esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM
###### Abstract  
Quantifying and predicting driving risks for connected autonomous vehicles (CAVs) is critical to ensuring the safe operation of traffic in complex environments. This study first establishes a car-following model for CAVs based on molecular force fields. Subsequently, using a convolutional neural network and long short-term Memory (CNN-LSTM) deep-learning model, the future trajectory of the target vehicle is predicted. Risk is quantified by employing models that assess both the collision probability and collision severity, with deep-learning techniques applied for risk classification. Finally, the High-D dataset is used to predict the vehicle trajectory, from which the speed and acceleration of a target vehicle are derived to forecast driving risks. The results indicate that the CNN-LSTM model, when compared with standalone CNN and LSTM models, demonstrates a superior generalization performance, a higher sensitivity to risk changes, and an accuracy rate exceeding 86% for medium- and high-risk predictions. This improved accuracy and efficacy contribute to enhancing the overall safety of connected vehicle platoons.  
traffic safety; deep learning; risk quantification; risk prediction +
Footnote †: journal: applied sciences  
0000-0001-9991-7880]D.M.  
Research on driving risk prediction and quantification can help identify potential road safety hazards. [PERSON] et al. [5] defined safety risks as ensuring that network decisions are consistent with human perception and proposed a safety attribute to instantiate and quantify these risks. [PERSON] et al. [6] introduced a machine-learning framework to systematically address future driving risks related to collisions, extracting features from records to define five different types of risks.  
Precise path planning and risk control can effectively reduce the likelihood of vehicles entering high-risk areas, thereby lowering the incidence of traffic accidents. [PERSON] et al. [7] proposed a multi-objective vehicle path control method based on deep Q-networks, which performs better than existing methods in terms of driving distance, time, and waiting time in dynamic traffic environments. [PERSON] et al. [8] used a multinomial logistic regression model to reveal the choice behaviors of each potential cluster in different interactive driving scenarios, setting higher standards for drivers when changing lanes.  
In summary, although extensive research has been conducted on risk prediction and quantification, the current risk prediction and quantification for CAVs generally rely on single-feature models and have not fully considered the impact of multi-feature models. This paper will build upon the CNN and LSTM models, using a CNN-LSTM fusion model to predict the future trajectories of following vehicles. It will integrate two feature models--collision probability and collision severity--to quantify the risks. By calculating the target vehicle's speed and acceleration, the paper aims to further achieve risk quantification and prediction for connected autonomous vehicles.
## 3 Experimental Design
### Optical Parameters of Laboratory Simulated Light Source  
Titanium dioxide needed to be excited under a certain condition of illumination. Xenon lamps which are similar to the solar spectrum can be chosen as a light source. They have 400 nm-315 nm-280 nm wavelengths with a width of 3.10 to 4.43 eV ultraviolet light. The xenon lamp (power 25 W, the luminous flux 3200 lm), which can generate excited electrons and holes, was used as a safe photocatalytic light source [19]. The xenon lamp and the rutting test specimen were placed in the sealed tank of the automobile exhaust analyzer, as shown in Figure 2. For the calculation of the spatial distribution of light intensity, luminaire efficiency, luminance distribution, and shading angle, etc., the spatial arrangement is simplified as a mathematical geometric model (see Figures 3-5 below). The experiments were carried out at nighttime to simulate the dark environment of an underground road.  
Three lighting arrangement schemes were used to obtain different photocatalytic reaction effects.  
Scheme A: The number of xenon lamps: 1, position, top, height H = 0.2 m.  
Scheme B: The number of xenon lamps: 2, position, both sides, height H = 0.2 m, elevation angle = 30\({}^{\circ}\).  
Scheme C: The number of xenon lamp: 3, position, both sides + top, height H = 0.2 m, elevation angle = 30\({}^{\circ}\).  
Figure 1: Preparation process of nano TiO\({}_{2}\) epoxy emulsified asphalt and mixtures.  
Figure 2: Rutting specimen and light source arrangement.  
As shown in Figures 2-4, P1 is the maximum point of illumination, P2 is the weakest one, and P3 is the midpoint of the edge. The illumination value needs to be calculated constantly.  
