esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Results of the First Measurement of Vehicle's Braking Characteristics  
Vehicle's braking characteristics measured with new only run-in brakes are given in Tables 5-7. It also shows the initial vehicle speed, stopping distance, braking time and MFDD. All values correspond to low-priced, mid-priced as well as high-priced brake components.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Initial Speed (km.h\({}^{-1}\))** & **Stopping Distance (m)** & **Braking Time (s)** & **MFDD (m.s\({}^{-2}\))** \\ \hline
1 & 48 & 10.55 & 1.55 & 9.09 \\ \hline
2 & 49.11 & 10.73 & 1.52 & 9.32 \\ \hline
3 & 48.76 & 10.58 & 1.52 & 9.27 \\ \hline
4 & 48.1 & 10.19 & 1.5 & 9.22 \\ \hline
5 & 53.02 & 12.5 & 1.67 & 9.27 \\ \hline
6 & 47.36 & 10.11 & 1.49 & 9.43 \\ \hline
7 & 51.33 & 11.81 & 1.62 & 9.16 \\ \hline
8 & 47.37 & 9.93 & 1.47 & 9.33 \\ \hline Average & 48 & 10.55 & 1.55 & 9.09 \\ \hline Standard deviation & 1.38 & 0.61 & 0.05 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Braking characteristics measured with mid-priced brake components.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Brake Disc** & **Low-Priced** & **Mid-Priced** & **High-Priced** \\ \hline Volume of material (dm\({}^{-3}\)) & 0.985 & 0.985 & 0.973 \\ \hline Weight (kg) & 6.96 & 7.12 & 6.95 \\ \hline Density (kg.m\({}^{-3}\)) & 7068.99 & 7228.43 & 6937.30 \\ \hline Wobble (mm) & 0.025 & 0.000 & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Basic physical characteristics of brake discs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Initial Speed (km.h\({}^{-1}\))** & **Stopping Distance (m)** & **Braking Time (s)** & **MFDD (m.s\({}^{-2}\))** \\ \hline
1 & 10.7 & 50.27 & 1.52 & 9.46 \\ \hline
2 & 9.87 & 47.68 & 1.47 & 9.39 \\ \hline
3 & 9.84 & 48.14 & 1.45 & 9.68 \\ \hline
4 & 10.57 & 49.52 & 1.52 & 9.46 \\ \hline
5 & 9.62 & 47.04 & 1.45 & 9.45 \\ \hline  
brake discs and pads cost EUR 58 and high-priced brake discs and pads cost EUR 148. The rear brakes were detached in this case. The stopping distance with detached rear brakes and low-priced front brakes was 16.69 m at the initial speed of 50 km.h\({}^{-1}\) and MFDD was 4.99 m.s\({}^{-2}\). When using high-priced brakes, the stopping distance shortened to 14.39 m and MFDD rose to 7.42 m.s\({}^{-2}\). Thus, it led to a 48% increase in MFDD, whereas, in the measurements in this publication, the value of MFDD did not changed. However, the results might have been distorted due to detached rear brakes.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 3 Experimental Result
### Creating the Synthesized Dataset  
As mentioned above, new rain and haze road dataset are generated using (5). Rain streaks are applied on Cityscapes' foggy dataset. Refs. [32; 33; 34] provides a collection of synthetic foggy images and transmission maps, which is ground-truth for (8). The fog data consisted of images of the vehicle front camera in various cities in conditions ranging from light to dense fog, as reflected in various scattering values (\(0.02,0.01,0.005\)). The synthesized dataset expresses the harsh road situation, which includes not only rain and haze on the road but also haze caused by heavy rain on the road. Figure 7 shows a visual representation of the rain and haze road dataset created by combining the Cityscapes foggy dataset with the rain model.  
This synthesized rain and haze dataset is used as input to the proposed FHRR-Net. To train the network, transmission maps in the foggy dataset is set to the ground truth of the low-frequency network and the hazy image in the foggy dataset is set to the ground truth of the high-frequency network. The loss function is optimized by comparing the ground truth of the dataset with the network output of the FHRR-Net.
