esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Transthoracic Echocardiography  
An experienced echocardiography (L.F.) performed all image acquisitions using a GE Vivid E95 ultrasound machine (GE Healthcare, Horten, Norway) provided by a 3.5 MHz transducer. LV quantitative analysis was conducted in accordance with guidelines [12]. LV mass and relative wall thickness were calculated using a two-dimensional parasternal long-axis view. LV mass was indexed to the patient's height elevated to 2.7, while the left atrial (LA) volume was indexed to the body surface area. LV ejection fraction (LVEF) was determined using the biplane method computing the LV end-diastolic and end-systolic volumes in apical four- and two-chamber views. LV diastolic function was measured according to current recommendations [13]. Global Longitudinal Strain (GLS) was assessed offline using a workstation (EchoPAC only software version 113, GE Healthcare, Horten, Norway), with images taken in the apical views (three-chamber, four-chamber, and two-chamber). An automated 2D strain software with manual adjustment capability was used to trace endocardial and epicardial borders. Peak longitudinal strain was measured for the basal, mid, and apical segments for each wall, obtaining a 16-segment bullseye. GLS was obtained as a mean of all peak strain before the aortic valve closure. The reproducibility of GLS was previously published [14; 15]. The day-to-day variability in the measurements of parameters investigating LV diastolic function was previously assessed in our laboratory by calculating intraclass correlation coefficients [16]. Our results indicated excellent reliability of the estimates of LV diastolic function.  
RV Systolic Function  
Tricuspid annular plane systolic excursion (TAPSE) was measured to assess RV systolic function. Pulmonary arterial systolic pressure (PASP) was estimated in accordance with current guidelines; tricuspid regurgitation peak velocity was added to an estimate of right atrial pressure (RAP). RAP was estimated from the measurement of the size and the respiratory reactivity of the inferior vena cava (IVC): (a) RAP = 5 mmHg when IVC diameter is <2.1 cm and has normal inspiratory collapse; (b) RAP = 10 mmHg when IVC diameter is >2.1 cm or when it collapses <50%; (c) RAP = 15 mmHg when IVC is both dilated and collapses <50%; (d) RAP = 20 mmHg when IVC is dilated and has no visible collapse [17].
## 2 Materials and Methods
### Sampling Methodology  
The sampling locations were determined utilizing the Geographic Information System (GIS) technique, specifically the ArcGIS Pro software, version 3.2.2 (Esri Inc., Redlands, CA, USA) employing the \"Create Random Points\" tool. Several parameters were considered in the generation of the sampling points, including land use (arable land and grassland), soil types (Cernisoil, Hidrisoil, and Luvisosil), slope inclination, and slope aspect (representing slope orientation as a proxy for solar radiation exposure). Additionally, the degree of landscape fragmentation was considered to prevent the selection of plots that overlap with existing torrential valleys. Consequently, 48 sampling points were generated, with 20 points situated in arable land and 28 in grasslands (Figure 2). The number of sampling points was proportional to the distribution of soil types in the study area: Cernisolis (2053.16 ha), with 12 sampling points, Hidrisoils (4635.45 ha), with 17 sampling points, and Luvisosil's (5436.30 ha), with 19 sampling points.  
For the soil sampling process, an auger with an internal diameter of 20 mm and a sample tube length of 60 cm with division markings from 10 to 10 cm were used. Three cores separated by 50 m were extracted at each of the 48 sampling points so that average values could be obtained; 144 cores were taken in total. The auger facilitated the collection of samples to a depth of 60 cm, and each core was split into 10 cm subdivisions, meaning that six samples were extracted from each soil core, and a total of 864 samples were extracted overall.
# Study on Passenger Comfort Based on Human-Bus-Road Coupled Vibration
## 6 Conclusions  
The three-dimensional human-bus-road coupled vibration system was established to study the dynamic response and human comfort. Parametric studies were also conducted to investigate the effects of road surface roughness, interlayer bonding condition, bus weight, bus speed, and sitting position. The following conclusions can be drawn.  