According to Reference [20], the illumination of the rutting panel is:  
\[E_{pi}=\frac{I_{c\gamma}}{H^{2}}.cos^{3}\gamma\frac{\Phi}{1000}M \tag{1}\]  
In the formula,  
\(E_{pi}\) -illumination of point P generated by the particular lamp (lx);  
\(\gamma\)-light incident angle of point P from the particular lamp (\({}^{\circ}\));  
\(I_{c\gamma}\)-light intensity value of point P (cd);  
\(M\)-maintenance correction coefficient of the particular lamp, usually 0.6-0.7;  
\(\Phi\)-rated luminous flux of the particular lamp (Im);  
Figure 4: Illumination geometric model of Scheme B.  
Figure 5: Illumination geometric model of Scheme C.  
Figure 3: Illumination geometric model of Scheme A.  
\(H\)-the height of lamp light center to the road surface (m).  
The single light source illuminance is calculated by Equation (1), while Equation (2) calculates the total illumination generated by several light sources.  
\[E_{p}=\sum_{i=1}^{n}E_{pi} \tag{2}\]  
According to Equations (1) and (2), total illuminance of P1, P2, and P3 are calculated respectively. The calculation results are shown in Table 6 (unit, lx):  
According to the illuminance design of conventional underground roads, luminosity is about 40-50 lx [20], Scheme B is consequently closer to the actual situation. In the following experimental studies, the xenon lamp model of Scheme B will be adopted.
# Adaptive Learned Belief Propagation for Decoding Error-Correcting Codes
## 3 Channel Models
### Performance Metrics  
\(Q\)_-factor_: The SNR per bit in the optical fiber channel is \(E_{b}/N_{o}\), where \(E_{b}=\mathcal{P}/m\) is the bit energy, and \(N_{o}=\sigma^{2}BN_{sp}\) is the total noise power in the link, where \(B=B_{0}N_{c}\). The performance of the uncoded communication system is often measured by the BER. The \(Q\)-factor for a given BER is the corresponding SNR in an additive white Gaussian noise channel with binary phase-shift keying modulation:  
\[\mathrm{QF}=20\log_{10}\Bigl{(}\sqrt{2}\operatorname{erfc}^{-1}(2\mathrm{BER })\Bigr{)},\quad\mathrm{dB}.\]  
Coding gainLet \(\mathrm{BER}_{i}\) and \(\mathrm{QF}_{i}\) (respectively, \(\mathrm{BER}_{o}\) and \(\mathrm{QF}_{o}\)) denote the BER and \(Q\)-factor at the input (respectively, output) of the decoder. The coding gain (CG) in dB is the reduction in the \(Q\)-factor  
\[\mathrm{CG}=\mathrm{QF}_{o}-\mathrm{QF}_{i}=20\log_{10}\operatorname{erfc}^{- 1}(2\mathrm{BER}_{o})-20\log_{10}\operatorname{erfc}^{-1}(2\mathrm{BER}_{i}).\]The corresponding net CG (NCG) is  
\[\text{NCG}=\text{CG}+10\log_{10}r_{\text{total}}. \tag{8}\]  
_Finite block-length NCG_: If \(n\) is finite, the rate \(r_{\text{total}}\) in (8) may be replaced with the information rate in the finite block-length regime [78]  
\[C_{f}\approx C-\log_{2}(e)\sqrt{\frac{\text{BER}_{i}(1-\text{BER}_{i})}{n}}Q^{ -1}(\text{BER}_{o}),\]  
where \(Q(x)=\frac{1}{2}\operatorname{erfc}\left(\frac{x}{\sqrt{2}}\right)\).
## 2 Photonic Crystal Cavities for Opto-Mechanical Interaction
### Discussion  
The interactions between an optical and mechanical resonator based on opto-mechanical cavities including 1D and 2D PhC cavities have been experimentally demonstrated with strong optomechanical coupling. In the mechanically tunable methods, N/MEMS actuators are employed to realize the displacement of photonic device in micro/nano scale. Under the applied electrostatic force or external force of N/MEMS actuation, a strong effect on the optical field could react with the displacement of boundaries or probe perturbation. Integration of N/MEMS and PhC cavities open the opportunities for sensing application based on nanoscale opto-mechanical interaction in PhC cavities. In the radiation pressure-driven schemes, both optical and mechanical resonators are fabricated in a nanostructure through periodic patterning, this makes the localized photon-phonon interaction happen in a single device. Compared to the Fabry-Perot cavity and Whispering-Gallery-Mode cavity, PhC cavity has a greatly reduced mechanical mass to the magnitude of a picogram, which is particularly suitable for ultrasensitive nanoparticle sensing using optomechanical backaction effects. Meanwhile, the beam width in PhC cavity approaching to or below the order of light wavelength confines the cavity mode in a nanoscale structure, resulting in a stronger optomechanical coupling than that obtained in F-P or WGM resonators. In addition, a flexible mechanical geometry can be effectively coupled with an optical mode driven by optical force, which provides a perfect platform to observe optomechanical dynamics, e.g., optical spring or damping effects, and to explore various sensing applications.
