esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Method
### Data Processing  
#### Radiation Calibration  
In this paper, pretreatments such as radiation calibration, atmospheric correction, were performed on the L1A-level GF4_PMS multispectral data ([[http://dds.nsoas.org.cn/mainIndex.do](http://dds.nsoas.org.cn/mainIndex.do)]([http://dds.nsoas.org.cn/mainIndex.do](http://dds.nsoas.org.cn/mainIndex.do))) in the Bohai Sea on 2 September 2019 to obtain true reflectance [38].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & \begin{tabular}{c} **Band** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Band Range** \\ **(\(\upmu\)m)** \\ \end{tabular} & \begin{tabular}{c} **Spatial** \\ **Resolution (m)** \\ \end{tabular} & \
\begin{tabular}{c} **Width** \\ **(km)** \\ \end{tabular} &
\begin{tabular}{c} **Revisit** \\ **Time** \\ \end{tabular} \\ \hline \multirow{4}{*}{**Near-Infrared**} & 1 & 0.45–0.90 & & & \\  & 2 & 0.45–0.52 & & & \\ \cline{1-1}  & 3 & 0.52–0.60 & 50 & 400 & 20 s \\ \cline{1-1}  & 4 & 0.63–0.69 & & & \\ \cline{1-1}  & 5 & 0.76–0.90 & & & \\ \cline{1-1} \cline{1-1}
**Middle Infra-red** & 6 & 3.5–4.1 & 400 & & \\ \cline{1-1} \cline{1-1} \cline{1-1} \cline{1-1}  & & & & \\ \cline{1-1} \cline{1-1} \cline{1-1} \cline{1-1}  & & & & \\ \cline{1-1} \cline{1-1} \cline{1-1}  & & & & \\ \cline{1-1} \cline{1-1} \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Band information of panchromatic multispectral sensor (PMS) on the Gaofen-4 (GF-4) satellite.  
Atmospheric correction, FLAASH (fast line-of sight atmospheric analysis of spectral hypercube), was performed based on the results of emissivity calibration of the PMS data [39]. The gain parameters of GF-4, providing the calibration coefficients for 5 types, are shown in Table 2, and the bias is 0. For example, the band status 26466 refers to green, red, near red band integration time, unit is millisecond (ms) [40]. The calculation is expressed as:  
\[\text{Le }=\text{Gain }\star\text{DN }+\text{ Bias}, \tag{1}\]  
where Le is the equivalent radiance at the entrance pupil of the satellite load channel, and gain and bias are the scaling coefficient gain and offset ([[http://www.cresda.com](http://www.cresda.com)]([http://www.cresda.com](http://www.cresda.com))).  
Where PMS is a sensor with full-color band on the GF4 satellite, and P1-P5 refers to bands 1 to bands 5.
## 2 Materials and Methods
### The Ground Segment
#### 2.2.2 Laboratory Activities  
The laboratory activities are aimed at characterizing the radar antennas of the MIPS system. More specifically, they involve the exploitation of an Anechoic Chamber (AC) to carry out the far field [34] measurements described in the following.  
Measurement of the Antenna Patterns  
The availability of the antenna patterns is necessary during the image focusing and the external radiometric calibration procedures described in Section 2.2.4. Specifically, for all radar antennas, we measured the patterns in the horizontal and vertical planes. As example, Figure 4 reports the measured co-polar, one-way amplitude patterns (black lines) of one vertically polarized X-band antenna (top panels), one horizontally polarized L-band antenna (middle panels) and one vertically polarized L-band antenna (bottom panels) of the MIPS system.  
Measurement of the Antenna Phase Center (APC) Position  
The accurate calculation of the position of the radar APC with respect to a local reference system defined over the antenna itself is key information for the SAR focusing procedure described in Section 2.2.4. In particular, we measured the APC position in an AC using the method proposed in [35], which can be effectively applied to a wide class of antennas, including the microstrip antennas of the MIPS system. This overcomes the limitations of other effective methods tailored to specific classes of antennas such as reflector [36,37] or horn [38] antennas. As example, in the panels of Figure 4, we overplot the phase patterns (red lines) measured by scanning the radar antennas around the estimated APC. As can be seen, in all cases, the measured phase patterns are very flat, with variations of less than two degrees within the main beam. This testifies, on one hand, to the good behavior of the radar antenna phase patterns within the main beam and, on the other hand, to the high precision of the APC positions that we estimated. Subsequent refinement of such estimates through external calibration approaches [39] is, therefore, not necessary.  
