esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Shock Resistance Performance
): **(a)** theoretical results; **(b)** experimental results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & \multicolumn{2}{c}{**Theoretical Results**} & \multicolumn{2}{c}{**Experimental Results**} \\  & \(\xi\) & _c/(N\(\cdot\)s/m)_ & \(\xi\) & _c/(N\(\cdot\)s/m)_ \\ \hline LS & - & - & 0.0415 & 2103.6 \\ CS-6 mm & 0.0563 & 2903.6 & 0.0806 & 4128.8 \\ CS-8 mm & 0.0639 & 3303.6 & 0.0849 & 4355.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Damping performance of the three vibration isolation systems under shock excitation.
# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 3 Proposed Approach
### Email Parsing  
The process of extracting relevant information from emails and converting it into an organized format that computer systems can easily use and understand is known as email parsing. This procedure is especially helpful when dealing with large numbers of emails or when combining email data with databases and other applications. Email content must be broken down into its parts, such as the sender, recipient, topic, date, and message body, during the parsing process. Additionally, it may also include the extraction of certain data points, like order numbers, dates, names, or any other information pertinent to a given application [27]. Email parsing, in the context of phishing, is the process of extracting relevant data from each email, such as the body content (B), the header information (H), and the label indicating if the email is phishing or not (L).
## 3 Numerical Results and Analysis
### Effects of Spatial Frequency Characteristics
The reason is, as shown in Figure 5a, for this item's aberration, when the RMS of aberrations are the same, the higher the spatial frequency, the closer the wavefront center region of the beam is to the wavefront of the plane wave. The flatter wavefront makes the central area of the Airy spot formed on the focal plane more regular, and the matching degree with the optical fiber mode-field is higher, thus higher CE is achieved.
# Cracks in Arch Dams: An Overview of Documented Instances
## 2 Observational Data/Material: Summary of Case Studies
### Single-Curvature Thin Dams  
_Copper Basin_ dam (1938): From the very beginning, there were horizontal movements in the crest, which showed that the dam was moving upward rather than downward. In 1945, cracks were discovered, and the most important ones were related to discontinuitiesin the abutment rock. Between 1942 and 1955, the cumulative upstream displacement was 110 mm, but between 1955 and 1975, it was only 11 mm and stopped. Meanwhile, between 1942 and 1955, vertical displacements averaged about 90 mm (120 \(\upmu\)m/year) and between 1955 and 1995, they increased to 21 mm (10 \(\upmu\)m/year).  
Dam B: Only a few years after its operation start, cracks were detected. In the following years, such cracks continued to grow, so the dam operation was kept under constant surveillance with monitoring systems. After the implementation, the displacement along the crest was mostly enlarged, but over the years the displacement in the crest decreased, and the displacements turned more curved, reaching the maximum at about 20 m above the ground. Meanwhile, on the downstream side, the horizontal cracks arched toward the left and right sides of the dam.  
Gene Wash dam (1937): During the initial years of the dam's operation, deformations developed at a fast rate. Between 1945 and 1964, horizontal movement stopped and only seasonal displacements were recorded; the total displacement amounted to 106 mm. Between 1942 and 1965, vertical movements of 90 mm were recorded, while between 1965 and 1995, only 8 mm of movement was observed. However, the main cracks were related to discontinuities in the abutment rock.  
Tolla dam (1961): It was designed to be extremely thin to provide a full-scale check of the validity of theoretical analyses when applied to dams approaching the ultimate degree of slenderness. Severe cracks developed during the first filling of the reservoir. Over the next eight years, these cracks continued to grow, which led to the building of a new arch downstream and the construction of buttresses to reinforce the original arch.
## 3 Results
### Out-of-Domain Test
#### 3.2.1 Statistical Significance of the Results of the Out-of-Domain Test  
To test the significance of the results obtained by the different regression methods for the _Out-of-Domain_ study, we employed the same statistical test used above for the _Out-of-Sample_ study. The test returned a \(p\)-value = \(1.2065\times 10^{-156}\), rejecting the null hypothesis; thus, we proceeded with the post hoc test.  
