  
Hybrid methods combine the advantages of multiple approaches, typically integrating heuristic algorithms with machine learning (ML) models to achieve higher performance. For example, in [26], an Adaptive Neuro-Fuzzy Inference System (ANFIS) is combined with genetic algorithms for multi-criteria optimization of regression test suites in the context of CI. In [27], the SBLA-AdaBoost CNN method was introduced, combining Side-blotched Lizard Algorithm-based optimization with an ensemble deep neural network to address TC minimization and prioritization in regression testing. Although this method demonstrates high accuracy in classifying faulty TC, it does not support the synthesis of new TC. The Law of Minimum (LoM) has recently been applied to test case prioritization to emphasize the influence of limiting factors such as method-level change impact on early fault detection. The authors [28] proposed a prioritization strategy based on LoM, which ranks test cases by the most constrained method coverage. However, they observed that such prioritization can lead to repeated execution of structurally similar test cases, potentially delaying the exposure of faults in under-tested regions. To address this, they introduced a dissimilarity-based enhancement using Jaccard similarity to penalize redundancy.
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 3 System Architecture  
This section presents the architectures of OFDM transceivers and the two proposed systems.