The antenna measurements were carried out in the AC at the DI of the University \"Parthenope\", Napoli, Italy.  
Figure 3: The IREA-CNR IT platform.
## 1 Introduction
The resulting methods are tested and analyzed by simulating flows over two-dimensional (2D) airfoils, such as symmetric NACA0012 or asymmetric RAE2822, and over three-dimensional (3D) bodies, such as an academic hemispherical headform or an aerodynamic ONERA M6 wing. The numerical results are presented in comparison with available experimental data or other numerical solutions reported in literature. It can be learned that the present method does function for compressible transonic shocked flow simulations, as well as for nearly incompressible flow simulations at low Mach numbers, and the proposed curved boundary treatment does introduce a notable improvement in the accuracy of the numerical approximation. The rest of the paper is arranged as follows: Key aspects of a high-order accurate numerical method, including governing equations, preconditioning, numerical flux modification, DG spatial discretization, shock capturing, and the enforcement of boundary conditions are described in Section 2. In Section 3, a practical treatment of curved boundaries is proposed. In order to have high-order boundary approximation, RBF interpolation is introduced to propagate the boundary motion to the interior of the mesh. Some details of the time-stepping schemes are then described in Section 4, with particular emphasis on the implicit LU-SGS algorithm. In Section 5, a set of 2D- and 3D-test cases are arranged to demonstrate the ability of the resulting methods. Finally, conclusions are drawn together with possible further extensions in Section 6.
## 4 Finite Element Calculation Model
### Initial Boundary Conditions
#### 4.3.2 Boundary Conditions and Initial Conditions of Temperature Field  
(1) Dirichlet boundary condition  
\[T=T_{a_{1}} \tag{31}\]  
where \(a_{1}\) is the abscissa of the inlet boundary and the upper and lower boundaries are set as adiabatic boundaries, which can be expressed as  
\[n_{d}(-k\
abla T)|_{y=b_{1},b_{2}}=0 \tag{32}\]  
where \(b_{1}\) and \(b_{2}\) are the ordinates of boundary, and \(n\) is the inner normal direction vector of the boundary.  
(2) Neumann boundary condition  
\[n_{d}(-k\
abla T)|_{x=a_{2}}=0 \tag{33}\]  
where \(a_{2}\) represents the abscissa of outflow boundary condition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multirow{2}{*}{**Density/(kg\(\cdot\)m\({}^{-3}\))**} & \multirow{2}{*}{\
\begin{tabular}{c} **Thermal Conductivity** \\ **/(kcal\(-\)m\({}^{-1}\)**\(\cdot\)h\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\))** \\ \end{tabular} } & **Latent** & **Specific Heat Capacity** &
\begin{tabular}{c} **Permeability** \\ **Coefficient** \\ **/(m\(\cdot\)d\({}^{-1}\))** \\ \end{tabular} \\ \hline Soil & 2110 & 1.55 & - & 0.301 & - \\ Frozen soil & - & 1.792 & 12.736 & - & \(5.3\times 10^{-4}\) \\ Unfrozen soil & - & 1.252 & 12.736 & - & \(4.6\times 10^{-2}\) \\ Water & 1000 & 0.56 & - & 0.495 & - \\ Ice & 918 & 2.24 & - & 0.998 & - \\ \hline \hline \end{tabular}  
* In this numerical simulation, \(T_{mm^{2}}\)= \(-1.0\)\({}^{\circ}\)C, \(dT\) = \(0.5\)\({}^{\circ}\)C.  
\end{table}
Table 2: Physical parameters of soil.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Names**} & \multicolumn{2}{c}{**Elastic Modulus**} & \multirow{2}{*}{**Poisson’s Ratio**} & \multirow{2}{*}{
\begin{tabular}{c} **Cohesion** \\ **(kPa)** \\ \end{tabular} } & \multirow{2}{*}{**Friction Angle**} \\  & & & & \\ \hline Frozen soil & 44.27 + 7.73 \(|T|\) & 0.25 & - & - \\ Unfrozen soil & 24 & 0.35 & 15 & 20 \\ \hline \hline \end{tabular}  
* \(T\) is soil temperature.  
\end{table}
Table 3: Mechanical parameters of soil.  
