esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theory of HSDTPEs
### Equilibrium Equation  
From Equations (14), (24), (30), (31), and (32), the total potential energy of a plate is obtained as:  
\[\Pi=\sum\Bigl{(}\Pi^{e}+\Pi^{l}+\Pi^{b}+\Pi^{f}+\Pi^{m}\Bigr{)} \tag{35}\]  
The variation of total potential energy \(\Pi\) results in the following discrete equation:  
\[\frac{\partial\Pi}{\partial\mathbf{a}}=\sum\Bigl{(}\mathbf{K}^{e}\mathbf{a}^{ e}+\mathbf{K}^{l}\mathbf{a}^{l}+\mathbf{K}^{b}\mathbf{a}^{b}-\;\mathbf{F}^{ad}- \mathbf{F}^{m}\Bigr{)}=0 \tag{36}\]  
where  
\[\mathbf{K}^{e}=\int_{-h/2}^{h/2}\left(\iint\limits_{\Delta e_{i}}\mathbf{B}^{ \mathrm{T}}\mathbf{D}\mathbf{B}dx\;dy\right)dz \tag{37}\]  
Figure 6: Moment boundary condition.  
\[\mathbf{K}^{l}=\frac{1}{d}\int_{-h/2}^{h/2}\left(\int_{-l/2}^{l/2}\left(\mathbf{N }^{l}\right)^{\mathrm{T}}\mathbf{D}^{l}\mathbf{N}^{l}\;ds\right)dz \tag{38}\]  
\[\mathbf{K}^{b}=\frac{1}{d}\int_{-h/2}^{h/2}\int_{-l/2}^{l/2}\biggl{[}\left( \mathbf{\overline{N}}^{\prime}\right)^{\mathrm{T}}\mathbf{D}^{b}\mathbf{ \overline{N}}^{\prime}+\left(\mathbf{\overline{N}}^{\prime}\right)^{\mathrm{T }}\mathbf{D}^{b}\mathbf{\overline{N}}^{\prime}\biggr{]}ds\;dz \tag{39}\]  
\[\mathbf{F}^{cd}=\iint\limits_{\Delta e_{m}}\left(\mathbf{N}^{e_{d}}\right)^{ \mathrm{T}}\mathbf{f}_{0}\;dx\;dy \tag{40}\]  
\[\mathbf{F}^{em}=\int_{-l/2}^{l/2}\left(\mathbf{\lambda}^{m}\mathbf{\tilde{N} }^{e_{m}}\right)^{\mathrm{T}}\mathbf{M}_{0}\;ds \tag{41}\]  
Assembling the above stiffness matrix and force vector, the equilibrium equation for a plate is then obtained as:  
\[\mathbf{K}\mathbf{\cdot}\mathbf{U}=\mathbf{F} \tag{42}\]  
where \(\mathbf{K}\) is the global stiffness matrix, \(\mathbf{F}\) is the force vector, and \(\mathbf{U}\) is the vector of DOFs to be solved.  
As described in [PERSON] et al. [4], the shear-locking problem could be well and naturally solved because the bending angles of rotation and shear angles are treated as independent variables in HSDTs. The regular full integration can be applied to make HSDTPEs valid for the thick-thin plates for the computation of Equation (42), for instance, seven quadrature points for each triangular element [57], four Gauss quadrature points for transverse direction \(z\) (where the analytical integration can also be applied for the direction \(z\)), and four Gauss quadrature points for the local direction \(s\) of each fictitious layer are used for the integration of the TrSDTPE using the third-order shear function \(g(z)\)[58; 59; 60].
