esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Stress State of the Main Beam Structure under Corrosion Fracture of Stay Cable
### Longitudinal Bridge Stress Analysis  
The local stress cloud diagram of the longitudinal stress of the main girder under different working conditions is shown in Figure 12. The local finite element model of the main girder is mainly in the compressive state, and the stress range is between \(-4\) MPa and \(-10\) MPa. The compressive stress of the roof of the main girder is greater than that of the bottom plate. The tensile stress appears in a small range between the anchorage zone and the top cable duct outlet, and the maximum tensile stress reaches \(0.612\) MPa. Figure 12b is the longitudinal local stress cloud diagram of the main girder after the fracture of the A1 cable. The spatial stress distribution of this working condition is similar to that of the initial state, and the overall stress range of the main beam is between \(-5\) MPa and \(-12\) MPa. Figure 12c shows the main girder's longitudinal local stress cloud diagram after the fracture of the A2 stay cable, and the overall stress range of the main girder is between \(-6\) MPa and \(-13\) MPa. It can be seen from the figure that with the influence of broken cables, the compression state of the main girder floor gradually weakens and tends to diverge. When the A2 cable breaks, the area of stress reduction increases by nearly \(40\%\).
# A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
## 2 Materials and Methods
### Implementation Strategy
#### 2.1.2 AC Attribute Models
Using the healthcare use case as an example, SAML v2.0 for healthcare-defined attributes [32; 33] are input to the AC policy decision process. This is illustrated for a big data Hadoop ecosystem in Figure 5.  
Encryption provides strong enforcement of AC policies. Attribute-based encryption is a primary research area. Applying encryption in the file system and through proxy gateways are the leading methods. In file system attribute-based encryption (ABE), object data are encrypted with the key associated with a set of attributes. Only subjects, users, or processes requesting access with matching attribute sets have access to the key. Combinations of hierarchical relationships between subjects, objects, and their associated attributes  
Figure 5: Attributes assigned and managed for AC policy decisions in the healthcare use case. The shades of the color red indicates sensitive data, shades of green indicates public data that may contain PII, and yellow indicates data where sensitive information has been removed. have been investigated to meet various objectives. The various research projects analyze the efficiency, methods of proof, and alternatives to organize attributes and associated keys. ABE is a security service subsequent to the AC decision process that further protects the data. This area is covered in additional detail in Refs. [34; 35; 36].
###### Abstract  
In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users' throughput. Different from other model-based reinforcement learning methods, such as the Deep \(\mathcal{Q}\) Network (DQN), the proposed method combines the Evolution Algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves.  
H  
APS; antenna control; reinforcement learning; evolution algorithm +
Footnote †: journal:
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 2 JSC Network Configurations
### Monostatic Deployment  
The monostatic setup employs radio signals to survey the environment, capturing echoes from targets via a sensing receiver co-located with the transmitter. To illustrate, consider a BS that employs the echoes of its own data-carrying downlink communication  
Figure 1: Urban scenario depicting monostatic, bistatic, and multistatic deployments where various targets, including pedestrians, cars, and UAVs, are detected, located, and tracked using JSC technology (figure background designed by macrovector/Freepik).  
Figure 2: On the left, extended vehicle target model with distributed dispersers and pedestrian model with point-like reflector. The reflections depend on the relative orientation of the visibility cone with respect to the sensing beam. On the right, the three sensing configurations for a JSC system in an urban scenario.  
signals to extract information about the surroundings. Addressing self interference (SI), effective beam management, and spurious targets is essential for safety-critical operations like collision avoidance and real-time object tracking when deploying such systems.
## 4 Proposed Model: RBF-Refined SVM Model
### Pre-Processing: Data Initialization  
The HETVNET's traffic dataset from CRAWDAD was transformed to an SVM format for training and testing. Each instance or observation within the dataset was represented as a vector of real numbers. In our dataset, there were few categorical features or attributes. The categorical features and attributes were converted to numerical data. For example, a packet receiving a categorical feature, such as  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SVM Type** & **Kernel Function \(K(x_{i}y)\)** \\ \hline Linear & \(x_{i}^{T}y\) \\ Polynomial & \((\gamma x_{i}^{T}y+r)^{\text{d}}\) \\ Sigmoid & \(\tanh(\gamma x_{i}^{T}y+r)\) \\ Radial Basis Function (RBF) & exp (\(\gamma\|x_{i}-x_{j}\|^{2}\), \(\gamma>0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Kernel functions in support vector machines (SVMs).  
