esa-sceva/satcom-chunk-collection huggingface dataset:
# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
## 5 Performance Evaluation
### Phase Ablation Test of the RoadInTCP Model  
**A. Three-phase ablation test of RoadInTCP model.** To demonstrate the significance of different phases and validate the design decisions in the proposed _RoadInTCP_ model, an ablation study has been conducted. We not only evaluate the experiment performance of _RoadInTCP_ at each individual phase, but also the performance of RoadInTCP with different combinations of phases.  
In Table 3, RoadInTCP\({}_{PI}\) represents the result obtained by Phase I which exploits only the topological information of a road network and the rich intrinsic properties of intersections and road segments. RoadInTCP\({}_{PII}\) indicates the result obtained by Phase II which exploits the road network structure to embed dynamic traffic features and other attributes  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **2014** & **2014** & **2015** & **2015** & **2016** \\  & **(First Half)** & **(Latter Half)** & **(First Half)** & **(Latter Half)** & **(First Half)** \\ \hline LR & 0.6643 & 0.6798 & 0.6499 & 0.6961 & 0.6538 \\ SVM & 0.7387 & 0.7401 & 0.7289 & 0.7478 & 0.7336 \\ DT & 0.7694 & 0.7721 & 0.7594 & 0.7847 & 0.7501 \\ RF & 0.7830 & 0.7893 & 0.7761 & 0.8095 & 0.7623 \\ MLP & 0.7564 & 0.7692 & 0.7505 & 0.7922 & 0.7498 \\ GCN & 0.8190 & 0.7919 & 0.8094 * & 0.8254 & 0.8151 \\ LSTM & 0.8201 * & 0.8145 * & 0.7923 & 0.8344 * & 0.8194 * \\ RoadInTCP & 0.8499 & 0.8568 & 0.8366 & 0.8694 & 0.8493 \\  & (3.634\%) & (5.193\%) & (5.591\%) & (4.195\%) & (3.649\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of RoadInTCP comparisons with different methods.  
features around intersections. RoadInTCP\({}_{PIII}\) reports the result obtained by Phase III which exploits various spatio-temporal correlations including short-term and long-term time-dependent correlations. RoadInTCP\({}_{PI+PII}\), RoadInTCP\({}_{PI+PIII}\) and RoadInTCP\({}_{PII+PIII}\), respectively, represent the performance obtained by different combinations of phases. As shown in Table 3, we observe that the performance of the individual phases is not significantly different, but they all outperform the existing methods, thus demonstrating the effectiveness of the model designs proposed for each phase. In addition, we find that the different combinations of phases perform better than each individual phase, but are slightly worse than the overall _RoadInTCP_ model. It demonstrates that the complete RoadInTCP model, by taking into account the different aspects of the three phases simultaneously, is effective for predicting traffic crashes. Finally, by comparing RoadInTCP\({}_{PI+PII}\), RoadInTCP\({}_{PI+PIII}\) and RoadInTCP\({}_{PI+PIII}\), we observe that RoadInTCP\({}_{PI+PII}\)(without Phase I) performs the worst overall, indicating the most significant improvement to be made by Phase I. Between RoadInTCP\({}_{PI+PII}\) and RoadInTCP\({}_{PI+PIII}\), the performance of RoadInTCP\({}_{PI+PII}\) is a bit worse, indicating the potential improvement by Phase III is slightly better than Phase II.
## 4 Path-Planning Method Considering Passenger Tolerance Characteristics
### Prerequisites and Required Functions  
The suppression of avoidance behavior for pedestrians for whom the PMV passenger can tolerate the invasion of PS was focused on to achieve efficient autonomous mobility in crowded environments. Thus, a PMV's path-planning method that suppresses its avoidance behavior when the passenger can tolerate approaching pedestrians was considered based on passenger tolerance characteristics. This method emphasizes avoidance for pedestrians that the passenger cannot tolerate to increase passenger discomfort. By contrast, when a tolerable pedestrian is approaching, the method moves the PMV in a straight line toward its destination. The PMV autonomous mobility system was assumed to consist of self-localization and environment recognition using LiDAR sensors, etc., and path planning to the destination. The PMV assumed in this method was a mobility scooter, which is a non-holonomic mobility system. It was also assumed that all surrounding pedestrians could be recognized with the perfect accuracy of their position and velocity, and there are no computational resource limitations.  
From the results of Section 3.3, it was clarified that a passenger can tolerate a pedestrian's invasion of their PS when the pedestrian's avoidance behavior in the range of 6.0-7.0 m ahead of the PMV is small, and the predicted passing distance is long. Based on these characteristics, the required function was set to suppress the avoidance behavior of the PMV for tolerable pedestrians with small avoidance behavior just before invading their PS, and long predicted passing distances to select a path with a shorter travel time.