The \(p\)-values obtained for the post hoc test are plotted as a heat map in Figure 2(a) for the _Out-of-Domain_ test. It can be seen that no significant differences (NS) exist between the performance of _Spline Continued Fraction_ (Spnl-CFR) and those of _Random Forest_ (rf) and _XGBoost_ (xg-b). There is no significant difference between the performance ranking of _Linear Regression_ (1-regr) and those of mlp, 1-svr, krnl-r, and grad-b.  
The Critical Difference (CD) graph for the _Out-of-Domain_ test is plotted in Figure 2(b). The Critical Difference (CD) is 1.3898. It is evident from the critical difference plot that the top three methods in _Out-of-Domain_ prediction are _Spline Continued Fraction_, _XGBoost_, and _Random Forest_. It can be seen that the average ranking of Spln-CFR is very close to second, which is the best-ranking performance among the ten regressors. There is no significant  
Figure 2: Statistical comparison of the regressors for the _Out-of-Sample_ test: (**a**) heat map showing the significance levels of the \(p\)-values obtained by the [PERSON] post hoc test and (**b**) critical difference (CD) plot showing the statistical significance of the rankings achieved by the different regression methods.  
difference between _Spline Continued Fraction_ and the second-best method, _XGBoost_ (xg-b), which has an average ranking between second and third for _Out-of-Domain_ prediction.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 6 Open Research Challenges and Future Research Directions
### Integration of 5G and Beyond  
As next-generation cellular networks continue to evolve, integrating 5G and beyond into V2V communication systems presents significant opportunities for improving performance, reliability, and scalability. Research efforts should focus on optimizing V2V communication protocols to leverage the capabilities of 5G networks, such as low latency, high bandwidth, and network slicing.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Aspects** & **DMS** & **CI-V2V Hub** & **V2V-EC** & **V2V BEN** & **V2V HCAN** & **V2V AI-DN** \\ \hline Latency & Low & Moderate & Low & Moderate & Moderate & Low \\ \hline Reliability & High & Moderate & High & High & High & High \\ \hline Data Processing & Decentralized & Centralized & Decentralized & Decentralized & Hybrid & AI-driven \\ \hline Scalability & High & High & High & Moderate & High & High \\ \hline Implementation Cost & Low & High & Moderate & High & Moderate & Moderate \\ \hline Interoperability & Moderate & High & High & Moderate & High & High \\ \hline Infrastructure Dependency & Low & High & Moderate & Moderate & High & Low \\ \hline Use Case & Ideal for rural and sparse & Suitable for urban areas & Ideal for latency-sensitive applications & Suitable for high-security applications & Suitable for mixed urban environments & Ideal for complex and dynamic urban environments \\ \hline \hline \end{tabular}
\end{table}
Table 1: V2V Architecture and their Contributions for ITSs.
## 3 Results and Discussion
### Correlation between FT-IR and UV-Vis Spectroscopies  
The obtained results from UV-Vis spectroscopy measurements of the degraded oil samples were correlated with the values of the degradation parameters analyzed by FT-IR spectroscopy (Figure 10). The oxidation, sulfation, and nitration levels presented a good correlation and linear trends with a \(R^{2}>0.9491\), and the correlation with the chemical properties of the degraded oil gives support and confirms the effectiveness of the sensing device to detect and monitor the evolution of the engine oil degradation process. Since engine oil analysis by FT-IR is a well-established method to evaluate oil condition with critical limits defined in the literature, the oxidation, sulfation, and nitration levels can be described by the displacement of the LSPR band given the good correlation between both methods. Based on the above and considering the linear trends obtained from the different measurements of the specific samples under study, which are in the service life interval of the lubricant recommended by the manufacturer, it is possible to propose that a displacement of the LSPR band beyond 590 nm, using the proposed sensor, will indicate that the lubricant has reached the end of its useful life. However, it is important to mention that the results obtained with this prototype are specific for the system under study and cannot be extrapolated to different vehicles, lubricants, or mileage intervals.  