(3) Initial condition  
\[T(x,y,t)=T_{0}(x,y,0) \tag{34}\]  
where \(T_{0}\) is the initial temperature of soil layer.
# Internal Curing Effect of Waste Glass Beads on High-Strength Cement Composites
## 3 Results and Discussion
### XRD Analysis  
Figure 5 shows the representative XRD patterns of the HSCC specimens that used WGBs at the ages of 3 and 56 d. Belite was the major hydration product of most samples. In some peaks, calcite, which is part of the cement raw material, and portlandite, which is generated by humidity during curing, were observed. Rutile was observed along with quartz, and it was used as an internal standard sample to ensure the internal standard mixing amount [42]. S-group specimens had similar hydration products regardless of age. The peak increase or decrease was similar because the initial hydration reaction was maximized during steam curing. For S-group specimens, the peak intensity of the specimens with a WGB content of 10% or 20% was observed to increase with age. The peak of portlandite at 18.0\({}^{\circ}\) changed with increasing WGB content and age. This appeared conspicuously in R-group specimens, and the internal moisture is expected to remain during curing. In R-group specimens, some belite was found regardless of age. Belite was measured at 16.2\({}^{\circ}\), and the peak occurred regardless of WGB content. The proportion of belite generated in samples at the age of 3 d tended to increase with WGB content; this can be observed at 32.5\({}^{\circ}\), 51.6\({}^{\circ}\), and 38.0\({}^{\circ}\). The proportion of belite detected in samples at the age of 56 d was the highest in the R10 sample; this can be noticed at 29.5\({}^{\circ}\), 32.0\({}^{\circ}\), 32.3\({}^{\circ}\), 34.8\({}^{\circ}\), and 52.0\({}^{\circ}\). R10 specimen exhibited somewhat higher peak intensities than other samples at the ages of 3 and 56 d, probably because the WGB content of 10% improved the hydrate formation rate in the paste matrix.  
Figure 5: XRD patterns of samples at the ages of 3 and 56 d (A: alite, C: calcite, D: dipotassium sulfate, E: ettrinigite, G: gypsum, P: portlandite, Q: quartz, and R: rutile).  
For the samples analyzed using the internal standard method, the quantification results of calcium hydroxide obtained through the normalization of anhydride are as follows. For SP, S5, S10, and S20, the measurements at the age of 3 d were 0.2%, 0.2%, 0.3%, and 1.5%, respectively; moreover, the measurements at the age of 56 d were 0.1%, 0.3%, 0.2%, and 0.2%, respectively. For RP, R5, R10, and R20, the measurements at the age of 3 d of age were 2.9%, 1.9%, 4.2%, and 4.5%, respectively; moreover, the measurements at the age of 56 d were 1.5%, 1.0%, 1.2%, and 1.2%, respectively. Figure 6 shows the results of quantitative analysis according to the Rietveld results of XRD. The fractions of clinker and hydrate production were obtained at the ages of 3 and 56 d; moreover, SP, S20, RP, and R20 were analyzed. When the mixing amount of WGBs and the amount of water captured were the same, the level of internal constituent materials verifiably differed based on steam curing and room temperature curing.
# An Object Detection and Localization Method Based on Improved YOLOv5 for the Teleoperated Robot
## 2 Method  
### Object Detection  
#### 2.1.1 YOLOv5 Network Structure  
YOLOv5 [26] is a one-stage object detection algorithm with fast detection speed and high detection accuracy. There are four versions of the YOLOv5 algorithm: YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, and the network depth and width increase sequentially. The detection accuracy of the four models usually improves sequentially. However, the model size also increases sequentially, making the model difficult to use on embedded devices with insufficient computing power. In addition, as the network parameters increase sequentially, the speed of model training and detection becomes slower, and the possibility of overfitting increases. Therefore, it is very important to balance accuracy, model size, and detection speed for YOLOv5 in practical applications.  
The input terminal, the Backbone network, the Neck network, and the head output terminal are the four parts of the classic YOLOv5 network structure. The input terminal contains image preprocessing operations such as scaling the input image to the network input size of 640 \(\times\) 640 and normalizing it, etc. The Backbone network is a deep convolutional neural network that aggregates and forms image features at different scales. The Focus, Conv, C3, and SPP modules make up the majority of it. The Neck network is a layer that combines and mixes image features, which can further expand the variety of features. The Conv, upsampling, and C3 modules make up the majority of it. The head output terminal generates object bounding boxes, predicts object classes, and predicts results using image features. The classic YOLOv5 network structure is shown in Figure 1.