## 3 Results  
In the present study, we proposed a one-step approach to develop TR-loaded polymer materials, such as PA12 and TPU, using SLS as a flexible additive manufacturing technology. In brief, in this method, a laser power source to sinter powdered materials (e.g., fine polymer powder, metal, ceramics, and others) is used to create the final solid structure upon a defined 3D model. Of importance for the suitable quality of the final object is the powder flow, which can impact the printed device's physicochemical properties, as well as the fine surface finish. In this aspect, the thin powder layer consistent deposition is crucial, without particle selection during the layer-by-layer printing procedure. In fact, the particles' dimensions should be smaller than the layer thickness, thus ensuring the homogeneous composition of the powdered material during laser scanning. A schematic presentation of the SLS working principle is provided in Figure 1.  
Here, for the specimen fabrication, a layer thickness of 125 \(\upmu\)m was used, defined as the optimal working thickness without selection of the TR particles, suitable dispersion of the rubber domain, and final object resolution. The TR particles dimensions were studied by SEM, and the particle size was found to be about 98% under 150 \(\upmu\)m, with a major size distribution between 60 and 100 \(\upmu\)m, as presented in Figure 2. Generally, in laser sintering, particle dimensions around 45-90 \(\upmu\)m are considered optimal in size [24]. In addition, particles smaller than 45 \(\upmu\)m are often considered as a generator of static forces, making the printing process difficult. Moreover, polymer particles with dimensions in the range between 150 and 200 \(\upmu\)m (e.g., polylactic acid) or even between 425 and 600 \(\upmu\)m (HDPE) proved to lead to the fabrication of porous devices with rough surfaces [24].  
Figure 1: Schematic presentation of the SLS printing process.
# Enhanced Sensitivity of FeGa Thin-Film Coated SAW Current Sensor
## 3 Technical Realizations
### Differential Oscillator  
In this work, the oscillation loop was employed to generate the SAW chip, and the differential structure was also used to eliminate the external temperature effect, vibration noise, and magnetic noise [26; 27; 28]. The FeGa thin film coated device as the sensing chip and the naked one as the reference chip were packaged into the same metal base, and connected into the differential oscillation loop made of discrete elements as an amplifier, phase shifter, mixer, and so on (Figure 6a), and the mixed oscillation frequency signal was collected by using the frequency counter as the sensing signal.
## 2 Data Acquisition
### Mature Green Tomato Image Acquisition  
The 4G wireless RGB camera (Hykvision 3T46 FDWD) is selected for image acquisition, which is mounted on the greenhouse robot. The mounting height of the camera is 90 cm from the ground, the vertical distance with the plant is around 50 cm. The horizontal and vertical field of this camera are 87.3 and 46.3\({}^{\circ}\), respectively. The maximum image size obtained by this camera is 1920 \(\times\) 1080 pixels. There are three main reasons to select this camera: first, the equipment head allows the user to remotely set the degree of camera rotation, to make sure a 50-160 cm plant field of view; then, it has a IP66 waterproofing grade, which is suitable for the humid working environment in greenhouse; third, it has a SIM card slot that supports 4G networks, the collected videos and images can be uploaded to the server through the high-speed 4G network.  
The greenhouse mobile robot can move along the navigation path and the camera shoots the video of tomatoes, which is uploaded to the server. Then, the video can be downloaded on the server side, thus the original pictures of tomatoes with 1920 \(\times\) 1080 pixels are obtained by intercepting the video frames. In order to make sure the diversity of images used to train the Mask R-CNN network, tomato images are selected under both positive light and backlight conditions. As shown in Figure 3, each lighting condition includes images with no occlusion, fruit overlapping, leaf cover and branch cover.  
Figure 2: The (**a**)actual and (**b**) schematic diagram of the greenhouse path.
# Energy-Aware Next-Generation Mobile Routing Chains with Fog Computing for Emerging Applications
## 3 Proposed Load-Balanced and Energy-Efficient Mobile Routing Protocol  
This section describes the proposed algorithm with interaction among developed phases.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline \multicolumn{2}{c}{**Overview and Limitations**} \\ \hline \multirow{6}{*}{Existing solutions} & Sensors and fog computing are frequently utilized for real-time emerging networks to automate the devices’ connection and communication systems. Moreover, fog nodes are increasing the scalability of constraint networks and reducing the energy consumption of interconnected devices for data transmission. \\  & However, due to the limited computing powers of the nodes, many solutions are not able to cope with robust stability for the communication systems. \\  & Furthermore, it was observed that many solutions do not offer security systems for wireless systems, and impose additional overhead for protecting network data. It was also noted that most proposed solutions have a longer delay for crucial network operations due to issues with frequent network disconnectivity. \\ \hline \multirow{2}{*}{Proposed algorithm} & In this research study, we proposed for a mobile network to offer an energy-aware solution with the combination of data trustworthiness using fog computing. Moreover, a lightweight authentication system is developed to support real-time communication in a reliable discipline. \\ \cline{1-1}  & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing related studies.
