esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Studies on Sv Wave Transmission through the Horizontal Layered-Structure Rock Slope
### Parametric Study on Dimensionless Fracture Spacing \(\xi\)  
In order to study the effects of dimensionless fracture spacing \(\xi\) on the dynamic response of the horizontal layered-structure rock slope, the normalized fracture stiffness \(K\) is fixed at \(K=0.5\). The other parameters of the slope are the same as described above. The numerical simulation results of different parts of the slope are shown below.  
In Figure 11, the horizontal coordinate is the dimensionless fracture spacing \(\xi\); the vertical coordinate is acceleration amplification coefficients of the slope crest \(\eta_{\rm crest}\). It can be found that the \(\eta\) crest does not monotonously increase with an increase in \(\xi\); it sometimes increases, and sometimes decreases. Figure 12 shows \(\eta\) versus \(H\), where \(\eta\) is the acceleration amplification coefficient of monitoring points on the slope surface. It can be found that \(\eta\) wavily increases with the increase of \(H\), and when \(H\) increases to the slope crest, \(\eta\) reaches the maximum. It also can be seen that the fluctuation enhances with \(\xi\) increasing, which is contrary to Figure 9.  
Figure 11: The FLAC results of \(\eta_{\rm crest}\) (where \(K=0.5\)).  
Figure 10: The distribution regularity of acceleration amplification coefficient of the slope on spatial domain (where \(\xi=0.4\)); (**a**) \(K=0.1\); (**b**) \(K=0.5\); (**c**) \(K=2\); (**d**) \(K=4\).  
Figure 13 shows the distribution regularity of acceleration amplification coefficient of the slope with different \(\xi\) (\(\xi\) = 0.05, 0.1, 0.2, 0.3, and 0.4, respectively). It can be found that the effects of \(\xi\) are obvious to the distribution regularity of acceleration amplification coefficient of the slope. With \(\xi\) increasing, the rhythm of contours \(\eta\) is more and more apparent. The rhythm mainly distributes along the joint, as Figure 13 shows. Before the elevation reaches the slope shoulder, the contours are approximately parallel to the oblique slope surface. When they reach the slope shoulder, contours begin to increase along the surface, which is identical to the conclusion drawn by [PERSON] et al. [23]. In addition, the acceleration amplification regularities of the slope in the vertical direction presented in Figure 13 are the same as the conclusions acquired by [PERSON] and [PERSON] [25], and [PERSON] and [PERSON] [26].  
Figure 12: The acceleration amplification coefficient of monitoring points on the oblique slope surface with different \(\xi\) (\(\xi\) = 0.01 to 0.4) (where \(K\) = 0.5).  
Figure 13: _Cont._
# An Image Processing-Based Method to Analyze Driver Visual Behavior Using Eye-Tracker Data
## 6 Conclusions  
The proposed method hinges on image processing techniques to overcome most limitations due to the 2D images collected by the eye-tracker video camera. This holds with high accuracy for what concerns gazes inside the road, where distortion due to perspective is limited. For gazes outside the road, accuracy can be affected by perspective and the presence of buildings. The farther the facades of the buildings are from the road, the more uncertain the assessment of the coordinates of the gaze looking at them. This uncertainty can be solved only by using the 3D model of the scenery.  
After the gazes are collected, their distribution over the road section and their temporal sequence are analyzed straightforwardly. This allows for an in-depth analysis of the driver's behavior. This will be a future step of the research, which implies a different setup of the simulation experiments from that used in the presented work.  
The current study quantitatively explores how drivers allocate their gaze positions relative to road curvature, significantly influencing safety behavior. While it may seem intuitive that drivers focus more on curves, quantifying this behavior provides valuable insights into attention distribution and informs strategies for designing safer road environments. It was realized by establishing a connection between eye-tracker data and virtual reality simulation, addressing a challenge in such simulator environments.  
It is worth mentioning that the proposed method can also be applied to images collected in a naturalistic survey. However, future research will also consider a larger sample to improve the significance of the findings by considering different classes of drivers by age and different driving scenarios.  