Figure 9: Displacement of the LSPR band position with mileage. Red line represents a linear fit of the experimental data.
## 5 Selection of Attributes, Modeling, and Results  
Due to the large number of new attributes derived from satellite data, attribute selection, and dimensionality, reduction of the input attribute space was needed. Prior to this process, all input attributes were normalized to a uniform range: interval <0, 1>. This ensured that all input attributes were equally treated, since some attribute selection methods are sensitive to this aspect. In the first round of experiments, neither the attribute selection based on correlation coefficient (in which the attributes showing the highest absolute value of Pearson or Spearman correlation with the target attribute were used), nor dimension reduction on the basis of principal component analysis (PCA) ensured satisfactory results. Models trained as such actually achieved lower or similar accuracy as before. This failure stemmed from the fact that high correlation with the target attribute can indicate the impact of a given input attribute, but only individually, in isolation from others, because their mutual correlation is ignored. Therefore, combining such input attributes need not increase the resultant model accuracy at all, if they are strongly correlated. On the other hand, the failure of PCA stemmed from the opposite reason: it is an unsupervised method, and can give no guarantee that its main components would be strongly correlated with the target attribute. For these reasons, we had to select input attributes differently, by applying methods that took into account their mutual correlations and could correctly gauge their joint effect on the target attribute. After some preliminary trials, we chose the relief method [20], FSRF-Test [21], and forward selection [22], whose applications resulted in significant improvements in the accuracy of trained models. Selected attributes were incrementally added in groups of five; therefore, the resultant models used 5, 10, 15, or 20 input attributes. We illustrate these steps in Figure 4, which represents a more detailed elaboration of the Data Preparation, Modeling, and Evaluation phases of the CRISP-DM model depicted in Figure 3.  
In the following Tables 2-4, we present the best results that we achieved for the three target attributes S, Sr, and g02, respectively, in terms of the correlation coefficient between their true and predicted field-measured values, their relative absolute error and their root relative squared error.
###### Abstract
Another important selection parameter is the type of forecasting, which may be classified into three main categories according to [PERSON] et al. [30]: (i) short-term forecasting of PV power generation that is applied in scheduling and dispatching of electrical power, or in the design of a PV-integrated energy management system, (ii) medium-term forecast, carried out for periods from one week to one month for power system and maintenance scheduling, and (iii) long-term PV power forecasting for periods from one month to one year, which is helpful for the planning of electricity generation and distribution [30]. As expected, long-term PV power forecasting is the most demanding task for the use of machine learning methods, which need a lot more research to understand their complications and transform them into efficient computational tools [31]. The experience from the application of deep learning in other related fields is useful in this context. [PERSON] et al. [32] constructed a long short-term memory (LSTM) RNN for making predictions of French nuclear power plants' steam generator output over a long-term horizon, in which the network hyperparameters are automatically optimized by a tree-structured Parzen estimator (TPE) algorithm. RNNs include feedback connections from the hidden/ output layer to the preceding layers, to catch the dynamics of sequential data and reproduce previous patterns in the future prediction. Similarly, RNNs are applied in long-term forecasting of lithium-ion battery performance [33], membrane degradation in fuel cells [34], rolling bearings remaining useful life [35], and machine health monitoring [36]. The current work belongs to long-term power forecasting. It focuses on the exploitation of actual data collected from monitoring grid-connected PV systems and has three objectives. The first objective is the comparison of an ANN model with a simple linear regression in PV energy forecasting using actual data. The second is the evaluation of the data in order to be useful either for forecasting or for fault diagnosis and degradation analysis. The third is to create a methodology for the performance evaluation of a PV system and reliable estimation of degradation rates, which is essential information for the creation of energy forecasting models. The rest of the manuscript is organized as follows: Section 2.1 describes the photovoltaic park's details and the experimental setup employed for its monitoring. Section 2.2 describe the pre-processing and Section 2.3 introduces the forecasting methods, whereas the results are presented and compared with actual data in Section 3. In Section 4, a comparative discussion of the performance of the alternative models and training approaches is carried out, and finally, conclusions are drawn on the long-term prediction and fault diagnosis capabilities.