# Using Canola Oil Biodiesel as an Alternative Fuel in Diesel Engines: A Review
## 3 Properties of Canola Oil Biodiesel
### Cetane Number  
The cetane number (CN) is a factor closely related to ignition delay. A higher CN will shorten the ignition delay time, reduce engine knock, reduce the time of heat loss, and improve the thermal efficiency of the engine. Because most of the molecular structure of biodiesel is a long chain consisting of C and H atoms, there are almost no branches or aromatic structures in the middle. It is this characteristic that causes biodiesel to have a high CN [74]. Most biodiesel fuels have a cetane number between 40 and 60 [37], and the CN of BD100 is 61.5, which is 10.2% higher than that of pure diesel (55.8) [57].
# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
## 2 Related Works
Most of these algorithms are based on animal behavior, evolutionary concepts, and physical phenomena. These schemes aim to solve clustering challenges, such as dynamic network topology, energy efficiency, and load balancing, by mimicking the adaptability, efficiency, and robustness of natural systems. Load balancing techniques are widely used in routing across various types of networks, including FANETs. In routing, load balancing refers to the practice of efficiently distributing network traffic across multiple paths or nodes to maximize network performance. In [33], the authors proposed a novel dynamic clustering mechanism called DCM utilizing a political optimizer. DCM addresses UAV load balancing to facilitate the efficient distribution of data packets within FANETs while ensuring good reliability and scalability. Using glowworm swarm optimization (GSO) to form and manage clusters, [PERSON] et al. [34] proposed a self-organization-based clustering scheme (SOCS) for FANETs. Both flame optimization (MFO) offers excellent coverage, with minimal energy consumption and a minimal number of CHs required for routing. The authors of [11] used the MFO algorithm for network building and node deployment in FANETs. A novel clustered routing model for FANETs was proposed in [35], utilizing a hybrid approach from a MGO with Java Algorithms, called HMGOC (MGO-JAYA). There are many advantages to using HMGOC, including improved load balancing, reduction in energy consumption, reduction in latency, and increase in network throughput. In [36], the authors proposed a bio-inspired clustering scheme called BICSF for FANETs that utilizes a hybrid mechanism consisting of GSO and krill herd (KH). Using the GSO algorithm, the proposed scheme uses energy-aware cluster formation and CH selection. In addition, in a behavioral study of KH, the authors proposed an efficient cluster-management algorithm. In Table 1, we summarize all previously existing state-of-the-art methods and their objectives, strengths, and weaknesses. A range of methods has been discussed in the existing literature for routing efficiency, energy conservation, and stability within FANETs. However, these approaches are not easily scalable or adaptable to such a highly dynamic and mobile FANET environment. To address these limitations, this paper proposes the HMAO algorithm to integrate the advantages of these methods with regard to gaps in adaptability and scalability and offers a comprehensive solution specifically for the complex demands of FANETs.
## 4 Engine Test Results and Discussions
### Engine Emission Characteristics  
#### 4.3.1 Fuel Effects on NOx Emissions  
Nitric oxide (NO) and nitrogen dioxide (NO\({}_{2}\)) are usually called nitrova oxides (NOx) together. NO plays a predominant role. The mechanism of thermal NO can be applied to analyze its formation in diesel engines. It can be simply ascribed to the following three factors: the concentration of oxygen (O\({}_{2}\)), the highest temperature, and the retaining time of the nonuniform burned gas. This mechanism mainly occurs during the diffusion combustion phase and in regions behind the front of the flame, where the temperature is rather higher than the calculated average temperature of the bulk gas within the cylinder [25].  
