esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation Experiment: Shadow and Caustics
### Preliminary Experiment
#### 4.1.1 Experimental Environment  
In the experiments, the previously described glass sphere and diamond were used as virtual transparent objects. They were rendered using three types of representation: no shadow, with caustics, and with shadow + caustics. Real floor patterns with different complexities--a uniform wood grain pattern and a high-frequency checkered pattern--were prepared. These patterns were given as little reflectivity as possible. The methods for rendering virtual transparent objects and generating the shadow and caustics are described in Section 3.  
The HoloLens was used as the OST-HMD in the experiment. The participants were 23 individuals (21 men, 2 women; max. age: 26, min. age: 20, avg.: 22.13, SD: 1.30), all in their twenties and with prior experience of AR using OST-HMDs. Each participant agreed to the purpose of the experiment and all had a corrected visual acuity of 1.0 or greater with no eye abnormalities. In the preliminary experiment, both the shadow and caustics were defined as \"shadow\" to allow the participants to recognize them as a single optical phenomenon. We showed each participant a picture of a real transparent object and explained to them that both the shadow and caustics were defined as shadow in advance.  
In each experiment, the participant stood approximately 1500 mm away from the visual location of the virtual objects so that the objects appeared in the HMD's FoV. Each participant observed one of the transparent objects for 1 min while switching between three different representations using the keyboard. While observing, they were allowed to move their head freely within the natural range of motion. Following the observation, they were asked to answer the following questions:  
1. Which transparent object was most similar to the real one?
2. Which transparent object had the shadow most similar to the shadow produced by the real one?
3. The shadow of which transparent object had the least amount of light and dark pattern?  
Each participant performed a total of four observations--each followed by answering of questions--for different combinations of transparent object and floor patterns. Each set of observation was followed by a 3 min interval. After the experiment, the participants were asked for comments.
## 5 Results
### Throughput Analysis without Nonlinear Effects
#### 5.2.2 Ray-Tracer Channel  
The second step was to repeat the studies within the ray-tracer radio channel, which outputs a channel characterized by spatial correlations causing interference in M-MIMO networks. The resulting CDFs of the average UE rates for the network-centric approach and the UCCF M-MIMO network architecture, with varying numbers of RUs serving a single UE, are depicted in Figure 6. In this case, a significant gain can be observed when adopting the UCCF network architecture. This is because of interference coordination, where a UE served by multiple RUs benefits from ZF precoding suppressing interference between UEs served by the common RU. In the extreme case of six RUs (all RUs serve all UEs), all interference is suppressed by ZF precoding in each RU. However, power allocation can still negatively impact the rates achieved by some UEs, particularly for five and six RUs serving a single UE. On the other hand, the utilization of many RUs causes an effect expected in the literature for UCCF M-MIMO networks: the distribution of average UE rates becomes close to uniform. This means that the Mobile Network Operator (MNO) can guarantee uniform QoS for almost all users. It remains an open question regarding which approach is better from the perspective of the MNO: Is it better to offer network users almost equal throughput at the cost of significant throughput degradation for the best users, or to improve the throughput of median users and those with the worst radio conditions while maintaining the QoS of the best ones? The answer to this question depends on the specific conditions and goals of the MNO. However, the UCCF M-MIMO network can provide flexibility in its implementation through the proper formulation of serving clusters.  
Figure 5: CDFs depicting the average UE rates for the network-centric and UCCF approaches, with varying numbers of RUs, under the Rayleigh radio channel.  
Figure 6: CDFs depicting the average UE rates for the network-centric and UCCF approaches, with varying numbers of RUs, under the realistic ray-tracer radio channel.
## 3 Results and Discussion
### Numerical Modelling  
#### 3.4.1 Voltage Predictions  
As outcome of the numerical simulations, the predicted voltage response of the cell 1 (or uncycled cell) at room and cycling temperatures are illustrated in Figures 16 and 17 during high constant current discharge (2C i.e., 40 A). From the comparison with the experimental measurements, the absolute value of the voltage deviations are overall less than 2%. Particularly, at the beginning and at the end of the discharge curves, highest voltage deviations can be found due to steeper slopes presented in the voltage behaviour of the battery cell. The good agreement in the simulated and the experimental voltages observed in Figures 16 and 17 translates for the accurate electrochemical characterisation as for the effective implementation of the parameters estimation in the framework of the coupled electrochemical-thermal modelling at both room and cycling temperatures.  
The predicted voltage response of cell 2 (or cycled cell) at room and cycling temperatures are illustrated in Figures 18 and 19 during high constant current discharge (2C i.e., 40 A). From the comparison with the experimental measurements, the absolute value of the voltage deviations are overall less than 2% and 3%, respectively. Particularly, at the beginning and at the end of the discharge curve, highest voltage deviations can be found due to steeper slopes presented in the voltage behaviour of the battery cell. The good agreement in the simulated and the experimental voltages observed in Figures 18 and 19 translates for the accurate electrochemical characterisation as for the effective  
Figure 15: Temperature increase at 35 \({}^{\circ}\)C for fresh and aged cells.  
