esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### Comparison Results and Discussion of Various Methods
#### 5.4.2 Selection Among Various Methods  
During the preliminary research, we found that geologists have proposed multiple leveling methods, each suitable for different data characteristics. Among these, the most widely used method is the CR method, which is applicable to most data leveling tasks.  
Figure 15: Geochemical maps of each element after MSC (**a**–**l**) corresponding to the 12 elements.  
However, it still has limitations. For instance, in small-scale map sheets, the large area covered by the map sheet often spans different geological units or even geochemical landscapes. As a result, if one map sheet is treated as a whole for leveling, errors may arise.  
The BL method is based on the assumption that the element distribution pattern within the same geological unit should be similar. The difficulty lies in selecting the appropriate boundary range, and its applicability is also limited--requiring the geological units at the boundary to be continuous. Despite these limitations, findings indicate that this method can be applied to both large-scale and small-scale maps, suggesting its reliability may be higher than that of the CR method.  
The MSC method yields results similar to those of the BL method. However, it does not require considering differences between boundary geological units, making it less restricted and more widely applicable. However, it does require the availability of geochemical data at multiple scales.
## 3 Methodology
### Flood Signal Detection
Objects whose F value is greater than or equal to 3 are classified as paddy rice. \[\text{F}=\sum\text{Signal}_{\text{flood}} \tag{6}\]  
Figure 7 shows the time-series optical and microwave signal from the Sentinel-2/1 over an exemplar object (latitude: 30.6444\({}^{\circ}\) N, longitude: 120.8041\({}^{\circ}\) E). There are ten observations acquired from the Sentinel-1 and twelve from the Sentinel-2 in the specified time window based on the method described in Section 3.2. Six optical observations are obscured by clouds, and four (fourth through the eighth) have positive flood signals because the preceding and following observations (third and ninth) have significant positive flood signals. This is illustrated in the time-series optical signal graph and corresponding images in the first row. Meanwhile, eight of the ten Sentinel-1 observations have positive flood signals with relatively low VH backscatter coefficients. Thus, integrating Sentinel-1 with Sentinel-2 increases the sum of positive flood signals over the object from three to eleven. It is noted that the ninth and twelfth observations from Sentinel-2 in the time window are partially contaminated by clouds, but not flagged in QA bands.
## 4 Discussion
### Handling Imbalanced Data  
Imbalanced data refer to outcome classes that appear with different frequencies. Such data pose a challenge for prediction since the default parameters of machine-learning algorithms are designed for balanced data. This can result in poor predictive performance, specifically for the minority class. For example, as shown in Table 2, in our binary classification problem for MCI and AD, the minority class (MCI and AD) appeared with a 22% probability. Applying a machine-learning algorithm naively without considering this class imbalance might lead to the algorithm always predicting the majority class (normal control), which here automatically resulted in 77.94% accuracy.  
We performed and compared three different methods for handling the problem of imbalanced data for MCI and AD: (1) downsampling the normal control group, (2) upsampling the MCI and AD group, and (3) hyperparameter tuning. First, we randomly subsampled the majority class to reach the size of the minority class and obtained the following performance: sensitivity = 0.586, specificity = 0.528, PPV12 = 0.145, NPV12 = 0.903, and AUC = 0.616. Second, we randomly sampled from the minority class to reach the size of the majority class and achieved the following performance: sensitivity = 0.440, specificity = 0.660, PPV12 = 0.150, NPV12 = 0.896, and AUC = 0.576. The third method was the hyperparameter tuning method that we presented in the Results section, which outperformed the two resampling methods (downsampling and upsampling). This was consistent with the findings from [15]. Our results also demonstrated that the gamma, c, and class.weight values were key hyperparameters that could be used to train the most optimal SVM model using the RBF kernel for imbalanced data.