# Numerical Investigation on Handling Stability of a Heavy Tractor Semi-Trailer under Crosswind
## 2 Numerical Method and Validation
### Validation Case of Heavy Tractor Semi-Trailer Model  
The model studied was a full-scale heavy tractor semi-trailer (see Figure 2) with a length, width and height of 16,903, 2438 and 3988 mm, respectively. Some details such as the engine room under the cabin, together with the radiator and other small parts around the engine, were ignored in the geometry by considering the complexities involved in mesh generation. A flat surface was added to the wheel surface adjacent to the ground, and the body surface was smoothed to save the computational resource.  
To validate the selection turbulence model, drag coefficient, lateral force coefficient, lift coefficient and yawing moment coefficient were compared with experimental results measured at the HD-2 Boundary Layer Wind Tunnel (HD-2 BLWT) at Hunan University and also the results from [PERSON] [34]. The 1/6-scale truck model is used in the wind tunnel displayed in Figure 3. The numerical simulation model used in the current work is the same as that used by [PERSON] and the experiment. The setup of the boundary conditions is also consistent with [PERSON]'s work. The boundary condition of the computational domain and grids resolution are delineated in Figures 4 and 5. Inlet 1 and inlet 2 are the velocity inlets, while outlet 1 and outlet 2 are set to the pressure outlet. In order to achieve the target of high precision, the domain was discretized by an unstructured trimmer grid in STAR-CCM+. In consideration of the large amount of separation around the tractor trailer, the resolution around the vehicle ranged from 25 to 40 mm, and two refinement regions were applied around the vehicle.  
The aerodynamic coefficients at different relative inflow angles are plotted in Figure 6 and show that the computational results are in agreement with the experimental and [PERSON]'s results. Among them, the aerodynamic lift coefficient obtained from the simulation is slightly different from the comparison experimental data and [PERSON]'s results, which may be caused by the difference between the ground clearance of the truck model in the numerical simulation and the wind tunnel test. In general, the selected turbulence model and computational setup could capture the three-dimensional, turbulent and incompressible flow field around the truck.  
Figure 2: Validation model.  
Figure 4: Boundary condition of computational domain.  
Figure 5: The longitudinal section of the grids.  
Figure 3: 1/6-scale wind tunnel test model.  
Figure 6: The comparison between experimental and computational results: (**a**) drag coefficient; (**b**) lateral force coefficient; (**c**) lift coefficient; (**d**) yawing moment coefficient.
# Research on the Robustness of Active Headrest with Virtual Microphones to Human Head Rotation
## 4 Experiments
### Analysis of Acoustic Transfer Responses  
The variation of acoustic transfer responses of the system was analyzed first. Transfer responses \(S_{vij}\) for virtual plants and transfer responses \(G_{ij}\) between the virtual microphone locations and the physical microphone locations in the primary field with respect to the angle of human head rotation at 250 Hz are shown in Figure 14, where \(i,j=1,2\). It can be seen from the four curves with marker in blue and red that the maximum variation of the transfer responses \(S_{vij}\) is about 17.9 dB in magnitude and 3.1 rad in phase, greater than the 6.2 dB and 1.0 rad in the simulation results, a variance which is related to the reflections in the room. The variation tendency between the simulated and measured results is similar in most cases, however, due to room reflections, the amplitude of the measured transfer responses increases with human head rotation angles greater than approximately 45 degrees, which is not consistent with the simulation results.  
The transfer responses \(G_{ij}\) are shown as the teal and black curves without marker, with a maximum variation of about 11.7 dB in magnitude and 1.1 in phase, which is less than the transfer responses \(S_{vij}\). The transfer responses \(S_{vij}\) for physical plants are given in Figure 15, where it can be seen that the transfer responses \(S_{vij}\) remain almost constant as head rotates, with a variation of less than 1 dB in magnitude and 0.4 rad in phase. These results are consistent with the analyses in Section 3.1 and verify that the transfer responses of the virtual plants vary more with the head rotation than other transfer responses.  