# Impact of Heat Exchanger Effectiveness and EGR on Energy and Emission Performance of a CI Engine
## 2 Materials and Methods
### Methodology for Numerical Modeling of the Combustion Process  
To perform a more detailed analysis of changes in the engine's energy and environmental performance with varying HEE levels, numerical modeling of the combustion process is required. This modeling consists of two main components: combustion analysis and the synthesis of engine operating cycle parameters. A numerical model of the investigated engine was developed using AVL BOOST software, with its visual representation shown in Figure 3.  
The combustion process analysis was conducted using the AVL BURN subprogram. Experimental data, including in-cylinder pressure throughout the cycle, fuel and air consumption, engine geometric parameters, compression ratio, fuel properties, and others, were input into the program. During the numerical analysis of the combustion process, key in-cylinder combustion parameters are determined, including the start of combustion (\(\varphi_{p}\)), combustion duration (\(\varphi_{2}\)), combustion intensity shape parameter (\(m\)), pressure rise, heat release rate, temperature rise, and temperature during the combustion process. The AVL BOOST software calculated the expected in-cylinder pressure, BMEP, and other relevant parameters. These results were subsequently used to validate the combustion analysis model.  
The mathematical model of the combustion process for the investigated engine is developed using the single-zone combustion model, as it is suited to describing the combustion kinematics of a CI engine. Heat release is determined using the Vibe function [43]:  
\[\frac{dx}{d\left(\frac{\varphi}{\varphi_{2}}\right)}=6.908(m+1)\left(\frac{ \varphi}{\varphi_{2}}\right)^{m}e^{-6.908(\frac{\varphi}{\varphi_{2}})^{m+1}}. \tag{1}\]  
Based on the heat release rate data, the burned fuel mass fraction, the in-cylinder temperature rise, and the pressure rise were determined. These data were used to perform a more detailed analysis of the combustion process and to explain the changes in engine emission parameters (smoke opacity, CO, CO\({}_{2}\), HC, O\({}_{2}\), NOx) and energy performance indicators resulting from variations in HEE. The results of the numerical analysis are presented in the form of diagrams showing combustion characteristics at an engine speed of 2000 rpm and a load of BMEP = 0.4 MPa, under both EGR on and off conditions.  
Figure 3: Numerical model of the investigated engine: SB1–2—ambient conditions; PL1—intake manifold; PL2—exhaust manifold; PL3—muffler; CL1—air filter; MP1–18—measurement points; C1–4—engine cylinders; 1–15—connecting pipes; TC1—turbocharger; CO1—heat exchanger.
## 5 Simulation Results and Analysis  
### Simulation Results of a Pair of Spherical Cap Electrodes  
The motion trajectories and release characteristics of particles in pure oil under a pair of spherical cap electrodes were obtained, as shown in Figure 16. During the actual breakdown process, electrons were emitted from the cathode, leading to the presence of free charged particles in the oil. From the figure, it can be observed that the trajectories of the charged particles under the electrostatic field were irregular. By carrying out multiple comparisons of simulation results, it was observed that the motion of the charged particles under the influence of the electric field was irregular. Initially, the charged particles were accelerated under the electric field, and then they underwent irregular motion in subsequent stages. Due to the non-uniform distribution of the electric field's strength, the motion velocity of the charged particles was also different.  
Figure 16: Motion trajectories of charged particles between a pair of spherical cap electrodes.  
Similarly, under the aforementioned simulation conditions, an attempt was made to introduce nanoparticles. However, since the simulation software cannot simulate actual TiO\({}_{2}\) nanomaterials, neutral solid particles without charge were used to simulate them while keeping other conditions unchanged. The simulation result of the motion trajectories of charged particles after adsorption by nanoparticles in the transformer oil can be observed in Figure 17. It can be observed from the simulation results that the addition of nanoparticles led to a decrease in the motion velocity of the charged particles after being adsorbed by the nanoparticles, indirectly reducing the migration rate of the charged particles in the oil. By comparing the two simulation result figures, the impact of nanoparticles on charged particles in the oil can be clearly observed.  
The reason why nanoparticles have a certain impact on the motion of charged particles in oil is because their particle size is several orders of magnitude larger than that of the charged particles. The presence of nanoparticles can cause adsorption effects on the charged particles, thereby affecting their motion. The presence of nanoscale particles increases the number of \"shallow traps\" in the oil, which can capture the charged particles in the oil, thereby reducing the migration rate of the electrons. From the simulation results, it can be observed that the migration rate of charged particles adsorbed by nanoparticles in the oil decreased.