# Process Development of Low-Loss LPCVD Silicon Nitride Waveguides on 8-Inch Wafer
## 1 Introduction
The loss of the final silicon nitride waveguide is \(2.1\) dB/cm @1550 nm. This is still much higher than that of the silicon nitride waveguide prepared by LPCVD of the same size. Secondly, there are also reports that in order to reduce the stress of the silicon nitride film deposited by LPCVD [21], the gas ratio of SiH\({}_{2}\)Cl\({}_{2}\) and NH\({}_{3}\) was adjusted so that the deposited film was a silicon nitride film rich in silicon. Although this method reduced the stress of the silicon nitride film, it also changed the material properties of the silicon nitride film, making it closer to silicon material, which led to the loss of some advantages of the standard stoichiometric ratio silicon nitride film. The loss of the final silicon nitride waveguide was \(1.5\) dB/cm @1550 nm. In addition, some new special structures of silicon nitride waveguides have also been reported [22; 23]; although these waveguides have low loss, the limiting capability of the optical field is weak, the critical bending radius is generally large (\(\geq\)500 \(\upmu\)m) for integration, and the process is complex. Recently, an interesting idea was proposed: to increase the confinement ability of the waveguide to the light field by integrating it with other materials [24; 25]. Still, because of our current conditions, some related explorations can be carried out in the future. Therefore, the preparation of low-loss and high-limiting-capacity silicon nitride waveguides still starts from the direction of how to prepare thick standard stoichiometric ratio silicon nitride waveguides via the LPCVD process. In order to solve the above problem, we proposed a method and successfully fabricated silicon nitride waveguides with a thickness of 400 nm by LPCVD on an 8-inch wafer in accordance with standard stoichiometric ratios. The silicon nitride waveguides prepared via this process showed significant advantages of low propagation loss and small critical bending radius. The measurement results show that the propagation loss is only 0.157 dB/cm at 1550 nm and 0.06 dB/cm at 1580 nm; for a bending waveguide with a bending radius of 50 \(\upmu\)m, the bending loss is only 0.013 dB, and for a bending radius of 80 \(\upmu\)m, the bending loss is only 0.0095 dB. This greatly improves the integration of the optical chip and achieves higher economic value.
One of the most important parts of sea ice monitoring is to distinguish sea ice from water, which can be regarded as a classification problem. As an ML method, the decision tree (DT) method has been widely applied to sea ice monitoring [41; 42]. Another powerful ML algorithm employed for classification is random forest (RF), which creates a variety of individual decision trees that operate as an ensemble [43]. Although the DT and RF algorithms have been applied to monitor sea ice using satellite remote sensing data, such as MODIS and CryoSat-2, there is a lack of information about how DT and RF algorithms can be utilized for monitoring sea ice using spaceborne GNSS-R data. The task of this study is to explore the potential application of spaceborne GNSS-R to distinguish sea ice from water using the DT and RF classifiers. Section 2 firstly gives the description of datasets used in this study and the extraction of features. Then, the sea ice monitoring approaches based on DT and RF algorithms and data processing flow are presented in Section 2. The sea ice monitoring results are presented and discussed in Sections 3 and 4, respectively. Finally, the conclusions are addressed in Section 5.
# Spam Review Detection Techniques: A Systematic Literature Review
Recall parameter can be computed only on positive or negative reviews that have been properly classified. Therefore, positive\(\_\)recall is defined by the following equation:  
\[\text{positive\_recall}=\frac{\text{\# positive\_well\_ classified}}{\text{\# total\_positive\_reviews}}\]  
where \(\text{\# total\_positive\_reviews}\) represent those reviews that were originally considered as positive within the dataset, and \(\text{\# positive\_well\_classified}\) is the number of positive reviews submitted to the spam review detection model and labelled as positive. Moreover, negative or neutral reviews are computed in the same manner. A false positive error occurs when the proposed model identifies reviews as spam that are not-spam, and a false negative error occurs when there is a failure to identify reviews as spam that actually are spam. Moreover, it was observed in the literature review that Precision is affected by the number of false positive errors and Recall is affected by the number of false negative errors. For evaluation purposes, AUC (Area under the receiver operating characteristic curve) is also employed to evaluate the accuracy of classification models [35]. Moreover, to draw a receiver operating characteristic curve (ROC), only the true positive rate (TPR) and false positive rate (FPR) are needed. The TPR defines how many correct positive results occur among all positive samples available during the test, while FPR on the other hand defines how many incorrect positive results occur among all negative samples available during the test. Therefore, ROC space is defined by FPR and TPR as \(x\) and \(y\) axes. AUC and ROC are standard measures for evaluating the quality of machine learning algorithms. Lift curve is used to visualize the performance of spam detection models and to predict outlier reviews [28]. Lift curve is also used for class distribution. It is observed from existing studies that researchers also used Rank Normalized Discounted Cumulative Gain to evaluate a spammer's individual features, e.g., rating, review text, etc. ## Discussion  
Accuracy is a basic measure to evaluate spam review detection methods. Previous works show that it is not practical to implement spam review detection methods without training [83]. The learner's accuracy is much higher when trained on real-world datasets [84]. Table 15 shows that most of the existing studies used precision, f-measure, accuracy and ranked normalized discounted cumulative gain parameters to evaluate the performance of spam review detection methods. Review of the state-of-the-art research shows that supervised learning methods mostly use the area under the ROC curve to evaluate the accuracy, whereas the lift curve is used to visualize the performance and outlier review of the methods.