In order to study the interaction between human, bus, and road, the human-bus-road coupled vibration system is proposed, including the bus model, the parallel biomechanical human model with 2 DOF, and road surface roughness condition. The proposed coupled model was then used to study the dynamic response of the system and the comfort evaluation of the human body.  
In the comfort evaluation, the annoyance rate based method is proposed to consider the randomness of passenger vibration, the difference of the psychosensory vibration, and the fuzziness of evaluation indicators. Compared to the fuzzy evaluation based on the ISO 2631 standard, the proposed annoyance rate based method can give out a quantitative evaluation of human comfort. Not only the degree of comfort can be evaluated, but the percentage of people feeling uncomfortable can also be obtained.  
The effects of interlayer bonding conditions of the pavement, bus weight, bus speed, and sitting position on the passenger comfort were also investigated. Among all the parameters, surface road roughness has the most significant effect on human comfort.  
Conceptualization, G.W., J.Z., and X.K.; methodology, J.Z. and X.K.; investigation and result analysis, J.Z. and X.K.; writing--original draft, J.Z.; supervision and writing--review and editing, G.W. and X.K.; project administration, G.W. and X.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China youth fund (51408557).  
The authors declare no conflict of interest.
## 3 Application Results
### Data-Based Classification Approach (Approach D)  
The results of the evaluation using the raw pressure data set are shown in Figure 12 (left). The five rows in Figure 12 (left) show the gradual changes in the regular states for different characteristic values. Obviously the rate of erosion increases (color changes from blue to red). These results also agree with human classification of the condition changes in this data set. Thus, based on a predetermined set of test data (derived from the real data set), any streamed data can be efficiently evaluated online. The results are summarized in Figure 12 (right), thereby demonstrating the progression of the states and thus increasing wear. Based on the detailed analysis, it can be concluded that the surface friction condition can recover in short time, and damage was already very significant at the beginning. According to practical experience, it is known that the surface conditions change constantly, layers are removed, new layers are established by internal processes, and a short-time stable state arises again. However, the material is ultimately exhausted, the entire surface is destroyed, and the formation of a wear-resistant surface is not possible. During the test run, the data were evaluated using the fuzzy-based model.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 5 Results and Discussion
### Experimental Results and Evaluation

Table 3: Average speaker identification performance using SUSAS dataset. Results in the above tables show that the proposed RBFNN-CNN outperformed all classical and deep learning models in both Emirati and SUSAS datasets. To prove that the proposed model is significantly better than other models, we conducted a statistical test between the proposed model and other models. We chose the non-parametric Wilcoxon test [43] because data were not normally distributed. The reported \(p\)-values were less than 0.05, which indicated that the proposed model was statistically significant. On the other hand, based on classical classifiers only, SVM surpassed other classifiers using the Emirati dataset; while MLP was the winning model with the SUSAS dataset. Furthermore, the CNN_BILSTM model proved to be the winning model in both datasets after the proposed RBFNN-CNN model. Based on these results, MLP had better performance as compared to the other classifiers using the SUSAS speech corpus. However, the results attained based on SVM were better than those achieved based on each of the MLP and KNN classifiers using the Emirati-accent dataset. Therefore, we can conclude that there is no rule that states the superiority of a specific classifier over another in any classification problem. Furthermore, the neutral talking environment was noted for the best accuracy followed by slow, soft, fast, loud, and shout, respectively. Based on Tables 1-3, it is evident that our proposed model outperformed the models in previous studies.