## 3 Results and Discussion
### Aerosol Optical Properties
#### 3.3.4 Aerosol Vertical Structure during Haze
It can be seen in Figure 5c that most values of V(z) are below 0.2 between heights of 0-6 km, which indicates that the aerosols were primarily regular shapes. Figure 5d shows that X(z) is mainly 0.5-1.0 within the height range of 0-1.5 km, which indicates that particle sizes are not large at low altitudes, which is consistent with the \(\alpha_{\mathrm{s}}\) values obtained from the ground-based nephelometer. Figure 6 shows the mean extinction coefficient profiles from the CALIPSO Level 2 aerosol profile product. The blue and red colors represent the profiles not processed and processed by quality  
screening, respectively. Below 1 km height, the extinction coefficient remains as high as 1 km\({}^{-1}\), which was very close to the mean extinction coefficient (1.27 km\({}^{-1}\)) measured by the nephelometer and aethalometer on the ground surface. The extinction coefficient then gradually decreases above 1 km, which indicates that the highest concentration of pollutants was below 1 km. Figure 5: Information on structure of aerosol vertical distribution from CALIPSO at 02:19:48 (LZT) on 12 June 2012. (**a**) 532 nm total attenuated backscatter coefficient; (**b**) Level 2 aerosol sub-layer classification information (“-1”: undefined signal, “0”: clean marine aerosol, “1”: dust aerosol, “2”: polluted continent aerosol, “3”: clean continent aerosol, “4”: polluted dust aerosol, and “5”: smoke aerosol); (**c**) Volume depolarization ratio; (**d**) Total attenuated color ratio. Figure 6: Information on aerosol extinction coefficient profile from CALIPSO at 02:19:48 (LZT) on 12 June 2012 (blue: without quality screening; red: with quality screening).
# Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks
## 3 Materials and Methods  
### Dataset  
In order to carry out the study, 29 sequences of thermal images covering various environments and objects were recorded. The set included 7060 thermal images (2564 without hands, 4496 with left or right hands). The images were recorded in sessions of different durations, at different times of the day and included various positions and cases of hot objects and hands. The resolution of thermal images sent to the CNN model, after preprocessing, was 120 \(\times\) 160 pixels. The temperatures of hot objects ranged from tens to hundreds of degrees of Celsius. The smartphone and thermal camera were vertically positioned. The set of images was then artificially expanded using data augmentation by randomly rotating the images in the range of \(\pm\)5\({}^{\circ}\) (each of the 7060 images was subjected to random rotation, which increased the set to 14,120 images) and randomly increasing the image temperature in the range of 1-5 \({}^{\circ}\)C (randomly increasing the temperature/brightening of real images), which resulted in 21,180 images (more than 4 times as much as applied in [19]). The set was then divided into training and test sets, in several variants, in order to verify the impact of the set on the learning process. Figure 2 shows sample images representing everyday situations, different levels of threat (no hot objects, warm or hot objects in the field of view, arm or hand near a hot object), objects and devices that workers, blind or visually impaired people may come into contact with in everyday activity.  
Figure 2: Examples of thermal images from the test set.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
###### Abstract  
Heterogeneous architecture-based systems-on-chip enable the development of flexible and powerful multifunctional RF systems. In complex and dynamic environments where applications arrive continuously and stochastically, real-time scheduling of multiple applications to appropriate processor resources is crucial for fully utilizing the heterogeneous SoC's resource potential. However, heterogeneous resource-scheduling algorithms still face many problems in practical situations, including generalized abstraction of applications and heterogeneous resources, resource allocation, efficient scheduling of multiple applications in complex mission scenarios, and how to ensure the effectiveness combining with real-world applications of scheduling algorithms. Therefore, in this paper, we design the Multi-Application Scheduling Algorithm, named MASA, which is a two-phase scheduler architecture based on Deep Reinforcement Learning. The algorithm is made up of neural network scheduler-based task prioritization for dynamic encoding of applications and heuristic scheduler-based task mapping for solving the processor resource allocation problem. In order to achieve stable and fast training of the network scheduler based on the actor-critic strategy, we propose optimization methods for the training of MASA: reward dynamic alignment (RDA), earlier termination of the initial episodes, and asynchronous multi-agent training. The performance of the MASA is tested with classic directed acyclic graph and six real-world application datasets, respectively. Experimental results show that MASA outperforms other neural scheduling algorithms and heuristics, and ablation experiments illustrate how these training optimizations improve the network's capacity.  