Figure 1: Non-linearization (Source: Introduction to Statistical Machine Learning, MIT Press, Cambridge, MA, USA, 2012 [7]).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kernel** & **Complexity** & **Optimality** & **Accuracy** \\ \hline Linear & High & High & Medium \\ Polynomial & High & Medium & Medium \\ RBF & Low & High & High \\ Sigmoidal & Medium & Low & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance measures of SVM kernels.  
(Yes, No), was represented as (1, 0). It was also recommended by some researchers to scale the data before application to the SVM. Scaling the numerical range of values helps to avoid complex numerical computations, as the kernels rely on the products of the vector features. In this paper, we did not scale our traffic data, as the usage of linear or polynomial kernels was exempted from this dataset. The usage of linear and polynomial kernels can demonstrate various difficulties in numerical calculations. Henceforth, the transformed SVM-formatted data endured its path forward in making the right choice of model, prior to its application to the SVM.
###### Abstract  
In electric powertrain traction applications, the adopted trend to improve the performance and efficiency of electromechanical power conversion systems is to increase supply voltages and inverter switching frequencies. As a result, electrical machine conductors are subjected to ever-increasing electrical stresses, leading to premature insulation degradation and eventual short-circuits. Winding condition monitoring is crucial to prevent such critical failures. Based on the scientific literature, several methods can be used for early identification of aging. A first solution is to monitor partial discharges. This method requires the use of a specific measurement device and an undisturbed test environment. A second solution is to monitor the inter-turn winding capacitance, which is directly related to the condition of the insulation and can cause a change in the stator impedance behavior. Several approaches can be used to estimate or characterize this impedance behavior. They must be performed on a machine at standstill, which limits their application. In this paper, a new characterization method is proposed to monitor the high-frequency stator impedance evolution of voltage source inverter-fed machines. This method can be applied at any time without removing the machine from its operating environment. The range and accuracy of the proposed frequency characterization depend in particular on the supply voltage level and the bandwidth of the measurement probes. The effects of parameters such as temperature, switching frequency, and DC voltage amplitude on the impedance characteristic were also studied and will be presented. Tests carried out on an automotive traction machine have shown that the first two series and parallel resonances of the high-frequency impedance can be accurately identified using the proposed technique. Therefore, by monitoring these resonances, it is possible to predict the aging rate of the conductor.  
winding health monitoring; frequency response analysis; online characterization; impedance; high-frequency behavior; inverter-fed electrical machines +
Footnote †: journal: applied sciences  
0000-0001-5882-788X][PERSON]  
1 IFP Energies Nouvelles, Institut Carnot IFPEN Transports Energie, 1 te 4 Avenue de Bois-Preau, 92852 Rueil-Malmisson, France; [EMAIL_ADDRESS] (J.C.); [EMAIL_ADDRESS] (N.H.O.)
###### Abstract  
The amount of solid waste generated annually is increasing around the world. Although the waste has a high calorific value, one major obstacle that may prevent it from becoming a feedstock for power applications is the existence of polyvinyl chloride (PVC), which causes corrosion and emission issues after combustion due to its high chlorine content. Torrefaction is known to release hydrochloric acid; thus, it has been applied in this study for the reduction of chlorine from potential waste feedstocks. Fiber-plastic (60-40%) waste blends, with different chlorine content levels, as well as PVC were used in the current study. Torrefaction was conducted at 400 \({}^{\circ}\)C. Chlorine and heat content were measured. Experimental results showed that organically bonded chlorine was reduced during torrefaction as a function of mass loss. The chlorine removal efficiency was only dependent on temperature and residence time, not chlorine level. The heat content of the sample increased with mass loss up to a maximum of \(\sim\)34 MJ/kg at \(\sim\)45% mass. It was also observed that at \(\sim\)30% mass loss, the organic chlorine content per unit heat content reduced by \(\sim\)90%, while the heat content was \(\sim\)32 MJ/kg, and \(\sim\)90% energy was retained.  