**Author Contributions:** Conceptualization, G.C. and L.M.; methodology, G.C. and L.M.; validation, L.M.; investigation, F.A.; writing--original draft, F.A.; writing--review and editing, G.C. and L.M.; supervision, G.C. and L.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** All subjects gave informed consent for inclusion before participating in the study. This study followed the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Politecnico di Milano (protocol code 35/2020, 2 December 2020).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** This research was supported by the i.Drive Lab ([[http://www.idrive.polimi.it/](http://www.idrive.polimi.it/)]([http://www.idrive.polimi.it/](http://www.idrive.polimi.it/)), accessed on 20 April 2024). The authors thank all the survey participants for investing their time in this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 6 Sensitivity Analyses for the Retrieval of GOME-2A SiF Using PCs  
In this section, we discuss a number of experiments investigating the three most important retrieval sensitivities. Rather than performing closed-loop retrieval simulations, we do retrieval experiments with real GOME-2A spectra. We investigate the effect of the number of PCs, the fit window, and the PC reference area on retrieval and use the four months of January, April, July and October 2013 as test months for reasons of computational time.
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 4 Computer Procedure for Connection between BIM and 3D Concrete Printing  
In this section, a computerized connection procedure between BIM and 3D concrete printing will be carried out, detailing the programs used, how they work, how to make the connection, and how to modify the necessary parameters.  
Figure 3: Exporting of the points cloud to CAD for BIM modeling.  
Figure 2: Connection between the concrete pump and the printing robot.  
In addition, verifications regarding the geometry of the design will be applied, to corroborate that the dimensions of the elements designed in BIM are being respected within the communication program.  
It will be explained how, once the element is printed, its dimensions can be checked with the help of a laser scanner and that information can be projected in a BIM design software.  
Brief Explanation of the Steps to Establish Communication between BIM Models and 3D Concrete Printing  
Figure 4 schematically shows the stages needed to carry out the communication between BIM models and 3D concrete printing. These stages are subdivided into the design of the initial constructive element modeled in RevitTM, the process of exporting the constructive element from RevitTM to DynamoTM software (which is integrated into the RevitTM interface), and the creation of the printing process control code in DynamoTM, for the KUKATM robotic arm.
## 4 Experiments and Results
### Discussion  
Here, we describe an analysis of position control error in open-loop control. Because there is no capture of the spatial position of the milli/microrobot, the results of the open-loop experiments depend on the initial position of the milli/microrobot. If the initial position of the milli/microrobot deviates from the initial position of the preset trajectory, it will cause a large deviation in the trajectory. Due to the randomness of the deviation between the initial position and the preset initial position of the micro-robot in the \"up stair\" and \"down stair\" movements, the error performance in these two cases is random (no correlation).  
Theoretically, the larger the characteristic size (e.g., length, radius) of the magnetic carrier, the easier it is to be manipulated by the system. However, for medical scene applications, the millimeter-level and sub-millimeter-level dimensions are requirements for the design and manufacture of microrobots. Therefore, we use a cylindrical magnetic carrier with a diameter of 1 mm and a height of 1 mm in our experiments. The experimental results show that the system can achieve the manipulation of magnetic carriers of millimetre-level. If the size of the magnetic carrier is further reduced, the error of its posture control will increase.
## 2 Geometric Model, Mesh Division, and Boundary Conditions
### Geometric Model
The Reynolds-averaged Navier-Stokes (RANS) equations are simplified as follows:  
\[\rho\left(\partial\vec{u}/\partial t+\vec{u}\bullet\
abla\vec{u}\right)=- \
abla p^{-}+\mu\
abla^{2}\vec{u}+f-\
abla\bullet\left(\rho\ u^{\prime}\otimes u ^{\prime}\right) \tag{4}\]  
In practical application, the specific expression is shown in Equations (5)-(7):  
\[\frac{\partial(\rho u)}{\partial t}+div(\rho uU)=div(\mu\bullet gradu)-\frac{ \partial p}{\partial x}+S_{u} \tag{5}\]  
Figure 2: Dimensioning of geometric model. \[\frac{\partial(\rho v)}{\partial t}+div(\rho vU)=div(\mu\bullet gradv)-\frac{ \partial p}{\partial y}+S_{v} \tag{6}\]  
\[\frac{\partial(\rho w)}{\partial t}+div(\rho wU)=div(\mu\bullet gradw)-\frac{ \partial p}{\partial z}+S_{w} \tag{7}\]  
Energy conservation equation: The heat transfer between the coolant and the cold plate needs to be simulated, so the energy conservation equation needs to be considered. \[\frac{\partial(\rho T)}{\partial t}+div(\rho UT)=div(\frac{k}{cp}gradT)+ST \tag{8}\]  
where \(T\) is the temperature, \(K\); \(C_{P}\) is the specific heat capacity of the fluid, J/(kg\(\cdot\)K); \(S_{T}\) represents the dissipation term of the fluid. In the above equation, we assume that the fluid is an incompressible fluid, treat it as a continuous medium, and ignore its molecular structure. The fluid does not undergo phase transition in the cold plate and does not cause mass loss in other ways. In the momentum equation, the non-slip wall is used. It is assumed that the fluid velocity at the solid wall is equal to the wall velocity, that is, the velocity is 0, and the fluid molecules are adhered to the wall. In the energy equation, the radiation heat transfer is assumed to be 0, and the influence of radiation heat transfer is ignored.