Figure 13: Diagram and photographs of the experimental setup (**a**) diagram of the experimental setup, (**b**) photograph of the ANC system, (**c**) photograph including primary source.
## 1 Introduction
In this paper, we first drive the exact analytical formulations of the statistical characteristics of univariate and bivariate \(\alpha\)-\(\mu\)/IGA distributions and compare the differences between the \(\alpha\)-\(\mu\)/IGA and the \(\alpha\)-\(\mathcal{F}\) models, as well as a new mixture \(\alpha\)-\(\mu\) model and the mixture IGA model. Then, capitalizing on the obtained statistical expressions, the PDF, the CDF, and the MGF of the sum of \(\alpha\)-\(\mu\)/IGA variates are derived in the i.n.i.d. and the correlated scenarios in the light of the multivariate Fox's H-function by utilizing the Mellin-Barnes type contour integral, respectively. At the same time, some exact approximated expressions are also deduced by using the mixture \(\alpha\)-\(\mu\) model. Thirdly, some performance metrics of interest for the single-link and MRC systems are investigated, respectively, including the outage probability (OP), the average bit/symbol error probability (ABEP/ASEP), and the average channel capacity and the effective rate. In addition, the asymptotic analysis of the ABEP of DPSK (Differential Phase Shift Keying) and the OP are considered at high signal-to-noise (SNR) regions. Finally, the numerical analysis and simulations are carried out to confirm the accuracy of our theoretical derivations in various scenarios. The remainder of this paper is organized as follows: In Section 2, the statistical features of the univariate and bivariate \(\alpha\)-\(\mu\)/IGA distributions are investigated, and some comparisons are discussed. Some exact and approximated analytical expressions of the statistical properties of the sum of \(\alpha\)-\(\mu\)/IGA variates are obtained in Section 3. Section 4 derives the analytical expressions of performance metrics including the OP, the ABEP/ASEP, the channel capacity, and the effective capacity. In Section 5, we show some asymptotic analysis by using several examples in high SNR regions. Then, the numerical evaluations and simulations are performed and discussed in Section 6. Finally, we conclude the whole paper in Section 7.
## 3 Materials and Methods  
### Dataset and Preprocessing  
The dataset used for this research was obtained from participants that took part in an online digital survey powered by Videra Health. The survey contained a total of 22 questions including a combination of traditional self-assessed measures of anxiety and depression, PHQ-9 and GAD-7, mixed with video recordings of participants responding to open-ended questions. Participants were given up to 3 min to respond to each question.  
Participants' videos were manually reviewed, and were approved if their videos met a minimum quality standard. For approval, a participant video needed to clearly show the participant's face, contain discernible speech, and contain a minimum of 50 spoken words throughout their entire session.  
The questions for the video-based sections of the survey were modeled on the PHQ-2 and GAD-2 questionnaires. The questions were modified to encourage open-ended responses, prompting participants to tell descriptive stories about their experiences over the course of the past two weeks. A general question that did not specifically ask about anxiety or depression was also included, with the intent to predict both depression and anxiety from a single question.  
The questions were given in the following order:  
* One general video question
* Nine multiple choice questions (PHQ-9)
* Two open-ended video responses related to depression
* Seven multiple choice questions (GAD-7)
* Two open-ended video responses related to anxiety  
The initial dataset included a total of 1149 participants that responded. The total number of participants after manual review was 955, with 194 participants being rejected for not meeting the minimum quality standards described above. A total of 4775 videos were used in the final dataset. This included a total of 61.1 h of video with 533,403 words (an average of 579 words per participant).  
Models were trained with five-fold cross validation stratified by the maximum severity bucket of each participant, to ensure that each fold had an equal balance of all severity levels of scores. The survey was re-administered 7 months later to generate a validation set that was temporally distinct from the initial survey; this dataset included a total of 337 participants, 1685 videos and 308,243 words. Videos were then transcribed, audio was extracted into waveforms, and images of facial features were processed.