Keywords:solid waste; plastic; PVC; torrefaction; chlorine removal; heat content +
Footnote †: journal: Applied Sciences
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## 2 Method
### Data Description
#### 2.1.3 Long Time Series Dataset of Snow Depth in China  
The long-term series of daily snow depth dataset in China (1979-2019), released by the National Tibetan Plateau Data Center (NTPDC), provides daily snow depth distribution data for China from 1 January 1979 to 31 December 2019. The raw data used to invert this snow depth dataset are from the scanning multichannel microwave radiometer (SMMR) (1979-1987), the special sensor microwave imager (SSM/I) (1987-2007), and the special sensor microwave imager/sounder (SSMI/S) (2008-2019) daily passive microwave bright temperature data (EASE-grid), processed by the National Snow and Ice Data Center (NSIDC). It has a spatial resolution of 25 km, which can be used for climate analysis, hydrological modeling, and water management on a large scale and in long time series [33-35]. During the processing of the data, the brightness temperatures of different sensors are first cross-calibrated, and the observations affected by the snow depth observation errors of ground stations, the surface water, and the liquid water content in the snow layer are removed before the inversion of the snow depth.  
Using this dataset, we calculated the snow cover change in the Tibetan Plateau between January 2018 and December 2019, and the corresponding snow depth changes for four  
Figure 2: (**a**) The IGBP land cover type on the Tibetan Plateau; (**b**) the land cover types on the Tibetan Plateau are reclassified into four types.  
different land cover types. The snow accumulation trend under different land cover types is similar. From October every year, the Tibetan Plateau gradually enters the snowfall period, reaching the peak of snowpack in January and February of the following year. The snow gradually melts after April as the temperature rises. Figure 3 shows the snow depth map of the Tibetan Plateau for one month in summer and winter using this dataset.  
By comparing the values with the daily reflectivity data of CYGNSS in the same region, the correlation between snow depth and reflectivity can be inferred, and the potential of CYGNSS in snow feature research can be verified.
# Simulation of Calibrated Complex Synthetic Population Data with XGBoost
## Appendix B Details on the Simulated Annealing Algorithm for the Calibration of Populations
### Initialise and Adjust Number of Swaps \(N\)  
The convergence of the SA algorithm is greatly influenced by the initial number of swaps (\(n\)). Choosing a very high \(n\) can lead to frequent changes in \(\Delta\) due to a high acceptance rate for a worse solution. In contrast, selecting a very low \(n\) can limit the potential to improve the objective function. Naturally, \(n\) should depend on the initial value of \(f(\mathbf{t},\mathbf{\hat{t}})\) and the size of the synthetic population. An initial choice of the swap rate \(n\) is as follows:  
\[n=\min(\max_{m,r}(|t_{m,r}-\hat{t}_{m,r}|)\cdot\frac{2}{3},\ 0.05\cdot\frac{N_{P}}{ \text{med}_{\text{resize}}}).\] (A1)The first part is related to the objective function, and the second to the size of the target population. This suggested starting value proved practical during the work in [29].  