## 4 Result and Discussion
### Improving Model Performance by Smoothing Factor  
The application of exponential smoothing to battery data that tends to be volatile led to a significant improvement in the prediction results. Figure 8 compares the prediction results of the application of exponential smoothing with a smoothing factor and the model without smoothing. It is evident that exponential smoothing produces more accurate predictions, particularly when the battery data experience sharp fluctuations. Battery data often experience sharp fluctuations, particularly in situations of intensive use or rapid environmental change. The implementation of exponential smoothing helps to mitigate these fluctuations by assigning more weight to the recent data. Thus, this method makes the model more responsive to recent changes in battery conditions. The results of the experiment show that exponential smoothing with a smoothing factor of approximately 0.3 produces more accurate prediction results, as shown in Table 3, compared to higher smoothing factor values and compared with the model without smoothing, as shown in Table 4. Therefore, for datasets with fluctuating characteristics, exponential smoothing can be a highly effective strategy for enhancing prediction quality.
# Research on the Impact of Flexible Working Hours on Reducing Traffic Delays in the City
## 1 Introduction
Changes in WHs have a positive impact on public transportation performance, although improvements in personal transport are less pronounced [28; 29]. Another study showed that shifting WHs to an earlier time in the morning reduces morning traffic and driver daily load [18; 30]. Another study conducted by the London Metropolitan Transport Authority showed that changing people's WHs can significantly improve urban and public transportation [18; 31; 32]. Studies indicate that people are more willing to change their departure time rather than their mode of transportation to avoid congestion. Understanding the psychological factors influencing departure time choice is key to motivating this behavior change [33]. Departure time choice has been examined from a microeconomic perspective, where individuals make trade-offs between costs, travel time, and deviations from preferred arrival times. Travel time variability (TTV) plays a crucial role, as high variability can lead to reconsideration of departure time choices. Prospect theory also highlights the importance of the decision-making framework for departure time choice, demonstrating the relevance of attitudes and the subjective importance of arriving at a preferred time. As mentioned, traffic congestion is related to commuting, and changing departure times could significantly contribute to reducing peak-hour congestion. Several studies have shown that people are more likely to change their departure time to avoid congestion than to change their mode of transportation [34]. The question remains, however, how to motivate people to make this behavior change. Answering this question requires a better understanding of the psychological factors that influence departure time choice. While this question is important for both car and public transport commuting, this paper focuses on car commuting [33; 35]. To date, departure time choice has primarily been examined from a microeconomic perspective, where the main determinants of behavior are objective factors, such as travel time, arrival time, and travel costs. This rational choice approach assumes that individuals make trade-offs between costs, travel time, and deviations from preferred arrival times to maximize their benefit. Later works have also included travel time reliability, which accounts for the uncertainty of actual travel time during a trip, i.e., unexpected delays [36; 37]. This concept, often referred to as travel time variability (TTV), is important,as people may reconsider their departure time choices under conditions of high travel time variability [38; 39]. Several studies have approached departure time choice considering the assumptions of prospect theory. These studies emphasize the importance of the decision-making framework and have shown that the choice of more or less risky departure times depends on the working conditions and positions of commuters. They indirectly demonstrated the relevance of attitudes, specifically the subjective importance of arriving at a preferred arrival time for the choice of departure time [40].