# The Investigation of a New End Wall Contouring Method for Axial Compressors
## 2 New End Wall Contouring Method
### Generating End Wall Contouring for the Actual Compressor  
After obtaining the end wall contouring in the standard space of \(\{(\epsilon,\xi,\eta)\}\ \varepsilon\in[0,1]\), \(\xi\in[0,1]\), \(\eta\in[-1,\ 1]\}\), the shape of the end wall contouring can be mapped directly to the three-dimensional end wall region of the axial compressor according to their spatial correspondence.  
As shown in Figure 5, the coordinate system of the compressor end wall region is \((r,\theta,\ z)\). \(r_{0}\) represents the radius of the baseline end wall surface; \(z_{\rm LE}\) and \(z_{\rm TE}\) represent the LE and TE axial coordinates; \(\theta_{\rm PS}\) and \(\theta_{\rm SS}\) represent the circumferential coordinates of the PS and the SS. Here, we correspond the end wall's LE and TE to \(\varepsilon=0\) and \(\varepsilon=1\) and the SS and PS that form the blade channel to \(\xi=0\) and \(\xi=1\), and we limit the waviness of the actual end wall contouring within \(\pm\Delta R\). Then, the end wall contouring in the standard space can be mapped to the actual axial compressor through  
\[\left\{\begin{array}{c}r=\eta\Delta R(z)+r_{0}(z)\\ z=\varepsilon(z_{\rm TE}-z_{\rm LE})+z_{\rm LE}\\ \theta=\xi(\theta_{\rm SS}(z)-\theta_{\rm PS}(z))+\theta_{\rm PS}(z)\end{array}\right. \tag{8}\]  
The above section discusses the definition and implementation process of this new end wall contouring method. Note that the new method can theoretically create arbitrary shapes by combining an infinite number of end wall contouring units. However, some preliminary studies indicate that combining one full-area unit and two localized units will usually be sufficient to control the corner separation. So, the new method generally requires less than 12 parameters for all the design processes.
# ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks
## 1 Introduction
#### Our Contribution  
The deep learning approach to ECG classification is similar to that of image classification, with the deep learning layer deepening and with decisioning based on a complex structure. Following this trend, we tried to find an end-to-end deep learning model suitable for ECG multi-classification only through changes in the deep learning architecture, without introducing a process of data transformation such as using a spectrogram.  
The major contributions of our paper are the following:  
1. A squeeze-and-excitation block was applied to the ECG classification model.
2. Our model was applied to a large-scale ECG dataset used in a hospital.
3. Our model, based on a squeeze-and-excitation residual network (SE-ResNet) surpassed the performance of a ResNet, which is known as one of the best models for ECG-signal multi-classification [5; 6], in terms of the F1 score.
4. We compared the inference time between the ResNet and the SEResNet, and confirmed that there is no significant difference in inference time.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Biological Processes
#### 4.3.2 Phytoremediation  
Phytoremediation is a bioremediation technique that exploits the capacity of plants to intercept, take up, accumulate, adsorb, or stabilize contaminants. The phytoremediation process aims to sequester contaminants via the roots of plants to lesser toxic elements or absorb them to the roots or shoots [102]. Some plants, called hyperaccumulators, can tolerate and accumulate more than 1000 mg\(\cdot\)kg\({}^{-1}\) of dry matter of copper, cadmium, chromium, lead, nickel, cobalt, or up to 10,000 mg\(\cdot\)kg\({}^{-1}\) of dry matter; this is to say, 1% by weight on a dry basis, of zinc or manganese [137].  
The process is slow, and, for this reason, if a plant has large roots and/or shoots, large quantities of pollutants can be accumulated.  
Several trees, as well as agricultural and herbaceous crops, are used, such as willlow (_Salix_ spp.) [103; 104; 105], poplar (_Populus_ spp.) [102; 103; 104], wheat (_Triticum-__L_) [106], sweet and grain sorghum (_Sorghum bicolor_) [107; 108], and smilo grass (_Piptatherum milliaceum_) [109]. The phytoremediation process can be assisted by natural chelators, which promote the bioavailability and adsorption of heavy metals, such as copper [138].  