The swap rate \(n\) can also be controlled during the SA algorithm for faster convergence. When selecting or deselecting individuals, the whole household is chosen; thus, the number of individuals in the synthetic population can change, affecting the algorithm's convergence. To address this, a scaling factor is introduced to maintain the total number of individuals or households at the same level as the target margins. Before the sampling step, the number of draws for the selection of persons and households to be included and excluded is adjusted by  
\[n_{in}= \max(\lceil n-gap\rceil,1) \tag{2}\] \[n_{out}= \max(\lceil n+gap\rceil,1)\quad, \tag{3}\]  
where  
\[gap= r_{re}\cdot\frac{1}{\sum\limits_{r=1}^{R}M_{r}}\left(\sum \limits_{m=1}^{M}\sum\limits_{r=1}^{R_{m}}t_{m,r}-\hat{t}_{m,r}\right) \tag{4}\] \[r_{re}\in (0,1). \tag{5}\]  
For \(\sum\limits_{r,l}t_{m,r}-\hat{t}_{m,r}>0\), the number of individuals or households selected is overall smaller than the target population margins. Multiplying with the factor \(\frac{1}{\sum\limits_{r=1}^{R}M_{r}}\) yields an estimate of the average number of individuals or households missing in each of the margin cells of the synthetic population. On the other hand \(\sum\limits_{r,l}t_{m,r}-\hat{t}_{m,r}<0\) implies that too many individuals or households have been selected. The value \(gap\) is, thus, an estimated difference to the desired total population in terms of individuals \(N_{P}\) or households \(N_{H}\). Considering Equations (2) and (3), it is clear to see that choosing \(r_{re}=1\) would imply that \(n_{in}\) and \(n_{out}\) can overcompensate for this difference calculated in Equation (4) and the solutions would likely begin to jump above and below the desired total \(\sum\limits_{t=1}^{M_{r}}t_{m,r}\). With a value of \(r_{re}=0.5\), half of the estimated difference is addressed when adding and removing individuals from the current selection. Thus, the value of \(r_{re}=0.5\) is recommended to stabilize the solutions.
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 4 Experimental Results
### Ablation Studies
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\lambda\) & **RMSE** & **ERGAS** & **SAM** & **PSNR** & **RASE** \\ \hline
0 & 0.81 & 10.49 & 5.91 & 42.66 & 7.25 \\
3 & 0.70 & 9.05 & 4.86 & 44.02 & 6.25 \\
5 & **0.48** & **6.49** & **3.46** & **47.00** & **4.47** \\
8 & 0.68 & 8.99 & 4.80 & 44.08 & 6.21 \\
10 & 0.58 & 7.73 & 3.96 & 45.45 & 5.34 \\ Expected Value & 0 & 0 & 0 & +\(\infty\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average performance of IFNPMH on 10 test images in the Chikusei dataset under the influence of different parameter \(\lambda\). (Bold represents the optimal value of the indicator). that the fusion effect is superior or equal to other values when \(\lambda\) is set to 5. This suggests that \(\lambda\) = 5 can boost the optimal performance of IFNPMH.
## 2 Materials and Methods
### Apparatus  
The experiment was carried out using a static driving simulator. The simulator includes three primary monitors (Samsung C32 JG50 FQC, 27 inches, 1920 \(\times\) 1080 resolution, Samsung, Seoul, Republic of Korea), active pedals, a force-feedback steering wheel (Logitech G29 dual-motor force feedback steering wheel, Logitech, Lausanne, Switzerland), and an adjustable driver's seat. A 2.1 surround sound audio system provides replications of the engine and driving sounds of the driver's own and surrounding vehicles. The automated driving scenarios and functions were programmed and implemented using SCANeRTM studio (AVSimulation, Boulogne-Billancourt, France). The automation could be toggled via the red autopilot takeover button on the steering wheel. On the participants' right-hand side, a laptop (ThinkPad X1 Carbon 344327C, 14 inches, 1600 \(\times\) 900 resolution, Lenovo, Beijing, China) was mounted to simulate the vehicle center console display for presenting NDRT and SAT information for the participants. The NDRT and SAT information interface were programmed using ErgoLAB 3.0, which could present material in multiple formats and record participant responses. The SAT information interface for the components was drawn by Figma, and Adobe Premiere Pro 2023 was employed for the motion effects.  
For the experiment, the participants were seated in the simulator. A head-mounted eye-tracking system (Tobii Pro Classes 3) was used to record the participants' eye gaze data. Tobii Glass 3 1.19.1 software was used to calibrate the eye-tracker, and Tobii Pro Lab was used to analyze the eye movement data of participants. The driving simulator, center console display, and eye-tracking system are shown in Figure 2.  
Figure 2: Apparatus—driving simulator, center console display, and eye-tracking system.
## 2 Working Principle of Electromagnetic-Ring Balancing Actuator
### Basic Structure of the Balancing Actuator
#### 2.2.3 Principle of Step Rotation  
The action of the counterweight disc is the core of the balancing actuator, which requires not only synchronous rotation with the actuator but also step rotation relative to the tooth rings. To realize these two requirements, the magnetic force between the counterweight disc and the tooth rings should be analyzed.  