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## 3 Methods  
### STARFM Data Fusion Algorithm  
The STARFM algorithm can use one or two pairs of MODIS and Landsat images acquired on the same day to predict Landsat resolution images on other MODIS dates. This study focuses on the one pair option. The algorithm utilizes the temporal information from MODIS and spatial information from Landsat images. Spectrally-similar pixels to the central pixel within a moving window are selected from the Landsat image. The model computes the time changes of reflectance in the resampled MODIS images for these spectrally similar pixels. The predicted Landsat-scale reflectance is then calculated as the weighted sum of the changes of MODIS reflectance (\(M\)) to Landsat reflectance (\(L\)) as:  
\[L\big{(}x_{w/2},y_{w/2},t_{p}\big{)}=\sum_{i=1}^{w}\sum_{j=1}^{w}W_{ij}\times \big{[}M\big{(}x_{i},y_{j},t_{p}\big{)}+L\big{(}x_{i},y_{j},t_{0}\big{)}-M\big{(} x_{i},y_{j},t_{o}\big{)}\big{]} \tag{1}\]  
where \(w\) is the searching window size and \((x_{w/2},y_{w/2})\) is the central pixel of this moving window. \((x_{i},y_{j})\) is a given pixel location for both Landsat and MODIS images; \(t_{0}\) and \(t_{p}\) are the pair date and prediction date, respectively; \(W_{ij}\) is the weighting function, which is determined by three measures: the spectral difference between MODIS and Landsat data at the given location, the temporal difference between the input and the predicted MODIS data, and the location distance between the central pixel and the surrounding spectrally similar candidate pixels.  
The data fusion accuracy is somewhat dependent on the consistency of pair images at t\({}_{0}\) and the time-series MODIS images at tp. The former is more related to characteristics of the two sensors and the latter is more related to surface conditions and observation geometries. More detail regarding the STARFM data fusion model is provided in [6].
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 3 TSCH Energy Model
### TSCH Time Slots
#### 3.1.5 Time Slot Sleep  
In time slots where no data have to be transmitted or received, the node sleeps during the whole duration of the slot. The CPU of the node only briefly wakes up at the start of the slot, e.g., to increment the ASN. The states of the Sleep time slot are shown in Table 6.
## 2 Methods
### Interactive Visual Analysis Tools for Real-Time Rendering  
In the process of 3D ocean data visualization, interactive visualization techniques are effective for exploring complex ocean data [19]. It is often necessary to provide users with the flexibility to interact with and manipulate the visualization result in real time. This involves allowing users to switch between different datasets, dynamically adjust various rendering parameters, or modify the camera angles and distances to explore the data from multiple perspectives. The goal is to ensure that the operations performed by users through mouse or keyboard inputs can be immediately updated and reflected in the results. To achieve this feature, a set of comprehensive and user-friendly interactive tools is indispensable. In this study, to achieve interactivity of volume rendering, the proposed framework integrates a set of interactive visual analysis tools. These tools include the following main functionalities:  
1. Data source selection and switching: In oceanographic research, the data required for visualization are often diverse and complex, often necessitating frequent switching between different data sources for analysis. This tool pre-loads a list of all available datasets, allowing users to quickly switch between them by simply selecting the desired data source from a dropdown menu.
2. Basic rendering parameter setting: This feature allows for a dynamic adjustment of key rendering parameters, including the sampling step size in the ray casting algorithm and transparency, either by using the mouse-controlled sliders or by directly entering values through the keyboard to enhance the visual representation of the data. Users can also modify the colorbar through a dropdown menu.
3. Bounding box and axes overlaying: To allow intuitive spatial references for the data's distribution, users can overlay the volume rendering result with the bounding box, as well as horizontal (latitude and longitude) and vertical (depth) axes through checkboxes. The inclusion of the bounding box helps to visually frame the dataset, while the axes enable users to easily understand the position of the data within the geographic coordinate system.
4. Volume cutting: The framework provides a volume cutting feature that allows users to slice the 3D data along coordinate axes, enabling a detailed examination of specific regions or layers within the volume, which is particularly useful for analyzing complex structures or features that may not be immediately visible in the full three-dimensional representation.
5. Spatial data filtering based on value: This tool helps users focus on specific regions of interest (ROI) by adjusting the minimum and maximum scalar field values, thereby filtering out the non-relevant data and displaying the relevant portion, enabling a more targeted analysis of ocean variables.
* Time-series animation playback: This feature is especially useful for representing the dynamic processes and changes in ocean environmental phenomena over time. Users can specify a time range of interest and visualize the corresponding time-varying volume data in an animation format.