From the minor difference in Figures 8 and 12, heptane- and tetrain-blended fuels have the highest cylinder pressure and burned zone temperature in most cases, which indicates that they improve the combustion and result in higher NOx emissions. Figure 13 displays the NOx emissions under different loads at the speed of 1900 r/min. Generally, with increased load, NOx increases and then decreases when the load exceeds a higher value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Torque** & **Start of Pre-** & **Duration of Pre-** & **Start of Main Injection** & **Duration of Main Injection** \\ (**N-m**) & **(**\({}^{\circ}\)CA BTDC)** & **(**\({}^{\circ}\)CA**)** & **(**\({}^{\circ}\)CA BTDC)** & **(**\({}^{\circ}\)CA**)** \\ \hline
400 & 26.2 & 3.1 & 14.6 & 9.8 \\
800 & 25.7 & 2.7 & 14.5 & 12.6 \\
1200 & 24.4 & 2.5 & 13.6 & 16.7 \\
1600 & 21.7 & 2.4 & 11.3 & 22.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Injection strategy at 1900 r/min.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Torque** & **Injection Pressure** & **Cooling Water Temperature** & **Oil Temperature** \\ (**N-m**) & **(MPa)** & **(**\({}^{\circ}\)C**)** & **(**\({}^{\circ}\)C**)** \\ \hline
400 & 107 & 83 & 101 \\
800 & 139 & 85 & 102 \\
1200 & 158 & 85 & 100 \\
1600 & 169 & 87 & 103 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Other conditions in experiments.  
In the ascending range, the increased combustion temperature plays a dominating role. After the inflection load, the concentration of oxygen dominates NOx formation. However, the addition of the four hydrocarbons almost all increase NOx emissions, especially the addition of tetralin. The addition of heptane and tetralin increases NOx emissions, so this shows that NOx emissions do not follow the change of cetane number of the blended fuels, nor does the change of distillation range. There is an exceptional case of the addition of 20% dodecane on NOx emissions, but the effect of heavy paraffin needs further study. From the constitution of diesel hydrocarbons, the addition of tetralin and heptane addition are obviously deteriorated the engine-out NOx emissions.  
Figure 12: The burned zone temperature versus crank angles at 1900 rpm.  
Figure 13: NOx emissions versus engine loads at 1900 rpm.
# Discrete-Time Sliding Mode Filter with Adaptive Gain
###### Abstract  
In feedback control of mechatronic systems, sensor signals are usually noisy and uncertain because of measurement errors and environmental disturbances. Such uncertainty and noise of feedback signals may cause instability of the controlled systems. This paper presents a new model-free discrete-time sliding mode filter for effectively removing noise by balancing the tradeoff between the filtering smoothness and the suppression of delay. The presented filter is an extension of a sliding mode filter ([PERSON] et al. Real-time quadratic sliding mode filter for removing noise. _Adv. Robot._, 2012) by including an adaptive gain, of which value is determined in a similar way to that of a first-order adaptive windowing filter ([PERSON] et al. Discrete-time adaptive windowing for velocity estimation. _IEEE Trans. Control Syst. Technol._, 2000). The effectiveness of the presented filter is validated through numerical examples and experiments.  
adaptive gain; sliding mode filter; nonlinear filter; backward Euler discretization
# A Greedy Pursuit Hierarchical Iteration Algorithm
## 4 Simulation Experiments
* Compared with the traditional LSI algorithm, the GPHI algorithm can use the limited sampled data to achieve higher parameter estimation accuracy--see Figure 9.
* With different noise variances, the parameter estimates converge near the true values as the iterative times increase, which shows that GPHI algorithm has certain robustness--see Figures 3-6 and 11-14. * The estimation accuracy of using limited sampled data is close to that of using large sampled data. This shows that the GPHI algorithm can realize parameter estimation using limited sampled data--see Tables 2 and 5. * The estimated outputs can match the true outputs well--see Figures 7, 8, 15 and 16. * The GPHI algorithm can accurately find the locations of key non-zero parameters, which shows that the GPHI algorithm can estimate the parameters and time delays simultaneously--see Tables 3 and 6 and Equations (32) and (33). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Sampled Data Length \(L\)**} & \multicolumn{2}{c}{**300**} & \multicolumn{2}{c}{**400**} & \multicolumn{2}{c}{**500**} & \multicolumn{2}{c}{**600**} & \multicolumn{2}{c}{**700**} & \multicolumn{2}{c}{**800**} & \multicolumn{2}{c}{**1000**} \\ \hline Estimation error \(\delta(\%)(\sigma^{2}=0.50^{2})\) & 2.3844 & 1.7831 & 1.9832 & 2.5122 & 2.4627 & 2.388 & 1.8807 \\ Estimation error \(\delta(\%)(\sigma^{2}=1.00^{2})\) & 3.641 & 3.2164 & 4.055 & 4.7375 & 4.5741 & 4.1934 & 3.2887 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The parameter estimation errors with different \(L\) and \(\sigma^{2}\). Figure 16: The true outputs, estimated outputs, and their bias of Experiment 2 with \(\sigma^{2}=1.00^{2}\). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Sampled Data Length \(L\)**} & \multicolumn{2}{c}{**300**} & \multicolumn{2}{c}{**400**} & \multicolumn{2}{c}{**500**} & \multicolumn{2}{c}{**600**} & \multicolumn{2}{c}{**700**} & \multicolumn{2}{c}{**800**} & \multicolumn{2}{c}{**1000**} \\ \hline Estimation error \(\delta(\%)(\sigma^{2}=0.50^{2})\) & 2.3844 & 1.7831 & 1.9832 & 2.5122 & 2.4627 & 2.388 & 1.8807 \\ Estimation error \(\delta(\%)(\sigma^{2}=1.00^{2})\) & 3.641 & 3.2164 & 4.055 & 4.7375 & 4.5741 & 4.1934 & 3.2887 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The locations of the estimated key non-zero parameters of Experiment 2 with \(\sigma^{2}=0.50^{2}\) and \(L=250\).
