esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Results of Reliability Methods
#### 3.2.1 Monte Carlo Method  
The results of the Monte Carlo method shown in Figure 10 were obtained from a relatively large number of simulations (\(10^{7}\)) compared to the pf (\(\approx 10^{-3}\)), which resulted in a low maximum error of \(\approx 0.1\%\). The number of simulations is directly related to the error of the results; therefore, its influence on \(\mathrm{COV}_{\mathrm{p}_{\mathrm{f}}}\) was analysed (Figure 10).  
The statement according to which \(\approx 10/\mathrm{p}_{\mathrm{f}}\) simulation is required for \(\mathrm{COV}_{\mathrm{p}_{\mathrm{f}}}\) to be less than 0.3 [7] matches the results shown in Figure 10. The maximum value of \(\mathrm{COV}_{\mathrm{p}_{\mathrm{f}}}\) for the number of simulations of \(\approx 10/\mathrm{p}_{\mathrm{f}}\) is 0.29, which confirms the statement. Increasing the number of simulations to \(100/\mathrm{p}_{\mathrm{f}}\) results in a decrease in \(\mathrm{COV}_{\mathrm{p}_{\mathrm{f}}}\) from an average of 0.2 to 0.07. Further increase of the number of simulations to \(1000/\mathrm{p}_{\mathrm{f}}\) and \(10000/\mathrm{p}_{\mathrm{f}}\) reduces the \(\mathrm{COV}_{\mathrm{p}_{\mathrm{f}}}\) to 0.008 and 0.005, respectively.
## 1 Introduction
(2023) investigated the influence of a hydrogen and hydroxyl biodiesel mix containing Melia dubia and peppermint oil on a diesel engine at different compression ratios (17.5, 19.5, and 21.5) with exhaust gas recirculations of EGR10 (exhaust gas recirculation percentage), EGR15, and EGR20% at 19\({}^{\circ}\), 21\({}^{\circ}\), and 23\({}^{\circ}\) CA (crank angles) at 1500 rpm. They observed that a CR of 19.5 and a CA of 23\({}^{\circ}\) with 15% hydrogen increased engine performance [10]. [PERSON] et al. (2022) studied the effect of a hydrogen, diethyl ether, biodiesel, n-butanol, and diesel mixture on a diesel engine at various advance fuel injection timings of 17.5\({}^{\circ}\) 20.0\({}^{\circ}\), 22.2\({}^{\circ}\), 25.5\({}^{\circ}\), 27.5\({}^{\circ}\), and 30.5\({}^{\circ}\) CAs at 1500 rpm. They discovered that cylinder pressure rose with advanced injection time and that the 5%H95%SMA mix performed better than the other tested blends [11]. [PERSON] et al. (2022) used Converged CFD to evaluate the effects of ammonia energy sharing with diesel fuel at varying advanced fuel injection times on engine performance and emission characteristics using the Caterpillar 3401 engine model. The findings revealed that using ammonia at a low flame speed reduced NOx emissions by 58.8% and indicated thermal efficiency [12]. The literature findings suggest that there are few works which focus on biodiesel or plastic pyrolysis; it was evident from the findings that the waste plastic oil was used as an equivalent to conventional fuel. However, since climatic pollution has become quite a significant aspect, especially due to the heavy usage of fossil fuels, research which orientsaround clean energy is a necessity. The present work explores hydrogen as a supplement in order to not only better emission characteristics but also improve performance specifics. The evaluations in this study were carried out on a direct injection diesel engine with a specification single cylinder, four stroke compression ignition engines fuelled with 20% waste-plastic oil biofuel, 80% diesel fuel, and 5%, 10%, and 15% hydrogen fuel, which, when combined, are referred to as DP20, DP20H5, DP20H10, and DP20H15 (waste-plastic oil biofuel, diesel, and hydrogen mixtures) at different injection timings with a full load. The outcomes were compared to DP20 mix samples. The research looked at the performance, combustion, and emission characteristics of several blends with advanced fuel injection timings of 17.5, 19.5, 21.5, 23.5, and 25.5\({}^{\circ}\) b TDC (before top dead centre). The effect of various advanced fuel injection timings on cylinder pressure, emitted heat, ignition delay, pressure increase rate, fuel consumption, thermal efficiency, smoke emission, PM emission, and NOx emissions was measured.