Depending on the type of pollutants, plants, and removal mechanism, the phytoremediation techniques can be divided into phytostabilization, phytoevaporation, and phytoextraction.
## 3 Materials and Methods
### Planning the SLR Process
#### 3.1.1 Selection of the Database Source  
One of the basic steps for conducting an SLR is selecting a reliable and comprehensive database source. In this regard, Scopus is recognized as the main searching source by many SLR papers in the field of SCM [45]. In fact, Scopus covers various disciplines [46] and a more comprehensive number of journals than other literature resources [47] since it includes the papers in databases such as Web of Science [42; 45; 48]. In this regard, the authors based on [12], which is the work performing a comparison among Scopus, ProQuest, Web of Science, Science-Direct, and EBSCO databases for searching in the field of SC in a combination of LEAN and Industry 4.0, aim to figure out which database is the most comprehensive one. Finally, this comparison revealed that Scopus is the most valuable database by retrieving most of the related papers. However, from ScienceDirect and EBSCO least number of related papers were obtained. Hence, for performing the present SLR Scopus is considered for searching the relevant keywords. Moreover, from the results of Table 2, it is obvious that the majority of the SLR papers in this field have not been conducting a snowballing search. However, the authors in the present work, to be sure about the completeness of the obtained results, performed both forward and backward snowballing as a complementary procedure. In this regard, scientific data sources other than Scopus, such as ProQuest, Web of Science, Science-Direct, and EBSCO databases were investigated especially in relation to backward snowballing.
## 5 Numerical Simulations
### Antenna Array  
Numerical simulations have been conducted within the frame of the SMOS-HR project [11] for a cross-shaped array equipped with 167 antennas, as illustrated in Figure 4. This number includes two additional antennas in order to meet the redundancy requirement for being calibrated with the aid of the redundant spacing calibration method [31]. All the antennas are assumed to provide a measure of the average brightness temperature of the scene (contrary to what is done for SMOS where it relies only on few measurements provided by three reference radiometers [32]). The spacing between the antennas along the arms is set to \(\sqrt{2}\,d\), and one arm is judiciously translated with respect to the others so that the shortest spacing between the closest antennas is reduced down to \(d\). This design, which increases the angular extent of the synthesized field of view, and therefore reduces the aliasing, has been patented [33]. If at ground level the useful swath across the track of the satellite depends on the elevation \(h\) of the platform above Earth's surface and on its orientation \(\epsilon\) with respect to the nadir looking direction, the main driver turns out to be the ratio \(d/\lambda\), as introduced in Section 3. To complete the modeling of the instrument, here \(h\) has been fixed to 685 km and \(\epsilon\) to \(0^{\circ}\) for practical reasons. However, this parametric study is not sensitive to variations of \(h\) or \(\epsilon\).  
Figure 4: A cross-shaped array populated with 167 antennas. The red arm is translated with respect to the three others in blue, and the green antennas are added for calibration purposes. The spacing between the closest antennas, as illustrated, for example, by antennas #125 and #1, or by antennas #166 and #2, is set to \(d\). As a consequence of the geometry, the spacing between the antennas along the arms is equal to \(\sqrt{2}\,d\).
## 2 Related Work
The idea was to map the different network traffic to a similar metric that could improve the classification accuracy. Recently, in [34], an efficient way of fine-tuning the hyper-parameters for DQ-learning was suggested by the authors. They evaluated on the NSL-KDD dataset that a small exploration rate achieves better classification accuracy. An online version of DQN-based IDS was proposed in [35]. The idea was to use the auto-encoders in the Q-network of RL as function approximators. It helped to improve the classification accuracy while real-world data were processed through the RL-trained network. The use of auto-encoders greatly reduces the effect of human interaction and thus achieves significantly higher efficiency during the online training mode. Most previous IDS research work is focused on how to improve the performance, or how the suggested strategies fare in contrast to others of a similar kind that rely on Machine Learning (ML). Based on the literature review, in this paper, our goal is to provide a general RL framework for both packet- and flow-level IDS problems with improved robustness. The proposed method can not only detect the attack traffic but can also match the attack traffic into separate classes, including normal traffic and different types of attacks, which, to the best of the authors' knowledge, has not been widely studied in the literature. The structure of the IDS at the packet and flow level is clearly laid out, and the design of each module is elaborated on in detail. The robustness of the proposed algorithm is improved by using the notion of a negative (sample) agent during the RL training. The exploration rate is adjusted based on the extensive search and bound method. Quantitative results of the proposed RL algorithms are shown to verify that the presented framework can effectively classify the regular traffic with higher accuracy and explicitly identify each attack separately using both packet and flow level information.