## 3 The Proposed Satellite Images DSM Generation Method
### Feature Extraction and Matching  
The proposed method adopts Scale Invariant Feature Transform (SIFT) [35] to extract key points from the satellite images. SIFT descriptors have invariant characteristics to both rotation and scale, and they are robust to noise and the change of viewing angle and illumination. Also, SIFT descriptors consist of locations, scales, orientations, and feature vectors, where the feature vector is a 128-dimension vector that records the detailed gradient information of the local region at the key point. Therefore, the number of SIFT descriptors has a great impact on the accuracy and speed of the matching process. The more descriptors are extracted, the more features are matched, but the feature extraction and matching time will increase, as shown in Table 1. In order to meet the accuracy requirements as much as possible, in the proposed method, the threshold value of the key point extraction is set to 30,000.  
After extracting the SIFT descriptors, the Nearest Neighbor Search (NNS) method is used to find the points that best matched the key points. The matching method of SIFT descriptors is usually performed using Euclidean distance. When using the NNS method to match SIFT descriptors, the nearest neighbor point and the second nearest neighbor point of each key point are found by the K-dimension tree algorithm [36]. Then the Euclidean distance between the key point and the nearest neighbor point and the nearest neighbor point are calculated. If the ratio of Euclidean distance between the key point and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of SIFT Descriptors** & **Number of Matched Points** & **Matching Time (s)** \\ \hline
10,000 & 1256 & 2.39 \\
20,000 & 2580 & 3.01 \\
30,000 & 3214 & 3.49 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The matching points and matching time of SIFT descriptors.  
Figure 1: Block diagram of the proposed DSM generation method.  
the nearest neighbor point to Euclidean distance between the key point and the second nearest neighbor point is lower than the threshold (the value of the threshold is generally set to [0.5, 0.7]), the matching is considered to be good.
## 4 Three-Dimensional Magnetic Field Modeling
### Carmel Area (Northern Israel)  
The results obtained at the stage of quantitative interpretation (see Figure 5) were used together with the various geological data to develop the initial Physical-Geological Model (PGM) of the Carmel anomaly [44]. Four profiles were used for 3D combined gravity-magnetic modeling: Profiles 1-3 parallel in the southwest-northeast direction and Profile 4 in the southeast-northwest direction crossing the first three profiles. The results of 3D modeling along with Profile 2 are presented in Figure 9.  
The uplifted crystalline basement produces the greatest gravity-magnetic effect with a magnetization of 3000 mA/m and a density of 2850 kg/m\({}^{3}\). Based on the results obtained from four interpreting profiles, the uplifted crystalline basement relief map is constructed (Figure 10). The vertical magnetization of the uplift of the high-magnetized crystalline basement can explain the coinciding between the maxima of the gravitational and magnetic anomalies.  
Figure 9: Results of 3D combined gravity–magnetic modeling along Profile II in the Carmel area. (1) Physical properties (numerator = density, kg/cm\({}^{3}\), denominator = magnetization, mA/m), (2) direction of magnetization vector, other than the geomagnetic field inclination for the region. “obser”, observed; “comp”, computed.