## 4 Experimental Study
### Results Analysis and Discussion
#### 4.4.3 Analysis of the Comparative Study (2) for MaF and WFG Problems with 2, 3 and 7 Objectives
By considering the plot of the MIGD, IGD and HVD values in Figures 9-11 during 30 independent runs, we can determine the importance of DB-CSA for solving DMOPs in types I (FDA1, FDA4, dMOP3), II (dMOP2) and III (dMOP1). By analyzing the perturbation of MIGD, IGD and HVD plots, we can see the challenging results obtained when solving FDA1, FDA4, dMOP1, dMOP2 and dMOP3 compared to FDA5 and FDA3 in type II with time-varying POF and POS in both severe and moderate search spaces, and FDA5, FDA3 and FDA2 with a slight change. The efficiency of DB-CSA-II is demonstrated when solving a dynamic tri-objective FDA4 with dynamic POS. However, the proposed DB-CSA-II algorithm assumed a competitive importance compared to the five standard MOEAs (DNSGA-II, dCOEA, PPS, MOEA/D and SGEA) when solving five FDA functions and three dMOP problems over the IGD metric, including different types of environmental changes. This contradicts the HVD metric when all results are not statistically significant at the level of 0.05. Furthermore, the importance of DB-CSA does not assume a high significance level compared to the five standard MOEAs when solving seven UDF and six F problems in type II with a time-varying POF and POS in a moderate environmental change. Finally, we can assume the importance of the DB-CSA algorithm compared to 13 MaOEAs including; MSOPS-II, MOEA/D, HypE, PICEA-g, SPEA/SDE, GrEA, NSGA-III, KnEA, RVEA, Two_Arch2, \(\theta\) -DEA, MOEA/DD, AnD for solving a set of many-objective optimization problems (9 WFG and 7 MaF) with 2, 3 and 7 objectives. Also the proposal has achieved the best results for solving the more complex DTLZ and WFG test suites with 3, 5, 8, 10 and 15 objectives compared to the seven MaOEAs (PMEA-MA, PMEA*-MA, SPEA2/SDE, NSGA-II/SDR, MaOEA/IGD, VaEA, SPEA). The main weakness of the proposed DB-CSA-II algorithm is presented when solving DMOPs in type I and II (FDA1, FDA3 and FDA4) characterized by a time-varying POS and POF, a dynamic spread or the dynamic density of the approximated solution set, with a nonlinear correlation between  
Figure 12: The plots of POF for MaF1-7 functions with 7 objectives using the DB-CSA algorithm. the decision variables. The high number of objectives also leads to the need for additional computational resources and increases the execution time. Figure 13: One-way ANOVA box-plots of 8 MOEAs over IGD for WFG functions with (**a**) 3, (**b**) 5 and (**c**) 15 objectives.
# Reliability Analysis of Hybrid Laser INS Under Multi-Mode Failure Conditions
## 6 Experimental Verification and Analysis
### Analysis of Ground Test Results  
The half sine impact test bench, based on the test input conditions in Table 2 and the output response on the platform, is shown in Figures 24-26.  
According to the input conditions in Figures 24a, 25a and 26a, a half sine impulse test was conducted. Data were collected based on the output of the laser gyroscope signal from the hybrid inertial measurement system and the angle measurement sensors of the inner and outer frames. The X, Y, and Z test data are shown in Table 8 below.  