## 4 Results
### Performance Analysis of All Five ML Models on Texture Dataset DS2  
In this experimental protocol, first, we apply all five ML models to the texture dataset DS2 without using the cross-validation technique. We then use the same ML model with the K5-CV approach. In the DS2 dataset, the number of attributes is greater, so we use the co-relation technique and obtain a better result. We see that the ML-SVM model gives the best result on DS2 without K5-CV. This result is in the form of ACC: 82.72, F15:88.79, SEN: 90.05, SPC: 97.67, PPV: 81.04, and NPV: 91.74. Similarly, all five ML models are applied on DS2, but, for the training and testing of the models, the data are divided using the k-fold  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **ML Classifier** & \multicolumn{6}{c}{**Performance Metric (\%)**} \\ \hline \hline
**DS1** & & **ACC** & **F15** & **SE** & **SP** & **PPV** & **NFV** \\ \hline \multirow{6}{*}{Without} & Logistic Regression & 97.09 & 83.02 & 87.30 & 98.20 & 87.50 & 98.19 \\  & Decision Tree & 96.16 & 82.53 & 86.88 & 97.76 & 80.00 & 98.00 \\  & Random Forest & 97.72 & 86.81 & 89.6 & 98.84 & 89.00 & 98.14 \\  & K-Nearest Neighbors & 99.12 & 99 & 99.02 & 99.48 & 99.00 & 99.38 \\  & Support Vector Machine & 98.55 & 97.74 & 97.75 & 99.24 & 97.85 & 98.15 \\ \hline \multirow{6}{*}{With} & Logistic Regression & 97.46 & 84.92 & 91.16 & 98.9 & 89.64 & 98.69 \\  & Decision Tree & 97.20 & 86.87 & 88.00 & 98.44 & 92.16 & 98.51 \\ \cline{1-1}  & Random Forest & 97.88 & 90.44 & 93.69 & 98.97 & 91.56 & 98.84 \\ \cline{1-1}  & K5-CV & K-Nearest Neighbors & 99.89 & 99.14 & 99.35 & 99.94 & 99.85 & 99.89 \\ \cline{1-1}  & Support Vector Machine & 99.17 & 98.02 & 98.09 & 99.77 & 98.00 & 99.27 \\ \cline{1-1}  & ML-MajMulVot & 99.95 & 99.24 & 99.45 & 99.97 & 99.95 & 99.91 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of all five ML models on DS1 with/without K5-CV and MajMulVot.  
cross-validation (K5-CV) technique, and the overall performance of all models is enhanced (Table 8).  
In addition, we have used MajMulVot, which improves the performance of all five ML models, and these results are depicted in Table 8. Here, we can see that the ML-MajMulVot model performs well on the DS2 dataset, and the performance increases compared to the ML models. The result of ML-MajMulVot on DS2 is as follows: ACC: 86.96, F1S: 85.93, SEN: 84.05, SPC: 95.08, PPV: 87.54, and NPV: 91.74.
## 4 Data Collection
### Data Clustering Analysis  
The concept of group technology and data clustering is used as the basic means in analyzing the information collected; this information will be processed with the aim of assisting the decision makers in making appropriate decisions that enable maximizing utilization of this information. Data will be sorted and organized into groups based on selected types of their similarities. The Single Linkage algorithm will be used in the group building process, and the resulting groups will then be analyzed to see how they can be used. Two scenarios will be studied to classify the information collected:  
1. Clusters will be built based on different tourists depending on the proximity in the geographical areas from which they came and regardless of the tourist agencies that organized the tours.
2. Clusters will be built based on different tourist agencies depending on the similarities of tourist nationalities for which offices organize trips.
# Thermomechanical Analysis of Steel-to-Timber Connections under Fire and the Material Density Effect
## 4 Conclusions  
In this work, a numerical methodology was developed to evaluate the safe design of a typical steel-to-timber connection under exposure to fire and under a tensile load, by considering the wood properties of GL20H, GL24H, and GL32H. The numerical methodology presented makes it possible to predict the structural behavior of the steel-to-timber connection during the time of exposure to fire, considering the reduction of properties with the temperature of the steel and wood components. The load-bearing capacity for different times was evaluated by analyzing the stress level and the wood char layer formation. When exposed to fire, wooden materials exhibit thermal physical degradation. In steel-to-timber connections, the type of wooden material can limit the use of these constructive elements in terms of fire resistance. It was concluded that the wood density influenced the resistance of the connection under ambient and fire conditions.  