As shown in Figure 4, the outer circumference of the counterweight disc is equipped with permanent magnets with equal spacing and the polarity of the adjacent permanent magnets is opposite. The tooth ring is a tooth-shaped structure with rectangular teeth; width W\({}_{1}\) is equal to the space between two adjacent permanent magnets and width W\({}_{2}\) is equal to the length of the permanent magnets. The counterweight block is bolted to the counterweight disc. When the counterweight disc  
Figure 3: Magnetic circuit of the balancing actuator.  
Figure 2: Three-dimensional model of the balancing actuator: **(a)** Global shape; **(b)** Rotatory part; **(c)** Stationary part; **(d)** Exploded view.  
rotates step by step, the center line of the counterweight block is correspondingly changed and the mass distribution is also changed.  
The normal condition of the balancing actuator is the self-lock condition, shown in Figure 5a. The self-lock force is generated by the magnetic force between the permanent magnets and the tooth rings. Under the self-lock force, the counterweight disc can synchronously rotate with the balancing actuator with no mass distribution changes. When the coil is powered on, the tooth rings are quickly magnetized. In case of counter clockwise rotation, the two tooth rings are magnetized to the N and S poles, respectively, shown in Figure 5b. The counterweight disc is driven by the total magnetic force to step rotate relative to the magnetized tooth rings. When the counterweight disc moves to the middle position of each step in Figure 5c, the angular velocity of the counterweight disc reaches the maximum, the coil is then powered off and the magnetic field disappears. Under the self-lock force, the counterweight disc slows down gradually and stops at the end of each step shown in Figure 5d. Through one step rotation, the counterweight plate rotates a fixed angle relative to the actuator. If the counterweight disc is needed to move on, a magnetic field in the opposite direction in Figure 5e is required through the opposite voltage of the coil. Assuming the magnetic field in one direction is continuously supplied, the counterweight disc will do reciprocating motion around one position and the motion should be strictly forbidden.  
Figure 4: Axial view of the two components: (**a**) Tooth ring; (**b**) Counterweight disc.  
Figure 5: Step rotary process of the balancing actuator: (**a**) Self-lock condition; (**b**) Start-up of step rotation; (**c**) Middle of step rotation; (**d**) End of step rotation; (**e**) Start-up of next step.
## 4 Optimization Methods of Base Placement
### Optimization Methods by Fusing OGM and IRM  
Based on the IRM information obtained from the target end effector pose, we could obtain the distribution of grasping ability base placements, where the grasping ability is expressed by the spherical range covered by the end effector at the target point [10].  
\[M=\underset{x,y,\tau}{\operatorname{argmax}}IRM(x,y,\gamma) \tag{2}\]  
We extracted the base placements that could enable the TCP to complete more poses, i.e., the optimal base placements, and compared them to the environmental information in the OGM to check for interference. If interference occurred, we selected the next best  
Figure 8: Mapping results: (**a**) reachability map of the WMRA; (**b**) inverse reachability map of the WMRA.  
base placement for comparison until we obtained a base placement that did not interfere with the environmental information, which became the optimal base placement and was published as the Goal_Point to the controller of the base through ROS. The specific algorithm principles are depicted in the table below, and the actual RVIZ visualization of the OGM-IRM map is presented in Algorithm 1.  
```
Input: Target Pose \(T\in\) SE(3), Inverse Reachability Map, Occupied Grid Map Output: Robot base pose B \(\in\) SE(2)
1:\(P\leftarrow\) IRM.setPose(\(T\));
2:\(O\leftarrow\) OGM.setObstacle;
3: MaxP \(\leftarrow\) arg max(P);
4:while (MaxP! = 0) do
5:if (max(P).isInCollision(O)) then
6: remove MaxP from the list P;
7: MaxP \(\leftarrow\) arg max(P);
8:else
9: Optimal P \(\leftarrow\) MaxP;
10:endif
11:endwhile
12:return Optimal P
```  
**Algorithm 1**Optimization of Base Placement