According to Table 8, it can be seen that during the impact tests conducted on the product in the X, Y, and Z directions, the laser gyroscope worked normally, the hybrid laser system function was normal, and the angular velocity change of the platform corresponding to each impact direction was less than 12 \({}^{\prime\prime}\)/s. The maximum angle change of the  
Figure 25: (**a**). Y-direction impact input. (**b**). Y-direction impact table body response output. The Chinese character “\(\
exists\)\(\
ists\)” in the picture means time domain.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{3}{c}{**Gyroscope Output**} & \multicolumn{3}{c}{**Angular Velocity of**} & \multicolumn{3}{c}{**Inner Frame Variation**} & \multicolumn{3}{c}{**Outer Frame Variation**} \\  & \multicolumn{3}{c}{**(\({}^{\circ}\)/s)**} & \multicolumn{3}{c}{**the Platform (\({}^{\circ}\)/s)**} & \multicolumn{3}{c}{**(\({}^{\circ}\))**} \\ \cline{2-9}  & **X** & **Y** & **Z** & **X** & **Y** & **Z** & **Before and After Impact** & **Before and After Impact** \\ \hline X impact & 4.8 & 43.8 & 64.2 & 5.5 & 22.8 & 40 & 0.011 & 0.035 \\ \hline Y impact & 21.3 & 18.4 & 44.9 & 10.5 & 10.6 & 27 & 0.006 & 0.03 \\ \hline Z impact & 42 & 44 & 5.9 & 36.6 & 32.1 & 5.9 & 0.107 & 0.027 \\ \hline \end{tabular}
\end{table}
Table 8: Data output of inertial measurement system for three-way impact test.  
inner and outer frames was 0.107\({}^{\circ}\), indicating that the system structure has the ability to withstand large overloads and mechanical environments under a high number of levels, and has good environmental adaptability and reliability.
## 2 Materials and Methods
### Data Collection and Technological Infrastructure  
This study was conducted using data collected from a fleet of 164 hybrid diesel buses (Figure 2), all compliant with the EURO VI-D emissions standard. These vehicles primarily operate in urban environments and are part of a major public transport network.  
Data acquisition is carried out through the vehicles' OBDII systems and integrated telematics diagnostic tools installed across the entire fleet. These electronic systems continuously monitor and record a wide range of variables from multiple ECUs (engine, aftertreatment, HVAC, etc.). Specifically, for the aftertreatment system, up to 99 variables are available, including both direct sensor measurements, such as temperatures, pressures, and NOx concentrations, and internally calculated values, such as the time or distance since the last regeneration event. Key components monitored include the Diesel Particulate Filter (DPF), Diesel Oxidation Catalyst (DOC), Selective Catalytic Reduction (SCR) system, and the ammonia blocker. The sampling frequency of the data ranges from 10 to 30 min, depending on the signal and the operational context.  
Based on prior domain-specific analyses and previous studies focused on DPF regeneration behavior, a predefined subset of variables was selected for this work. Rather than conducting a broad exploratory selection, the study directly leverages the variables known to be most informative for predicting clogging dynamics. These include, among others, the estimated soot content in the filter and operational indicators such as the distance and time since the last regeneration. The complete list of selected variables is summarized in Table 2.  
The telematics and storage infrastructure, provided by a third-party technology supplier, enables the download of data in XLSX format for subsequent processing and analysis. Although data extraction in this study was performed manually, the platform also offers API-based access, which opens the possibility for future integration of real-time and automated data ingestion. This would allow the proposed model to operate in a fully online manner while also supporting continuous validation and updates through scheduled batch  
Figure 2: A hybrid diesel bus from the fleet under study.  
retraining. The dataset was processed using Python (version 3.12.7), employing standard libraries such as pandas, matplotlib, and scikit-learn. One key limitation of the dataset is its relatively low sampling frequency of 10 min, which, while adequate for general operational monitoring, may hinder the granularity and precision of certain visualizations and downstream analyses. This constraint originates from the vehicle's own onboard electronics (OBDII system), as its operational design imposes a limit on the number of requests that can be made per unit of time. Additionally, it does not allow simultaneous data retrieval from different ECUs, further restricting the resolution and synchronicity of the collected signals.
## 1 Introduction
### Underwater Glider (UG)
[24] is the fact that a full assessment of the energy loss due to the hull and water compressibility effects is quite important. Thus, the saved UG battery power can be used not only to feed on-board gauges, such as CTD, turbulence, acoustic and biochemical sensors (engaged in mesoscale eddy observation and turbulent microstructure and internal wave observation), but also some indispensable additional instrumentation, e.g., the Doppler velocity logger. In [PERSON] et al. [25], a formation of UGs has been considered which has advantages in sustained ocean observation with high resolution and the adaptation to complicated ocean missions. A multi-layer coordinate control strategy is developed for the fleet of hybrid underwater gliders to control the gliders' motion and formation geometry with optimized energy consumption. The feasibility of the coordinate control system and motion optimization method has been verified both by simulation and sea trials. The fleet of three Petrel-II gliders developed by Tianjin University has been deployed in the South China Sea, and the technology is available for larger groups of UG.