## 3 Results and Discussion
### Influence of Processing Conditions on Apparent Spray Droplet Sizes  
In order to apply a beam-steering correction by distribution fitting, the influence of the atomization process conditions on apparent droplet sizes must be investigated. Therefore, a special gas capillary with an exchangeable tip was used in the presented study, see Section 2.3. As an example, the measured volumetric density distribution \(\mathrm{q}_{3}\) of apparent droplet sizes are depicted in Figure 5 for measurements with an exit orifice diameter of 1.0 mm (1.5 mm) and a gas pressure of 0.3 MPa (0.9 MPa) on the left (right) side. Additionally, the corresponding log-normal fits (R\({}^{2}\) > 0.99) are shown. The parameters used were chosen as they represent the extrema of the applied process parameters. This means, the smallest exit orifice diameter at the lowest gas pressure (left) resulting in the smallest gas volume flow (0.7 Nm\({}^{3}\cdot\)h\({}^{-1}\)\(\pm\) 0.1) and the largest exit orifice diameter at the highest gas pressure (right) resulting in the largest gas volume flow (8.2 Nm\({}^{3}\cdot\)h\({}^{-1}\)\(\pm\) 1.1).  
The apparent droplet size distributions presented vary over a small range, concerning both form and position. Mean values and the corresponding uncertainty values \(\mathrm{u}_{\mathrm{r}}\)(x) of \(\mathrm{x}_{10,3}\), \(\mathrm{x}_{50,3}\), \(\mathrm{x}_{90,3}\) are shown for measurements under the processing conditions presented in Table 2. The described behavior was found for all measurements (\(\mathrm{d}_{\mathrm{exit\, orifice}}\) = 1.0, 1.3, 1.5 mm; \(\mathrm{p}_{\mathrm{gas}}\) = 0.3, 0.6, 0.9 MPa). Hence, all measured apparent droplet size distributions, caused by the beam-steering effect, could be fitted by a log-normal distribution curve with R\({}^{2}\) higher than 0.99. However, no significant influence of \(\mathrm{p}_{\mathrm{gas}}\) or \(\mathrm{d}_{\mathrm{exit\, orifice}}\) on position and form of apparent droplet size distributions was found in the statistical analysis.  
Although the measured distributions vary only in small ranges, the impact of the beam-steering effect cannot be generalized using a fit function with fixed coefficients. Based on these results, apparent droplet size distributions can be approximated by individual log-normal distribution fits. This might be different when higher gas pressure differences between inlet and atmospheric conditions are present, as gas expansion might have a more pronounced impact. This point should be investigated in further studies. Moreover, the presented study is performed with regard to continuously working atomizers. These atomizers are designed to deliver droplet size distributions of high temporal steadiness. Hence, apparent droplet size distributions also show highly constant values over time. When atomizers are operated in pulsed mode, like in discontinuous combustion applications, the positon and form of apparent droplet sizes may be changing over time. Hence, further studies may addressed the effect of beam steering under discontinuous operation conditions.
# Cryptanalysis of a New Color Image Encryption Using Combination of the 1D Chaotic Map
## 3 Chosen-Plaintext Attack
### The First Phase of Attack
\(C\)' is obtained by rotating \(C\) by the quantity of \(lp\) to the left as the following sequence:  
\[\begin{array}{l}C_{q}\prime=\left\{\text{mod}(k+D(lp+1),256)\oplus C_{q}(lp),\;\text{mod}(k+D(lp+2),256)\oplus C_{q}(lp+1),\;...,\right.\\ \left.\text{mod}(k+D(M*3N-1),256)\oplus C_{q}(M*3N-2),\;\text{mod}(k+D(M*3N),2 56)\oplus\\ \left.\left.\left.\left.\left.\left.\left.\left. where  
\[ \begin{array}{ll}p=&\bmod(k+D(1)\oplus C_{q}(0),256)\oplus\bmod(k+D(M*3N),256) \oplus C_{q}(M*3N-1)\\ &-D(1)\oplus C(0)\oplus D(M*3N)\oplus C(M*3N-1)\end{array} \]  
\(lp\) can be obtained according to the position of \(p\) in \(W\) by the following equation:  