# Multi-Information Fusion Indoor Localization Using Smartphones
## 5 Experimental Verification and Analysis
### Discussion and Analysis
#### 5.2.3 Improved Dynamic PDR Results  
To evaluate the generality of the improved PDR method to different devices, users, and environments, we conducted experiments with VivoY85a and Honor60 smartphones and two volunteers in the two trial sites. Figures 11 and 12 show the cumulative density function (CDF) of the localization error with different devices, users, and environments. The experimental results show that the improved PDR method has a lower localization error and better performance than the traditional PDR method. These results primarily occur because step length estimation and heading direction compensation were calculated based on more accurate information, thus reducing the effects of device heterogeneity and achieving comparable localization error with different users (height 155 cm and 180 cm) in different environments.
## 1 Introduction
This feedback loop allows the agent to learn and refine its policy over time, balancing exploration and exploitation to achieve better charging decisions in the future. Experimental results using real-world data demonstrated that the RL-based approach can achieve the design objectives [13]. However, one significant limitation of the approach in [13] is the assumption that all participants behave honestly. In practice, this assumption is unrealistic, as some consumers may attempt to manipulate their SoC values to gain priority access to charging. Such manipulation is known as a False Data Injection (FDI) attack, where malicious consumers underreport their SoC values to increase their chances of receiving energy. This manipulation disrupts fairness, reduces the system's efficiency, and negatively impacts honest consumers by lowering their charging opportunities. This paper investigates the security vulnerabilities of RL-based charging coordination and proposes countermeasures to mitigate the impact of FDI attacks. To the best of our knowledge, this is the first work that addresses the security of RL-based home battery charging coordination. The primary contributions of this work are summarized as follows:* We introduce several FDI attacks designed to stealthily manipulate SoC values. Using real-world data, we evaluate the impact of these attacks on the RL-based charging system, showing that attackers gain unfair advantages by increasing their charging opportunities, while honest consumers experience reduced charging and lower battery levels. As the number of attackers increases, system performance deteriorates further, with reduced fairness, lower consumer satisfaction, increased power drawn from the grid, and decreased overall reward. * To address these threats, we develop a deep learning-based detector for SoC falsification. Our experiments demonstrate that the proposed detector effectively identifies malicious behavior with high accuracy and low false alarm rates, safeguarding the RL-based charging system from FDI attacks and mitigating their negative impact. The rest of this paper is organized as follows. Section 2 reviews prior research on charging coordination and security in smart grids and identifies research gaps. Section 3 presents the FDI attacks and evaluates their impact. Section 4 describes the proposed detection mechanism and its performance. Finally, Section 5 concludes the paper and discusses future research directions.
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 5 IoV System Model Based on AE-PBFT Algorithm
### Identity Authentication  
(1) During the parameter initialization phase, the system generates public-private key pairs in batches using the ECC asymmetric encryption algorithm to facilitate the distribution and management required for subsequent registration steps. A random number \(P_{private}\) is selected as the private key on a non-singular elliptic curve \(E\), and its corresponding public key \(P_{public}\) is calculated accordingly.  
(2) When new vehicles are added to the system, they must initiate a registration identity request to the nearby RSU. The RSU collects and encrypts the vehicle's identity information, then transmits it to the cloud server for processing and backup. Subsequently, the information is submitted to the CA for review and certification. Assuming the vehicle set is \(V=v_{1}\), \(v_{2}\), \(v_{3}\)... and the RSU set is \(R=r_{1}\), \(r_{2}\), \(r_{3}\)..., the CA center reviews the identity request of each vehicle. For vehicles that pass the review, the CA issues an identity certificate in the form of (\(T_{i}\), \(PW_{i}\), \(DID_{i}\)), where \(T_{i}\) is the registration timestamp, \(PW_{i}\) is the password assigned to the vehicle, and \(DID_{i}\) is a unique decentralized identifier. Additionally, the CA maintains a verifiable data registry (VDR) \(W=DID_{1}\), \(DID_{2}\), \(DID_{3}\)..., which records all registered DIDs. The CA only issues certificates to DIDs that are registered in the VDR. If a vehicle's DID is not found in the registry, it indicates that the identity request has failed the review process, and the vehicle cannot be added to the IoV system.  