## 5 Computer Simulations
### Spectral Efficiency Performance with UPA and mm-Wave Channel  
Figure 2 illustrates the spectral efficiency performance of the proposed hybrid precoding technique when mm-Wave propagation environment is considered to generate simulation results. Furthermore, UPA is taken into account for antenna deployment, and mm-Wave channel parameters are initialized as \(N_{ray}=10,\ N_{cl}=4,\ \sigma_{x,i}^{2}=1\ \forall i\), and \(AS=10^{\circ}\) to generate the channel matrix according to (55). These channel parameters describe the number of rays/cluster with Laplacian distributed azimuth and elevation angles of arrival and departure, number of clusters, average power of each cluster, and standard deviation, respectively. Moreover, the angle spread for transmitter sector is supposed to be \(60^{\circ}\) and \(20^{\circ}\) in the azimuth and elevation domains, respectively [23]. Simulation results are generated by changing the number of antennas and transmitted data streams simultaneously to show the effectiveness of the proposed method. Figure 2a plots the spectral efficiency achieved by the proposed algorithm when the number of antennas at communicating nodes and data streams are set as \(N_{t}=N_{rel}=100,\ N_{r}=36\), and \(N_{s}=2,\ 3,\ 4\), respectively. The number of RF chains is equal to the number of transmitted data streams, i.e., \(N_{t}^{RF}=N_{rel}^{RF}=N_{r}^{RF}=N_{s}\). It is evident from the obtained results in Figure 2a that a minor performance gap exists between the proposed approach and full-complexity beamforming. However, the performance gap may increase when a considerably large number of data streams are transmitted. Figure 2b shows the performance of the proposed hybrid beamforming by increasing the number of antennas and data streams such that \(N_{t}=N_{rel}=144,\ N_{r}=36\), and \(N_{s}=3,\ 4,\ 5\), respectively. It is clear from the obtained results, in Figure 2b, that the proposed algorithm approaches the upper bound through the best selection of the relay node. Figure 2c plots the spectral efficiency by further increasing the number of antennas at communicating nodes and transmitted data streams such that \(N_{t}=N_{rel}=256,\ N_{r}=64\), and \(N_{s}=5,\ 6,\ 7\) to show the usefulness of the proposed methodology. Again, there is a small performance gap when comparison is made with fully digital precoding. In conclusion, the proposed algorithm achieves good performance consistently when system parameters are changed over a wide range. Moreover, significantly better performance is obtained in comparison to the BHM and SVD-based relay selection strategies.  
Figure 2: \(Cont.\)
## 3 Results and Discussion
### Nitrate Concentration Assessment  
Groundwater nitrate contamination has emerged as a critical concern impacting both human health and groundwater quality in recent years [11; 49]. One of the main contaminants is NO\({}_{3}\)\({}^{-}\), which is mostly found in industrial and agricultural wastewater that has high nitrogen concentrations.  
In the pre-irrigation period, the lowest and highest values of NO3\({}^{-}\) concentrations in groundwater were 27-945 mg/L and the average value was 138.80 mg/L, respectively. In the post-irrigation period, the lowest and highest values of NO3\({}^{-}\) concentrations in groundwater were 20.77 and 797.94 mg/L, respectively, with an average value of 113.56 m/L. As can be seen from the data obtained, a decrease in nitrate concentrations was observed in the post-irrigation period. This situation is directly related to the irrigation carried out in certain periods in the region. With the effect of irrigation, there has been a dilution in pollutant concentrations in groundwater. In this study area, 17 and 15 out of 20 groundwater samples in both irrigation periods, respectively, exceeded the maximum acceptable limit of 50 mg/L and are not suitable for drinking purposes. In the pre-irrigation and post-irrigation periods, 85% and 75% of the average nitrate concentration values of the water samples taken surpassed the required value for drinking water (NO3\({}^{-}\), 50 mg/L), respectively. Considering the areal distribution results, it was observed that especially the western and northwestern parts of Harran Plain had the highest concentrations in terms of NO3\({}^{-}\) distribution. It has been speculated that high NO3\({}^{-}\) concentrations may be due to human activities, particularly excessive use of chemical fertilizers in agriculture. It indicates that children face greater health hazards from nitrate exposure than adults.  