\[lp=M\times 3N-Pos_{p} \tag{17}\]  
where \(Pos_{p}\) is the position of \(p\). Step 4: \(C\) can be obtained by rotating \(C^{\prime}\) to the right \(lp\) times. \(D(1)\) can be obtained by:  
\[C(1)=D(1)\oplus C(0) \tag{18}\]  
The permuted matrix \(P\) corresponding to the original can be obtained by:  
\[P(i)=\bmod(C_{I}(i)\oplus C_{I}(i-1)-D(i),256) \tag{19}\]  
Now we discuss if we can get \(lp\) by the two special plains where one is with all \(k_{1}\) pixel values and the other is with all \(k_{2}\) pixel values according to Equations (12) and (13). \[ \begin{array}{ll}W\prime=\{\bmod(k_{2}-k_{1},256),\bmod(k_{2}-k_{1},256), \ldots,\bmod(k_{2}-k_{1},256),\ p\prime,\bmod(k_{2}-k_{1},\ 256),\ \ldots,\\ \bmod(k_{2}-k_{1},256)\end{array}  \tag{20}\]  
where  
\[ \begin{array}{ll}p\prime=\bmod(k_{2}-k_{1}+D(1),256)\oplus C_{q}(0)\oplus \bmod(k_{2}-k_{1}+D(M*3N),256)\oplus C_{q}(M*3N-1)\\ -D(1)\oplus C(0)\oplus D(M*3N)\oplus C(M*3N-1)\end{array} \]  
We can get \(lp\) from Equation (20) since all the pixel values of \(W\prime\) except \(p\prime\) are the same. The above steps can be expressed as in Algorithm 1. Since the rotation amount \(lp\) and the diffusion matrix \(D\) have been obtained, the permuted image \(P_{I}\) can be acquired by Algorithm 1. ```
1: Set the plain image \(Z\)
2: Obtain the encrypted image pixel sequence \(C^{\prime}\) after the reshape \(\mathrm{Cz}\)
3: Obtain the sequence \(C^{\prime}\)
4:\(G(i)\leftarrow C\prime(i)\oplus C\prime(i)\)
5:\(D\gets G\,except\,D(1)\)
6: Set the plain image \(Z\)
7: Obtain the encrypted image pixel sequence \(C_{q^{\prime}}\) after the reshape \(\mathrm{Cz}\)
8: Obtain the sequence \(C_{q}\prime\prime\)
9:\(G_{q}(i)\gets C_{q^{\prime}}(i)\oplus C_{q}\prime\prime(i)\)
10:\(lp\gets W(i)=\bmod(G_{q}(i)-G(i),256)\)
11:\(C\gets lp\), \(C\)
12:\(D(1)\gets C(1)=D(1)\oplus C(0)\)
```  
**Algorithm 1** Obtain the Rotation Amount \(lp\) and the Diffusion Matrix \(D\)
# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 4 Results and Discussions
### Optimizing the Network
#### 4.6.1 Latency
The figure is intended to visually represent the relationship between network usage latency, trust keywords, and the different configurations in fog computing and cloud computing. By comparing the network usage latency in fog computing and cloud computing, the figure provides insights into the advantages and disadvantages of each computing architecture and how trust keywords affect network performance.
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
###### Abstract  
Cross-technology Communication (CTC) is a novel technique enabling data transmission among devices equipped with different physical layers in a heterogeneous wireless network. Particularly, by enabling cross-technology communication between ZigBee and WiFi, the energy-saving problem and media access control under cross-technology interference would be resolved easily. However, the current CTC designs are either too slow or destructive to ongoing ZigBee transmissions. In this paper, we propose a new Cross-technology Communication framework based on ZigBee chip-level side channel. The proposed design enables the high-throughput ZigBee-to-WiFi cross-technology communication while maintaining transparency to the existing ZigBee network. Specifically, we modify some ZigBee chips within one ZigBee symbol to create a side channel at the CTC sender (ZigBee) for carrying CTC bits while the CTC receiver (WiFi) can detect those modifications and demodulate the CTC bits at WiFi preamble detection. Both theoretical analysis and experiments show that our design would be transparent to ZigBee and throughput can reach 31.25 Kbps--1000\(\times\) the state-of-the-art CTC design.  
cross-technology communication; heterogeneous wireless network; high throughput; ZigBee; WiFi +
Footnote †: journal: Applied  
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