(3) The RSU receives the vehicle registration message from the CA center and verifies it. At this point, the RSU acts as a node on the blockchain. It employs the blockchain consensus algorithm to determine whether consensus can be reached. If the RSU decides to trust the vehicle, it will grant the vehicle a digital signature; otherwise, it will refuse to record the vehicle information in the blockchain.  
Figure 7: Construction of IoV system.
## 3 Magnetic and RF-EMF Measurement Results
### Measurement Results for Electric Bus While Charging  
While the electric bus was charging, B values were measured at 30 locations outside the bus and 15 locations inside the bus, as depicted in Figure 22. Before charging the bus, ambient B values were measured and recorded as 0.361 \(\upmu\)T. As illustrated in Figure 22a, the B values were higher near the pantograph during the charging process, with lower values recorded at locations farther from the charging point. For the measurements taken off the bus, the highest B value recorded was 2.8452 \(\upmu\)T, while the lowest was 0.4152 \(\upmu\)T. For the measurements taken inside the bus, notably elevated B values were observed in the areas beneath the pantograph. The highest B value recorded inside the bus was 4.5260 \(\upmu\)T, with the lowest being 0.7980 \(\upmu\)T.  
Figure 21: Contributions of bands to total RF-EMF measurements on the E4 route.  
Figure 20: Box plot of the total RF-EMF measurements on the E4 route.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Electric Bus** & **Non-Electric Bus** \\  & **Bavg (\(\upmu\)T)** & **Bavg (\(\upmu\)T)** \\ \hline L1\_m & 0.4086 & 0.4038 \\ L2\_m & 1.8014 & 0.4491 \\ L3\_m & 1.3071 & 0.4218 \\ L1\_n & 0.4174 & 0.4061 \\ L2\_n & 2.4663 & 0.4542 \\ L3\_n & 1.1579 & 0.4263 \\ Overall & 1.2598 & 0.4269 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of B values measured for electric and normal buses for the E4 route.
## 4 Simulation Analysis Results and Discussion
### Stress Distribution Pattern during Braking  
#### 4.2.1 Analysis of Friction Surface between Ventilated Brake Disc and Brake Pad  
From the results of the temperature field analysis, it can be seen that during the braking process, the form and rate of airflow through the outer surface of the brake disc of the high-speed rotating disc are different, which makes the convective heat dissipation of the brake disc surface and the clearance structure and air different, resulting in different rates of temperature change on its surface. Under the effect of thermal expansion and contraction, higher thermal stress will be generated at the connection between the brake disc surface and the gap structure; Figure 14 shows the stress cloud diagram of the surface of the ventilated brake disc at different braking moments. The initial stage is shown in Figure 14a; as the ventilated disc is subjected to the brake pad's friction, the high-stress area is mainly concentrated in the friction contact area between the ventilated disc and the brake pad. At the same time, the other locations have a more uniform stress distribution. However, with the increase in braking time, the friction surface stress shows a circular distribution.  
Figure 15 shows a three-dimensional position-time-stress cloud along the radial direction of the friction surface of the ventilated brake disc. In Figure 15, the \(x\)-axis represents the radial position of the surface of the ventilated brake disc, the \(y\)-axis represents the time by braking, and the \(z\)-axis represents the equivalent force. Compared with Figure 9, it can be seen that the rate of temperature rise in the friction contact area is higher than the rate of temperature rise around it; the air forms turbulence on the surface around it, taking away most of the brake disc friction surface temperature, but the friction contact area continuously generates heat in the brake disc surface to form a temperature difference, resulting in the stress induced in the friction contact area being higher than the stress around it. The highest stress in the friction contact area reaches 439 MPa. Its stress distribution law is consistent with the distribution law of the temperature field.  
Figure 13: Temperature profile of the nodes along with the axial distribution of the gap structure as a function of time.
## 6 Simulation and Results  
The simulation tool of the bandwidth estimation is the Network Simulator version 2 (NS2). Another is Matlab. The simulation scenarios are like a real time wireless sensor network. Firstly, we use an online method based on ACK interval which is used to probe the available bandwidth. Secondly, we utilize the multi-armed bandit algorithm to find the optimal packet size for achieve better energy efficiency and throughput. The algorithms were simulated for capability evaluation in different channel network states.