Furthermore, it is recommended that nitrogen fertilizer application be judiciously regulated, whilst also limiting overuse, in view of the fact that agricultural practices such as this significantly contribute to elevated nitrate levels in the groundwater of cultivated land. Moreover, given that animal manure leaching is a significant contributor to nitrate pollution in groundwater, it is recommended to implement regulations on local husbandry practices, adopt rational approaches to managing the disposal of animal waste and faces, and eliminate unreasonable discharge patterns.
### Organization  
This document is structured as follows: Section 2 offers a brief summary of the key concepts, setting the stage for grasping our protocol. A thorough explanation of the hypotheses that support the GHZ\({}_{3}\)MQPEC protocol is provided in Section 3. The GHZ\({}_{3}\)MQPEC protocol is formally outlined in Section 4. An illustrative example that demonstrates how the protocol functions is presented in Section 5, while the issues of efficiency and security are examined in Section 6. The document wraps up with a recap and a discussion on the more intricate aspects of the protocol in Section 7.
# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 3 Proposed Approach
### Scratch Model  
Building a deep neural network from scratch is essential for a better understanding of the mechanism of deep learning methods and getting insight into the dataset's feature space. In this paper, a scratch model is developed by incorporating CNN with LSTM. After applying the preprocessing tasks, the processed frames of video are passed to the CNN architecture, used as a spatial feature extractor. The CNN architecture is comprised of nine layers containing convolution layers with a varied number of filters of 3 \(\times\) 3 dimension, maxpool of 2 \(\times\) 2 pool size with stride 2, and flatten layers. In this architecture, the \"same-padding\" is used, and the stride dimension is (1, 1). For faster convergence during training, ReLU is used as an activation function that provides output to the [0, \(\infty\)] interval. Afterwards, a flatten layer is appended to create a single long feature vector for each selected frame of a video. This CNN architecture processes the sequentially picked frames of a video, and features are extracted from those frames. In this regard, we have implemented the CNN part our model using TimeDistributed layer of Keras. This wrapper employs a same layer to all the selected sequential frames of a video one by one and produces feature vector for each frame. The layer, which is TimeDistributed, shares same weights while processing the frames of a video. Without a TimeDistributed wrapper, a layer updates weights while working on each frame of a video, which resembles image classification. This method lacks the consideration of frames as sequential parts of a video and thus is unable to grab the features of the frames with respect to the whole video. Later, an LSTM layer of 128 hidden units is utilized to extract the temporal features from the feature vector produced by CNN architecture. A dense layer of 128 neurons is added next with a dropout rate of 0.2, followed by the output layer, where the Softmax activation function is used to classify five individual traditional Bangladeshi sports video classes. Therefore, in total, the scratch model contains 17,248,843 weights/parameters. Scratch model architecture is depicted in Figure 2.
# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 2 Methods
### The Measurement System  
For detecting visual, manual, and cognitive distractions, the study utilized the Pupil Labs Core, a wearable ET device complemented with high-definition cameras [34]. This head-mounted device, chosen for its superior accuracy compared to alternatives like the SMI ETG 2.6 and Tobii Pro Glasses 2, featured binocular glasses equipped with two infrared eye cameras and a wide-angle Red-Green-Blue (RGB) world-view camera [35]. ET data includes video recordings (with a world-view camera, Eye0, and Eye1) and raw data components (such as time stamps, pupil positions, pupil diameters, and calculated gaze positions marked using x and y coordinates). The setup included an extensible, open-source mobile ET system with software for recording and analyzing data. Calibration was semi-automated, and for enhanced post-processing capabilities, ID-tag markers (AprilTag; tag36h11 family [36]), sized 50 mm \(\times\) 50 mm and printed on hard plastic, were strategically positioned around the vehicle's dashboard center console and in the driver's windscreen area, as shown in Figure 1. The parameters of the ET system are presented in Table 1.  
Figure 1: Test apparatus: Pupil Core eye-tracking device installed on the head of driver; ID tags in the view of driver (around windscreen and center console).