The slightly longer simulation time observed for the voltage curves in Figure 18, highlight the effect of the temperature on the internal battery cell electrochemical processes and further the non-negligible role of the temperature in the assessment of the battery performances during long-term cycling.
# A Comparative Analysis of 3D Software for Modeling Fatigue Crack Growth: A Review
## 2 Software
### Analytical-Based Approaches  
Analytical-based software employs pre-existing libraries of closed-form stress intensity factor solutions to predict fatigue crack growth, encompassing crack paths and fatigue lives. This approach offers faster results compared to numerical-based methods, as it eliminates the need to develop numerical models. However, it is important to consider certain limitations associated with this approach. One limitation is the applicability of existing libraries, which are primarily designed for planar cracks. These libraries may not accurately account for crack shapes that deviate from planarity, potentially leading to less accurate predictions in such cases. Additionally, analytical-based software typically neglects changes in crack shape during fatigue growth, which may limit its accuracy in situations where crack geometry evolves significantly. Load redistribution, an important phenomenon as a crack propagates, is often not fully considered in analytical-based software. This neglect can result in less precise predictions, particularly in scenarios where load redistribution significantly affects crack growth behavior. Furthermore, nonlinear effects, such as material nonlinearity or large deformations, are typically not accounted for in analytical-based software, potentially limiting its suitability for analyzing cases where these effects play a significant role. Despite these limitations, several analytical-based software packages have been developed and made available for fatigue crack growth analysis. Examples include NASGRO [110], NASCRAC [111], ESCRACK [112], AFGROW [113], and VIDA [114]. These software tools provide valuable capabilities for analyzing fatigue crack growth, offering efficiency and convenience in certain applications where the aforementioned limitations are acceptable or can be mitigated through appropriate assumptions and considerations.
## 3 Proposed Procedure
### Algorithms and Optimization Procedures for Efficient Processing of Vector Data
#### 3.4.3 Interpolation Processing  
The interpolation processing phase focuses on restoring missing data values and maintaining data connectivity. Linear interpolation techniques are employed to fill missing elevation values and color information based on adjacent data averages. Digital Elevation Models (DEMs) and orthoimages are utilized to create seamless connections between terrain and building boundaries.  
\[V(x,y)=Sig(Bi.V\times Bi.W),\ Bn.W=\frac{Bn.D}{Sig(Bn.D)}\]  
Specifically, the Wise Interpolated Texture (WIT) technique was applied to minimize texture discontinuities and enhance the visual quality of interpolated data. This approach reduces color differences and boundary mismatches that may occur when combining multiple textures, optimizing the process to maintain a more uniform and high-resolution texture.
## 2 Encryption Principle
### Principles of Elliptic Curve Cryptography  
As depicted in Figure 3, regarding the elliptic curve encryption process, a finite field \(Fp\) is selected, and the elliptic curve \(Ep(a,b)\) and a base point \(P\) on it, with order being a prime number \(n\), are determined. Ellipse parameters \(a\) and \(b\), \(n\), and \(P\) are public information.  
Step 1: Key generation  
Select the base point \(P\) on the elliptic curve \(Ep(a,b)\), choose a large number \(k\) as the private key, and generate the public key \(Q=kP\).  
Step 2: Encryption  
When \(B\) sends the plaintext to \(A\), the following steps are performed:  
* Pass \(Ep(a,b)\) and points \(Q\) and P to \(A\);
* Encode the plaintext to be transmitted to a point \(M\) on \(Ep(a,b)\);
* Generate a random integer \(r\) (\(r<n\));
* Calculation \(C=\{rP,M+rQ\}\);
* Step 3: Decrypt
* \(M+rQ-k(rP)=M+r(kP)-k(rP)=M\);
* Decode the point \(M\) to obtain the plaintext.  
Figure 3: Elliptic curve encryption and decryption process.