This study concludes that the proposed thermomechanical model can offer good responses and enables several applications and analyses for future research involving this thematic. These constructive elements must be chosen in advance to prevent and delay the fire damage effect, allowing the connection to remain in service with more safety and durability. Future work will also consider different insulation materials, ensuring greater durability in these types of components. The main purpose of this study was to analyze a steel-to-timber connection in double shear with dowels under fire with the finite element method considering the material density effect, choosing three different wood species. The proposed methodology allowed the verification of the wood density effect on the heat transferred through the connection under fire, and a thermomechanical complex model was developed to analyze this type of problem, which was a great novelty in this work. The analysis in this study allows for the improvement of structural and safety designs, maximizing the load-bearing capacity of connections when exposed to fire.  
Figure 22: Temperature (**left**) and equivalent stress (von Mises) (**right**) in the wood element, both with the middle cross-section, at 300 s.  
**Author Contributions:** Conceptualization, E.M.M.F.; methodology, H.M.L.; validation, C.G., investigation, C.G.; writing--original draft preparation, E.M.M.F.; writing--review and editing, E.M.M.F. and H.M.L.; supervision, E.M.M.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Smart Low-Cost Control System for Fish Farm Facilities
## 3 Selected Factors for Smart Low-Cost Control Systems for Aquaculture
### Selected Control Factors for Aquaculture Facilities with Cages in the Sea  
In this case, all the control factors for water and light are inviable. The only control factors are the adjustable feed velocity and schedule. Monitoring can be applied to water quality and flow. Regarding water quality, the selected parameters are water temperature, turbidity, DO, and light intensity. Water temperature is one of the key parameters for estimating fish size. DO is essential for fish well-being and survival and cannot be modified. The schedule of feed shoots can be adjusted to the DO to ensure that fish are fed with optimum DO concentrations. Turbidity and light intensity are also important for feeding behavior and for the correct operation of other sensors. Additional parameters might include monitoring hydrocarbon and algal blooms as a source of pollution and wind, current, and swell, due to their effect on feeding success.  
Monitoring fish detection/counting and feed falling can be challenging but possible. Fish swimming depth and velocity based on fish detection are crucial to understanding the swimming behavior of fish, which can indicate hunger, stress, or even a decrease in water quality, as well as the fish size, due to the fact that its relation with the feeding demand is mandatory for the control systems. Finally, if possible, the presence of other fishes, such as predators, which affect fish mortality, and intruders, should be included in the control systems to consider their effect on the feed demanded by the grown individuals plus the intruders and on the mortality. The same system proposed for detecting fish predators can be used to detect fish escapes caused by a broken net, which is one of the most problematic events in aquaculture facilities with cages in the open sea.  
In these networks, we have the feeders as actuators, and several pieces of data can be used to define the operation of the automatic feeder. On the one hand, abiotic parameters such as water temperature are used to estimate the required amount of feed; other abiotic parameters, such as DO, light intensity, turbidity, wind, current, and sea swell, are necessary to schedule the feeding time. On the other hand, biotic parameters, such as fish size and the presence of intruders, are considered to estimate the required feed quantity.  
A summary of the factors included for the control of the aquaculture facilities composed of cages in the sea can be seen in Figure 9. The different colors of the factors indicate the Domain to which the factor belongs.  
Figure 9: Factors considered for Smart Low-Cost Control Systems for aquaculture facilities in cages in the sea. These are the factors that must be included in control systems in fish farms in the sea. Factors of the five domains are included. Most of the factors are used only for monitoring with sensors. Only three factors are related to the use of actuators.
## 2 Dataset
### Generalizability Analysis of the Fusion Dataset  
To further enhance the dataset's practicality and application scope, we conducted a comprehensive comparative analysis of three different datasets: a dataset containing 1200 purely real-captured images (RSCB), a dataset containing 1200 completely AI-generated images (AISCB), and a fusion dataset (FSCB) containing 1200 images that combined AI-generated and real images. Each dataset included five different student behaviors, with 300 images for each behavior.  
As shown in Figure 3, the experimental results indicated that the fusion dataset demonstrated similar accuracy and reliability to the purely real-captured dataset. This finding highlights the high applicability of the fusion dataset in student classroom behavior detection, showing that combining AI-generated images with real images can effectively enhance the diversity and coverage of the dataset.  