multiapplication scheduling; heterogeneous resources; combinatorial optimization; deep reinforcement learning; training optimization methods
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 5 Conclusions  
Wavelet transform is a signal analysis tool. The tight support and orthogonality of wavelet function cause it to have good performance in signal de-noising. A new de-noising method of ECG signals based on wavelet energy and sub-band smoothing is proposed in this paper. The wavelet coefficients that required threshold de-noising are determined by wavelet energy, and then the sub-band smoothing filter is used for further de-noising. The experimental results show that the proposed method can significantly improve the quality of ECG signal, and the de-noising results have higher SNR, lower MSE and lower PRD. In the future, we will attempt to adopt the image/video processing methods [31; 32; 33; 34; 35; 36; 37; 38] to process ECG signal. With the wide application of deep learning in image processing, We will adopt network optimization methods [39; 40; 41; 42; 43; 44; 45; 46; 47; 48] to improve the real-time and high efficiency performance of ECG signal processing.  
Conceptualization: D.Z. and S.W.; investigation: J.W.; methodology: D.Z. and S.W.; software: D.Z. and S.W.; supervision: [PERSON]; validation: A.K.S. and V.S.S.; Writing--Original draft: D.Z. and S.W.; Writing--Review and editing: [PERSON], A.K.S. and X.D.  
This research was funded by the National Natural Science Foundation of China (61772454, 61811530332, 61811540410, 61772087, 61232016,61972057, U1836208), the \"Double First-class\" International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology (No. 2018 IC25) and the Young Teacher Growth Plan Project of Changsha University of Science and Technology(No. 2019 QJCZ076), Hunan Provincial Natural Science Foundation of China(No.2019J50655,2019J50649), Scientific Research Foundation of Hunan Provincial Education Department of China (No.18B160,18C0238),Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle Infrastructure Systems (Changsha University of Science and Technology) (No. kfj180402).  
The authors declare no conflict of interest.
## 4 Numerical Results and Discussion
Similar to rigid body, Lagrange multipliers can be used to compute reaction forces acting on flexible bodies. Figures 14 and 15 shows the reaction forces between flexible and grating base used in design processes. Figure 12: Flexible body 2 elastic displacement. Figure 13: Flexible body 2 elastic orientation. The material selection as well as the geometrical dimension of flexible and flexure bodies should be estimated to ensure the system's stability. Therefore, the stiffness of the flexible bodies must be much lower than the rest of the system components with considerable factor of safety. Table 4 illustrates the primarily design properties of flexible and flexure bodies. Dynamic reactions forces acting on flexible and flexure bodies are obtained and fed forward to FEATool Multiphysics to examine the stresses with maximum range of grating movement. According to the DFD procedures shown in Figure 3, the stress distribution of flexible body2 with the corresponding displacement along Z-axis show the highest of 1.5 \(\upmu\)m, see Figures 16 and 17. It is found that, the maximum stress value at the neck \begin{table}
\begin{tabular}{c c c} \hline
**Properties/Body** & **Flexible** & **Flexure** \\ \hline Mass (Kg) & 0.012 & 0.068 \\ Density (Kg/m\({}^{2}\)) & 8000 & 2810 \\ Elastic Modulus (GN/m\({}^{2}\)) & 193 & 72 \\ Poisson ratio & 0.27 & 0.33 \\ Tensile strength (MN/m\({}^{2}\)) & 5800 & 2200 \\ Yield strength (MN/m\({}^{2}\)) & 1720 & 950 \\ \hline \end{tabular}
\end{table} 
Table 4: Flexible and Flexure bodies mass properties. Figure 14: Reaction forces at fixed joint between flexure and flexible body 2. Figure 15: Reaction moments at fixed joint between flexure and flexible body 2.  
point is 700 MN/m\({}^{2}\) which is lesser than yield strength of flexible body material, see Table 4. In addition, the maximum value of the stress on the flexure bodies is less than its material yield strength with factor of safety 3.4. At the end of the procedures, the designed parameters of grating device reach the required performance without breakdown.
# Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks
### Applications of Blockchain in 6G Network
#### 1.1.5 Smart Grid  
A smart grid is introduced to meet people's higher electricity needs and optimize resource allocation. The smart grid makes better decisions about power and energy-sharing data. It supports management approaches for peak energy demand and also reduces energy consumption by exchanging the data of energy feedback and power consumption [24]. However, due to the possibility of malicious access during information exchange over the insecure communication network, multiple entities are unwilling to convey their information to protect their data privacy. Moreover, there are various types of cyberattacks in smart grid systems, such as session hijacking, nonrepudiation, denial-of-service (DoS), flooding, spoofing, and man-in-the-middle (MiTM) attack [25]. To overcome the security and privacy issues, smart technology is required that provides trust, reliability, and data-sharing security. Many researchers adopt blockchain as a solution to secure the smart grid. For instance, the authors of [26] presented a secure and reliable data exchange scheme by utilizing the processing mode. The proposed scheme provides transparency and auditability of energy production, pricing, and services in smart grids. They utilized smart contracts to authenticate and track the data flow between different blockchain members. Additionally, they incorporated a distributed ledger that provides an immutable and transparent data usage record. As a result, their adopted scheme outperforms in terms of payoffs compared with baseline works. Further, [PERSON] et al. [27] focused on the efficiency and privacy issue in the current electricity trading scheme. Therefore, they analyzed a decentralized scheme of peer-to-peer electricity trading by introducing smart meters. They utilized a blockchain wallet for transactions and a smart contract to initiate the transmission switch instructions. The results revealed that the proposed scheme reduces the cost and latency. Furthermore, they offered fast and privacy-aware roaming to prove the proposed scheme's feasibility and efficiency.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 5 Experimental Results and Comparison  
The algorithms listed in Section 4 were run on both \"lane detection\" and \"traffic status\" datasets. The machine learning models were run on a data set split into 70% for training and 30% for testing. A clear distinction between the models is described in this section. Due to the imbalanced data set, the following standard set of evaluation metrics was used to evaluate the performance of the classifiers:  
**Accuracy:** This is a common evaluation metric and is used to compare the four different classification models. Generally, accuracy is used to determine how often the predictions made are true and in favor of or against the event with respect to all the instances of the event;  
**Precision:** This parameter is used to determine the successful predictions from all predictions made in favor of the event. It provides a variable to determine the ability to successfully predict an event;  
**Recall:** This metric is used to determine the successful predictions from all true instances of the event. It provides a parameter to determine how true the made predictions are in favor of an event with respect to all of its instances;  
**F1-score:** This gives a weighted average of the precision and recall metrics. It is the best metric for averaging out and balancing all the evaluation metrics as a whole;  
**ROC curve:** This provides a trade-off between the true positive rate (ratio of correct observations to total number of observations) and false positive rate (ratio of false observations to total number of observations);  
**Macro and micro evaluation metrics:** These are evaluation metrics that are widely used when working with multi-class classification problems. Macro evaluation computes the average of each class evaluation metric, such as precision. Micro evaluation focuses on the data from each class and computes its metric with respect to the entire data.  
It should be noted that the dataset used in this paper is imbalanced in terms of the output classes. Therefore, micro evaluation metrics would provide a better understanding of the performance of the models.
# Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
## 4 Proposed Platoon-Based Coordination Scheme to Minimize Traffic Congestion
### Setting up a General Kinematic Model for the Vehicles  
The first task is to set up a general kinematic model for the vehicles [37]. The goal is to keep the vehicles moving by a consistent rule. When a vehicle moves on the road, it can involve two maneuvers: (i) straight-moving; (ii) lane-changing. A vehicle \(i\) at the position \(x_{i}\) and velocity \(v_{i}\) is in the straight-moving mode with a front vehicle \(i-1\) if they satisfy the following condition:  
\[\begin{cases}v_{i}=\frac{dx_{i}}{dt}=\dot{x}_{i},\\ a_{i}=\frac{dv_{i}}{dt}=\dot{v}_{i},\\ \varepsilon_{i}=x_{i}-x_{i-1}-L_{i-1}-d_{gap},\end{cases} \tag{2}\]  
where \(a_{i}\) denotes the vehicle \(i\)'s longitudinal acceleration. For the sake of safety, the vehicle \(i\) must maintain a safety distance, \(d_{gap}\), with the vehicles that are immediately before or after it. \(\varepsilon_{i}\) denotes the relative distance between two vehicles. \(L_{i-1}\) is the length of the preceding vehicle. In this case, \(\varepsilon_{i}\) is bound by the constraint \(\varepsilon_{i}\geq T\times v_{i}\). \(T\) is the time gap and can be measured by the time required for the vehicle \(i\) to react to the possible collision and brake to stop, e.g., \(\varepsilon_{i}=t_{react}+t_{brake}\). For humans, \(t_{react}\) is approximately 1 s. For autonomous driving, \(t_{react}\) is negligible [38]. The time for braking is \(t_{brake}=\frac{v_{i}}{a_{i}}\). In practice, a vehicle moving at 20 m/s (72 km/h) does not often come to a stop in less than 5.0 s if \(a_{i}=4\) m/s. In addition, as mentioned above, the velocity of every vehicle is limited by \(v_{max}\). Therefore, \(d_{gap}=(t_{react}+t_{brake})a_{i}\).  
Note that, in the straight-moving maneuver mode, the vehicle \(i\) must first find a safe gap in the lane. However, if there is such a safe gap in the lane-changing maneuver mode, the vehicle may select a lane-changing action.
# Ten Years of Experience with Scientific TerraSAR-X Data Utilization
## 3 Results
### Proposal Analysis Overview
#### 3.1.4 Geosphere  
Most of the TerraSAR-X proposals are related to the geosphere--one-fifth intends to use the data to improve the understanding of the dynamics of the Earth's crust as well as soil properties and erosion processes (see Figure 7). 37 percent of the geosphere proposals address tectonic issues. Co- and post-seismic surface deformations after an earthquake event are investigated by providing displacement fields and trends [116; 117]. The findings help, for example, to improve the understanding and identification of the local tectonic settings [118] and even the interaction between two subsequent earthquake events [119] by combining the TerraSAR-X derived displacement field with GPS measurements and elastic dislocation models. Earthquakes can also be triggered by ground water extraction [120].  
Observation of active volcanos is addressed by 27 percent of the geosphere-related proposals. Since their surface is constantly and rapidly changing, TerraSAR-X data are used to determine surface deformation caused by emptying and refilling of magma reservoirs [121]. Also, the growth and collapse of lava domes is monitored [122]. High-resolution X-band data are particularly utilized for the identification and analysis of small scale surface deformations at the caldera's rim and walls [123]. Moreover, the rapidly changing geomorphology and deposits of eruptive volcanos is investigatedby evaluating the backscatter changes [124]. TerraSAR-X data are further used for the monitoring of geothermal fields [125; 126; 127]  
The identification of landslides, their spatial extent and dynamics is focus of 25 percent of the proposals. Landslides mostly appear on hill slopes dominated by weakly consolidated sediments [128]. They can be triggered by earthquakes, hydrological factors [129], melting of permafrost [130] and other erosion processes [131], but also human activities like cutting steep slopes to accommodate roads [132]. Inventories help to assess landslide hazards to protect people, properties and infrastructure [133; 134].  
A smaller share of proposals (12 percent) concentrate on soil mapping. Here, high-resolution X-band data are utilized for top-soil mapping [135], soil surface texture [136] and the investigation of erosion and sedimentation processes [137]. Other proposals assess the impact of ground water extraction [138]. A special case is the formation of sinkholes when subsurface cavities are formed which suddenly collapse [139].  
All these applications benefit from the high spatial resolution and the short wavelength of TerraSAR-X. These features, together with the high observation frequency, provide a very sensitive observation and mapping tool. Accordingly, the higher resolution StripMap and Spotlight modes are requested. SAR interferometry is by far the dominant measurement technique of this research field and is mentioned in 84 percent of the proposals. SAR polarimetry is rarely applied; only 4 percent request polarimetric data.