## 3 Results
### Factors Influencing Optical Property Extraction of Top Layer  
Accurate optical property extraction from the top layer is critical for the stepwise method because estimation results of the bottom layer are based on those from the top layer. Influencing factors of \(m\!f\!p_{1}\)\({}^{\prime}\)/\(m\!f\!p_{2}\)\({}^{\prime}\), \(\mu_{a1}\)/\(\mu_{a2}\) and \(\mu_{s1}\)\({}^{\prime}\)/\(\mu_{s2}\)\({}^{\prime}\) determine the contributions of each layer on diffuse reflectance. Larger contribution implies higher accuracy for estimating the corresponding optical properties theoretically. Parameter of \(\mu_{s1}\)\({}^{  
Figure 4: Absolute error contour maps for estimating \(\mu_{a1}\) (**left panel**) and \(\mu_{s1}\)\({}^{\prime}\) (**right panel**) of four representative two-layered simulation samples with varying values of \(mfp_{1}\)\({}^{\prime}\)/\(mfp_{2}\)\({}^{\prime}\) by using the stepwise method with different start and end frequencies. The values of \(mfp_{1}\)\({}^{\prime}\)/\(mfp_{2}\)\({}^{\prime}\) were 0.4, 0.8, 1.2 and 4 for (**a**), (**b**), (**c**) and (**d**), respectively. Different colors in the vertical bars on right of each graph denote different levels of error in percentage. Note that the absolute errors of \(\mu_{a1}\) and \(\mu_{s1}\)\({}^{\prime}\) larger than 60 and 20% were treated as 60 and 20% for better visual effect.  
Figure 5: Absolute error contour maps for estimating \(\mu_{a1}\) (**left panel**) and \(\mu_{s1}\)(**right panel**) of four representative two-layered simulation samples with varying values of \(\mu_{s1}\)/\(\mu_{a1}\) by using the stepwise method with different start and end frequencies. The values of \(\mu_{s1}\)/\(\mu_{a1}\) were 133, 50, 20 and 5 for (**a**), (**b**), (**c**) and (**d**), respectively. Different colors in the vertical bars on right of each graph denote different levels of error in percentage. Note that the absolute errors of \(\mu_{a1}\) and \(\mu_{s1}\)\({}^{\prime}\) larger than 60 and 20% were treated as 60 and 20% for better visual effect.  
for apple fruit with typical values of \(\mu_{a}\) being 0.01-0.05 mm\({}^{-1}\) and \(\mu_{s}{}^{\prime}\) being 1.0-2.0 mm\({}^{-1}\), respectively, in the wavelength region of 500-1000 nm. Note that the illumination pattern in this case is modulated only along the horizontal axis (i.e.,\(f_{y}=0\)) with the spatial frequency \(f_{x}\) varying from 0.01 to 0.3 mm\({}^{-1}\).  
In order to improve estimation accuracy for optical properties of two-layered horticultural products by using the stepwise method, it is suggested to have relatively large spatial frequency for estimating \(\mu_{a1}\) and \(\mu_{s}{}^{\prime}\) through one-layered model, and relatively small frequency for estimating \(\mu_{a2}\) and \(\mu_{s2}{}^{\prime}\) through two-variable fit based on two-layered model. Effect of tissue optical properties on frequency selection should also be taken into account, which is consistent with the suggestions discussed in Section 3.3 (i.e., optimize the spatial frequency in terms of \(mfp_{1}{}^{\prime}\) and/or \(mfp_{2}{}^{\prime}\)).
## 4 Protocol Description
### Initialized Routing Structure
#### Periodic Sink Update:  
With the HEIGHT message broadcast process, an initial neighborhood is formed by each sensor for which it records neighbor ID, height, state, estimated delay, residual energy of all neighbors, as well as the transmission power that the node uses to communicate with its neighbor on the path to the sink. Each sensor records its own ID, state, and residual energy. In addition, each node maintains sink ID with its minimal-delay sink. In a fire scenario, the sink may become disabled and the network's topology will be changed by the fire. To ensure robust connectivity, each sink will periodically send out a HEIGHT message to refresh the network. The refresh rate is a protocol design parameter that trades off overhead for increased robustness to lost HEIGHT messages and path changes. In a fire situation one would expect to decrease the period, although the impact on network traffic load must also be examined.