Moreover, the fusion dataset provides an effective solution to the challenges of dataset scarcity, student privacy protection, and rapid dataset construction. Traditional real-captured image datasets are often limited by high data collection costs, lengthy collection times, and privacy concerns. By incorporating AI-generated images, we can rapidly expand the dataset's scale while providing rich training data without infringing on student privacy.  
Additionally, the high accuracy and reliability of the fusion dataset lay a solid foundation for subsequent behavior analysis and educational interventions. Utilizing such a multi-source dataset enables researchers to gain a more comprehensive understanding and analysis of student behavior patterns in the classroom, thereby providing critical data support for improving teaching methods and enhancing educational quality. This innovative dataset construction method showcases the immense potential of AI technology in the education sector, paving the way for future educational technology research and applications.
## 2 Materials and Methods
### Tailpipe Measurements  
#### 3.1.1 Exhaust Flow Rates Comparisons  
Figure 3 presents the differences of each exhaust flow measurement system (PEMS or derived from CVS flows) to their mean value for each cycle phase (smaller symbols) or the whole cycle (larger symbols). The frameless symbols refer to the WLTC phases, while the framed symbols refer to the lab-RDE phases. The dashed lines indicate a 10% difference based on the currently assumed uncertainty of the flow meters [9]. Each point is the mean value of three repetitions.  
As a reference, the mean (average) of the three measurement systems (PEMS and CVS derived) was chosen because none of the methods can be considered as a reference method with low uncertainty. The differences were within \(\pm\)4 kg/h (few exceptions), which translated to a deviation of around \(\pm\)10% of their mean value for low mean flow rates (\(<\)40 kg/h), and a lower than \(\pm\)5% deviation for higher flow rates. PEMS #2, which was used with both of the vehicles presented consistent behavior (differences were around zero), while the CVS derived flow was slightly overestimating (1-5 kg/h) for the SI vehicle and underestimating (2-3 kg/h) for the CI vehicle. The authors were not aware of any studies comparing exhaust flow measurements, other than the 2017 margins review study [9], where, in most cases, the differences were within \(\pm\)10%.  
For these measurements, the setup of the flowmeters was carried out according to good engineering practice (i.e., having a straight tube between the two flowmeters, and avoiding sharp curves or sudden changes of diameter); however, it cannot be excluded that some of the deviation mentioned above was due to the set of instruments installed in the series.  
Figure 4 presents examples of the flow measurements in real time, obtained from the three systems. Although they generally agreed well, at low flowrates (PEMS #1 SI vehicle), differences of around 4 kg/h were observed. At low flow rates, the uncertainty of all of the methods was high.  
Figure 3: Differences of flow measurement systems from their mean value for each cycle phase. **Left panel:** SI vehicle. **Right panel:** CI vehicle. The frameless symbols are the WLTC phases, while the framed symbols indicate the Lab-RDE phases. The larger symbols represent the overall cycle results, and the smaller symbols represent each cycle phase. Each point is the mean value of three repetitions. The dashed lines indicate a 10% difference.
# Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting
## 3 Proposed Data Embedding Method
### Data Embedding
Beside that, a key \(\kappa\) can be utilized to select/skip blocks to distribute the payload bits across the entire video. One of the advantages of joint utilization of the intra prediction mode and MVP for data embedding is that the payload is distributed into both intra and inter-coded blocks. The competition between the intra and inter prediction modes ensures that only the one with the better \(RDC\) is adopted for coding and data embedding purposes. Specifically, the RDO evaluates and decides the optimal mode (i.e., either intra or inter mode) to be coded. When the cost of embedding more bits in intra mode is higher than inter mode, inter mode will be coded, and vice versa. It is noteworthy that utilizing predictive syntax element to embed data can maintain the perceptual quality of the video without the introduction of drift error [32]. Specifically, the encoder calculates and encodes the prediction error/residual signal by taking the difference between the original and predicted(after associated with data bits) values. Therefore, the reconstructed video is a close approximation of the original video.