## 5 Experiments and Results
### Objective Evaluation  
The results are presented in Figure 6. The JGMM method revealed the best performance in terms of MCD for all conversion pairs. This result was due to the MCC minimization criterion adopted in the JGMM method. In terms of PD and PESQ, the proposed VC method was superior to the other methods for all conversion pairs. This results can be explained by the fact that the objective function for the proposed VC method was similar to that adopted in calculating the PESQ. The original purpose of the PESQ was to perceptually compare the overall quality of clean (untouched) speech with that of reconstructed (or distorted) speech [25; 30]. The role of the PESQ in distinguishing the voices of different speakers has not been discussed to date. Our assumption was that even if two different speakers uttered the same sentence and the two voices were time-aligned using DTW and SOLA, the PESQ between the two voices would be very small. This assumption was verified by the experimental result  
Figure 5: Average PESQs for each conversion pair according to the weight for the asymmetrical disturbance.  
wherein the average PESQs between the reference voices and time-aligned target voices were 0.922, 0.581, 1.084, and 0.614 for rms \(\rightarrow\) bdl, bdl \(\rightarrow\) slt, slt \(\rightarrow\) clb, and clb \(\rightarrow\) rms conversions, respectively. Considering the range of PESQs is \(-0.5\) to 4.5 [30], these values are remarkably low, and hence, the PESQ is also a good indicator of differences in voice personality. The average PESQ of the proposed method was always higher than that between the reference and time-aligned target voices for all conversion pairs, as shown in Figure 6. Such improvements in PESQ mainly came from conversion to target speech, since no attempt to improve the quality was carried out on reference speech. The experimental results also showed that the correlation between the perceptual distance and the PESQ was \(-0.7315\), whereas the correlation between MCD and the PESQ was 0.4805. This was graphically verified by the scatter plots presented in Figure 7 where the perceptual distance is more clear correlated with the PESQ, compared with MCD. Consequently, the distance metric adopted in this study is more useful for the prediction of perceptual similarity to target speech by comparison with the previously employed distance metric.  
Figure 6: The objective evaluation results for each method, each conversion pair.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Impacts of V2X Technology
Additionally, based on the virtual driving experiment, [PERSON] et al. analyzed the impacts of two types of information, that are, respectively the continuous information such as velocity and the incident-based discrete information such as traffic accidents [24]. The results reveal that these two types of information have a positive influence on car-following behavior. To be special, affected by the information, the driver has a larger time-to-collision (TTC) and smaller deceleration, but a more intense tendency to continue driving when the traffic signal has turned to \"yellow\". The impacts of V2X technology on drivers in the car-following process were explored in the aforementioned works, and the results show that there are certain positive influences of V2X technology on car-following behavior in aspects such as safety, efficiency, comfort level, energy consumption, and emission. These improvements are essentially the expression of new characteristics of car-following behavior affected by V2X technology. Although the above works can provide an important reference for modeling the car-following behavior in the V2X environment, the specific impacts of V2X technology in the different development stages on car-following behavior and traffic flow along with the modeling efforts have become the research priority in the field of traffic flow theory currently and in the future.
# Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
## 2 Related Work
### Obstacle-Dependent Gaussian Algorithm  
The ODG determines the driving environment risk level. Therefore, a driving path is created safely in a low-risk place. Figure 9 illustrates how the ODG algorithm represents risk using the Gaussian in (17):  
\[O_{risk}(p_{{}_{Y}})=\omega\exp(-\frac{(p_{{}_{S_{Y}}}-p_{{}_{Y}})^{2}}{ \sigma^{2}}), \tag{17}\]  
where \(\omega\) is the risk at the obstacle location, \(p_{{}_{S_{Y}}}\) is the lateral position of obstacle, and \(p_{{}_{Y}}\) is the lateral road position. Finally, \(\sigma\) is the variance of obstacle movement.  
[PERSON] et al. [33] experimented by applying the ODG to avoid static and dynamic obstacles. Therefore, the ODG function can be generalized for driving environment obstacles, such as lanes and vehicles.  
The maximum risk to the center of a different target depending on the target state and the Ego vehicle state, \(E\), is defined as \(A_{k}\), and the risk of each situation is determined using the maximum collision risk. Let \(\sigma_{k}\) specify the extent to which the obstacle affects the boundary based on the width of \(S_{k}(W_{S_{k}})\) and \(E(W_{E})\) and the obstacle displacement.  
Figure 9: Obstacle-dependent Gaussian concept (\(\omega=1\) and \(\sigma^{2}=0.2\)).  
The reliability \(R\) (confidence interval level) is the extent to which the obstacle affects the surrounding environment (i.e., the extent to which the obstacle size and space move during the sample time may be within the range). This probability is calculated as a ratio of the ODG integration to the total size. Therefore, \(\sigma_{k}\) is required to calculate the \(R\) value in the Gaussian error function (\(ERF\)) in (20).  
\[\sigma_{k}=A_{k}/ERF^{-1}(R), \tag{18}\]  
\[A_{k}=\frac{W_{E}}{2}+\frac{W_{S_{k}}}{2}+\Delta p_{S_{y}}, \tag{19}\]  
\[ERF(x)=\frac{1}{\sqrt{\pi}}\int_{-x}^{x}\exp(-t^{2})dt, \tag{20}\]  
where \(\Delta p_{S_{y}}\) is lateral displacement of \(S_{k}\) in the sampling time.  
The ODG is defined for the line markers to prevent the vehicle from straying out of its solid and dotted lines (\(O_{l}\)) and is defined for the vehicles (\(O_{v}\)). Moreover, \(ODG_{Total}\) is the sum of the ODGs:  
\[ODG_{Total}(p_{Y})=\sum_{i}^{N_{l}}O_{l}+\sum_{i}^{N_{v}}O_{v,k}, \tag{21}\]  
where \(N_{l}\) is number of lines on the road, \(N_{v}\) is the number of surrounding vehicles, \(p_{Y}\) is the lateral road position, and \(O_{v,k}\) is the mean ODG of the \(k\)th surrounding vehicle.
## 3 Methodology
### Data Pre-Processing  
The main reason for testing several algorithms on the dataset was that their performance varies for the selected features. The study suggested that algorithms behave differently; depending on the dataset, the efficiency and performance may also vary. With this approach, it is easier to identify any one algorithm which suits the dataset with better accuracy and performance. For this purpose, a similar approach was used for this study. For the study, the Orange data mining tool was used in accordance with the process shown in Figure 4 [49].  
Pre-processing methods were used to transform raw data collected from these systems into an understandable form. Table 1 shows the features selected from SIS, where CGPA is converted into nominal order as excellent, very good, good, fair, adequate, or poor/fail; plagiarism count into low, medium, or high; coursework (CW)1 into pass or fail; CW2 into pass or fail; and end-of-semester evaluation (ESE) into pass or fail. Online activity was captured from Moodle in terms of minutes and is converted into nominal order as follows: activity on campus--low, medium, or high; and activity off campus--low, medium, or high. From eDify, four features--played, paused, likes/dislikes and segment--were selected, and discretization was applied on the continuous data to transform them into categorical data.  