## 2 Methods
### Simplified Coherent Averaging Method
#### 2.2.1 Coherent Averaging Method  
Let \(x(t)\) be the ECG observation signal, \(s(t)\) be the ECG original signal, and \(n(t)\) be the noise.  
\[x(t)=s(t)+n(t) \tag{10}\]  
We take the R peak of the ECG as the interception point and the RR interval as the length. \(x(t)\) is divided into \(M-1\) segments, denoted \(x_{i}(t)\):  
\[x_{i}(t)=s_{i}(t)+n_{i}(t)\;\;,\;\;\;i=0,1,\cdots,M-1 \tag{11}\]  
where \(M\) is the number of R peaks in \(x(t)\).  
Due to the nonstationarity of ECG signals, the lengths of the RR intervals are not equal. Therefore, \(x_{i}(t)\) needs to be linearly scaled to the same length and resampled to the same sample number. Then, the resampled \(x_{i}(t)\) is used as a row vector to construct a matrix, and coherent averaging is obtained by calculating the column average of the matrix:  
\[\overline{x}(t)=\frac{1}{M-1}\sum\limits_{i=1}^{M-1}x_{i}(t)=\frac{1}{M-1} \sum\limits_{i=1}^{M-1}(s_{i}(t)+n_{i}(t)) \tag{12}\]  
\[\overline{x}(t)=\overline{s}(t)+\frac{1}{M-1}\sum\limits_{i=1}^{M-1}n_{i}(t) \tag{13}\]  
where \(\overline{s}(t)=\frac{1}{M-1}\sum\limits_{i=1}^{M-1}s_{i}(t)\).  
According to Formula (13), the second term is the coherent averaging noise, which is only \(1/(M-1)\) times as much as the original noise. If \(M\) is large enough, \(\overline{x}(t)\approx\overline{s}(t)\). We reversely scale and resample \(\overline{x}(t)\) back to the original length and place it repeatedly according to the initial position. Thus, the denoised signal is obtained.  
The coherent averaging method can obviously suppress the noise and enhance the local information of PQRST waves in the ECG. However, the nonlinear and nonstationary characteristics are weakened at the same time; in particular, the difference in amplitude cannot be recovered.
## 3 Study Area and Methodologies
### Methodologies
#### 3.2.2 Land-Use Dynamic Degree  
The land-use dynamic degree provides a quantitative measure of the pace at which LUCs occur, playing a significant role in assessing and comparing the regional disparities and tendencies of LUCs [72]. The equation is as follows:  
\[K=\frac{U_{b}-U_{a}}{U_{a}}\times\frac{1}{T}\times 100\% \tag{1}\]  
where \(K\) signifies the rate of change, or dynamic degree, associated with a particular category of land use; \(U_{a}\) and \(U_{b}\) denote the respective areas occupied by that specific land-use type at two distinct points within the time frame of the study; and \(T\) symbolizes the duration of the research period over which the land-use changes are being observed and measured.
## 3 Results and Discussion
### Pore Size Distribution, Porosity, and Absolute Permeability
The changes in porosity and absolute permeability were relatively larger and more uniform for the sub-volumes from sample B than for the sub-volumes from sample A. Relatively large changes in porosity were observed for sub-volumes with relatively low initial porosities (see Figure 9a and Table 4). Figure 9b demonstrates the sensitivity of absolute permeability to changes in porosity, and as we can see, an increase in porosity leads to an almost linear increase in absolute permeability. This means that porosity is an important characteristic of a porous medium for assessing its absolute permeability. Figure 9: (**a**) Porosity and absolute permeability changes of the sub-volumes and (**b**) the sensitivity of the absolute permeability change to porosity changes.