# Sequence-Information Recognition Method Based on Integrated mDTW
## 1 Introduction
The proposed model presented in this paper offers high interpretability, integrating traditional sequence modeling methods with deep-learning techniques to achieve superior performance. Its transparent framework ensures a clear understanding of each component and parameter, exemplified by the straightforward calculation process of mDTW. Furthermore, the model's seamless integration with deep learning enables powerful feature extraction while maintaining interpretability. With low computational complexity and minimal parameterization, the model remains efficient and reliable, even for large-scale datasets. Overall, this approach combines interpretability, deep-learning integration, and computational efficiency, making it well-suited for a wide range of intelligent computing tasks.
## 5 Results
### Haurwitz Results  
From Figure 3, the [PERSON] clear sky model overestimated GHI in five stations, namely Marrakech (\(-4.6\%\)), Tangier (\(-2.3\%\)), Ouarzazate (\(-12.2\%\)), Fes (\(-6.1\%\)), and Agadir (\(-3.5\%\)), whereas it underestimated GHI at Tantan (15.9%). From Figure 4, which gives the hourly rRMSE, rRMSE was less than 15% in Agadir (7.7%), Tangier (5.4%), Marrakech (7.7%), and Fes (8.0%), showing average agreement in these stations between the McClear and Haurwitz clear sky modes. It was more than 15% at the Tantan (21.4%) and Ouarzazate (15.7%) stations, which indicates poor agreement in these stations. From Figure 5, which indicates rMAE, all of the stations showed an rMAE of less than 15%, which shows good agreement, with the exception of the Tantan (17.7%) station where rMAE was greater than 15%, thus indicating poor agreement as indicated by high bias.  
From Table 5, which indicates the coefficient of determination (R\({}^{2}\)) at all six stations, R\({}^{2}>0.99\) in all stations, indicating excellent performance based on Table 4, with the exception of the Ouarzazate (<0.99) station, where the coefficient indicates average correlation, and Tantan (0.97), where the coefficient indicates poor correlation between the Haurwitz and McClear clear sky models. From Table 6, Haurwitz was not feasible at any of the six stations based on Section 4.6. criteria when compared to other clear sky models, and it has the minimum rMBE at Agadir. From Figure 6, Haurwitz was closer to McClear, which shows good agreement that varies at different stations. The performance of Haurwitz clear sky model with stations is also summarised in the supplementary material Tables S1 to S6 and Figure S4.
## 2 Finite Element Modelling
### Model Description
#### 2.1.1 Materials  
The parameters of 3D unit cell models were summarized in Table 2, where \(\upmu\) represents the Poisson's ratio; \(\lambda\) represents the compression index; and \(\kappa\) represents rebound index. According to [PERSON] et al. [32], the [PERSON]'s modulus (E) of embankments fill and gravel cushion layer were 15 MPa and 30 MPa, respectively. According to [PERSON] et al. [22], the [PERSON]'s modulus of embankments fill was 20 MPa. [PERSON] et al. [33] and [PERSON] and [PERSON] [28] adopted a [PERSON]'s modulus of 25 MPa for granular embankment materials. In this study, the [PERSON]'s modulus of gravel embankment was 25 MPa and other parameters of gravel embankment materials were the same as those described in [PERSON] et al. [33]. The Modified Cam Clay constitutive model, due to its excellent performance, was adopted here for the consolidation calculation of the subsoil. The pile, geogrid and pavement (including base, subbase, and AC layer) were linearly elastic. Both gravel and solidified soil were simulated using the Mohr-Coulomb failure criterion. According to [PERSON] et al. [34], the cohesion (\(c^{\prime}\)) of cement-solidified soil increased obviously with increasing curing agents, while the friction angle changed slightly and the average was 38\({}^{\circ}\). The internal friction angle (\(\varphi\)/) of cement-solidified soil grains ranged from 30\({}^{\circ}\) to 45\({}^{\circ}\)[35]. According to [PERSON] et al. [36], the effective cohesion of the cement solidified intermediate-plasticity clay ranged from 10 to 110 kPa. Due to the cementation products and strong interlocking microstructure in the solidified matrix, the solidified soils possessed high cohesion and friction angle than the  
Figure 1: Layout of numerical model: (**a**) cross section, (**b**) plan view, (**c**) elevation view, (**d**) mesh.  
granular fill materials. In this work, the cohesion and friction angle of the solidified soil were taken as 75 kPa and 39\({}^{\circ}\), respectively.