The dataset was fed into the analyzer, and the select column widget was used to select the features from the available variables. Student ID is the meta attribute, and the result was selected as the target variable. The discretize widget was used to transform the variable into categorical data in order to be used in the study. The rank widget scoring technique was used for feature selection, which could be used further for prediction. Information gain, gain ratio, and the Gini decrease weight were compared for the performance of the prediction model. Furthermore, the investigation of the feature selection proceeded using the genetic algorithm, and principle component analysis (PCA) was used to further reduce the features. Multivariate projection was used as an optimization method that finds a linear projection and associated scatterplot that best separates instances of separate classes, uncovering feature interactions and providing information about intra-class similarities.  
Figure 4: Data mining process.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 5 Analysis and Discussion
### Computational Cost Evaluation  
In addition to robustness and imperceptibility, practical watermarking systems must also meet acceptable levels of computational efficiency, especially when applied to large-scale multimedia processing. To evaluate the feasibility of the proposed method, we analyzed the execution time and computational cost of each major component in the embedding and extraction pipeline, with results summarized in Table 8.  
The perceptual map computation--which includes multiscale Laplacian filtering, saliency estimation, and semantic masking--is the most computationally expensive component, accounting for approximately 45% of total embedding time. On CIF videos, this stage required around 1.6 s, while on HD content it reached 4.3 s. Despite its cost, this step is highly parallelizable, making it suitable for GPU acceleration.  
The keyframe detection and redundancy encoding stage contributed 15% of the overall cost, with average processing times of 0.5 s for CIF and 1.2 s for HD videos. This step is mostly lightweight and relies on histogram-based scene segmentation, which can be partially parallelized.  
The DCT coefficient estimation and QIM embedding stage, benefiting from spatial-domain simplifications, showed low computational overhead--0.4 s for CIF and 0.9 s for HD--representing only 10% of total cost. Similarly, the watermark extraction and voting stage can be completed in 0.7 to 1.6 s, depending on resolution, and is well-suited to parallel execution.  
Overall, the entire pipeline can be executed for low-resolution content and scales reasonably for HD videos. As detailed in Section 4.1., all experiments were conducted on a workstation equipped with an AMD Ryzen 9 5950X CPU and 64 GB RAM, with no GPU acceleration. The implementation used Python with OpenCV and NumPy, and was executed in a single-threaded configuration. Under these conditions, the average processing time for HD-resolution videos was ~7.8 s per frame, which corresponds to approximately 0.13 frames per second (fps). Although this is not sufficient for real-time streaming, the method was designed for offline applications such as copyright protection, tamper detection, and forensic validation--where robustness and precision are prioritized over execution speed.  
We also emphasize that this version is a research prototype and not hardware-optimized. With further engineering improvements--such as GPU-based semantic masking, frame-level parallelization, or approximation of perceptual maps--the processing rate can be significantly improved. Preliminary tests with parallel decoding and lightweight masking models suggest that throughput above 5 fps may be achievable on modern hardware.
# Assessing the Compaction Quality of Dolomitic Asphalt Pavements Using Ground Penetrating Radar
## 2 Description of Test Sections  
The studied sections are located in Szeged, Hungary. Two newly constructed two-lane residential roads, Section I. and Section II., were chosen to conduct the survey (Figure 1). The lengths of Section I. and Section II. are 220 m and 240 m, respectively, while the width of both sections is 6 m.  
Figure 1: The location of the studied residential road sections.  
In both asphalt mixes (AC 11 and AC 22), the aggregate rock type was dolomitic limestone, and the proportion of bitumen was 4.1% by weight. The unbound granular base was also composed of dolomitic limestone [12].  
AC 11 and AC 22 are common asphalt mixes (AC) used in road construction, with the numbers indicating the grading of the aggregates; AC 11 has smaller aggregates than AC 22. AC 11 is typically used for wearing courses, while AC 22 is used for binder and base courses. These mixes are standard in Europe for creating durable, high-performance pavements.