## 3 Results
### Stability of the Candidate Reference Genes
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**ACt**} & \multicolumn{2}{c}{**Bestkeeper**} & \multicolumn{2}{c}{**NormFinder**} & \multicolumn{2}{c}{**geNorm**} \\ \cline{3-10}
**Experimental Conditions** & **CRCs** & **Stability** & **Rank** & **Stability** & **Rank** & **Stability** & **Rank** & **Stability** & **Rank** \\ \hline \multirow{8}{*}{Temperature treatment} & Actin & 0.459 & 5 & 1.173 & 7 & 0.326 & 5 & 0.249 & 1 \\  & TUB & 0.415 & 3 & 0.985 & 3 & 0.183 & 2 & 0.328 & 5 \\  & UBCE & 0.401 & 1 & 1.080 & 4 & 0.160 & 1 & 0.310 & 4 \\  & PMP-2 & 0.402 & 4 & 1.156 & 6 & 0.189 & 3 & 0.289 & 3 \\  & HIS & 0.637 & 6 & 0.631 & 1 & 0.566 & 8 & 0.488 & 8 \\  & EFI\(\gamma\) & 0.439 & 2 & 1.131 & 5 & 0.274 & 4 & 0.249 & 1 \\  & 18S rRNA & 0.560 & 8 & 1.315 & 8 & 0.481 & 6 & 0.368 & 6 \\  & UBQ & 0.587 & 7 & 0.702 & 2 & 0.495 & 7 & 0.438 & 7 \\ \hline \multirow{8}{*}{Developmental stages} & Actin & 0.433 & 3 & 0.363 & 2 & 0.306 & 4 & 0.109 & 1 \\  & TUB & 0.450 & 4 & 0.373 & 3 & 0.338 & 5 & 0.109 & 1 \\  & UBCE & 0.384 & 2 & 0.687 & 5 & 0.088 & 2 & 0.254 & 5 \\  & PMP-2 & 0.484 & 6 & 0.304 & 1 & 0.396 & 6 & 0.124 & 3 \\  & HIS & 0.450 & 5 & 0.813 & 6 & 0.211 & 3 & 0.305 & 6 \\  & EFI\(\gamma\) & 0.380 & 1 & 0.566 & 4 & 0.088 & 1 & 0.204 & 4 \\  & 18S rRNA & 0.851 & 8 & 0.973 & 7 & 0.793 & 8 & 0.514 & 8 \\  & UBQ & 0.670 & 7 & 1.098 & 8 & 0.579 & 7 & 0.402 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stability of eight candidate reference genes expression in _B. xylophilus_ under different experimental conditions. Figure 3: Average expression stability values (MV) of the candidate reference genes calculated by geNorm at (**a**) different temperature treatments and (**b**) different developmental stages.
## 3 Results
Problems with the feeding system: Failures or malfunctions in the material feeding system can cause interruptions in the material flow and increase the CT.
3. Misadjustments in machine parameters: Errors in the configuration of machine parameters, such as injection pressure or cooling time, can generate variations in the CT. In the analysis of the plastic injection molding process, a similar behavior has been observed between injection time in Figure 11, average backpressure, transfer pressure, and maximum injection pressure in Figure 12. This similarity suggests that these variables are closely related and that their behavior is influenced by common factors. Figure 11: Historical data (injection time (blue) and cycle time (orange)). Based on this analysis, cycle time (CT) is proposed as the primary control variable for process optimization. CT represents the total time it takes for the process to complete a full cycle, from the start of injection to the ejection of the part from the mold. An efficient CT is crucial for process productivity and profitability. The choice of CT as the primary control variable is based on the following reasons:  
1. Relationship with productivity: cycle time (CT) is a direct measure of process productivity. A reduction in CT implies a higher number of parts produced per unit of time. 2. Impact on product quality: CT influences the quality of the final product, as it determines the exposure time of the molten material to heat and pressure. An adequate CT ensures the correct solidification of the material and minimizes the appearance of defects. 3. Influence on other variables: CT is related to other important process variables such as injection time, average backpressure, transfer pressure, and maximum injection pressure. By optimizing CT, the behavior of these variables can be indirectly improved.
## 2 Materials and Methods
### Analytical Procedure, Quality Assurance and Quality Control  
The concentration of the dissolved organic carbon (DOC) was obtained using a LiquiTOC II (Elementar, Langenselbold, Germany) and the stipulated SRPS ISO 8245:2007 method. Using the 340i, WTW, SenTix\({}^{\text{\textregistered}}\)21 electrode, according to the method of SRPS H.Zi.111:1987, the pH values of the experimental samples were measured. The residual chloride concentration in the water was determined according to the SRPS ISO 9297/1:2007 method. The sulfate concentration was determined by iodometric titration of excess chromate ion with Na\({}_{2}\)S\({}_{2}\)O\({}_{3}\) solution after proper sample precipitation by the addition of excess barium chromate. Water alkalinity was analyzed in the form of hydrogen carbonate concentration, using the volumetric method (APHA, 1982). Determination of the selected organic pollutants was performed using gas chromatography with a mass detector (GC/MSD) (Agilent 7890A/5975C, Santa Clara, CA, USA) and gas chromatograph with an electron capture detector (GC/\(\mu\)ECD). The detailed analytical, quality assurance and quality control procedures are given in our previous papers and shown in the Supplementary Materials [23; 24; 33; 40].
## 2 System and Channel Models
### One-Hop FSO SublinkSubsystem under Various Weather Conditions
\right., \tag{3}\]  
where \(\theta_{x,y}=2\omega_{x,y}^{L}/L_{x,y}\) is the beam divergence angle, \(A_{x,y}=(\pi D_{x,y}^{2})/4\), \(D_{x,y}=2d_{x,y}\) is the receiver aperture diameter, \(L_{x,y}\) is the beam propagation distance, and \(\omega_{x,y}^{FSO}\) [dB/km] is the beam attenuation coefficient affected by the weather-dependent index of refraction structure parameter \(C_{n}^{2}\) weather, as shown in Table 1[30; 33; 34; 35]. It is worth noting that when considering the pointing errors, the geometric loss of the beam during transmission has also been taken into account. By integrating Equation (1) with \[F_{\gamma_{x,y}^{FSO}}\Big{(}\gamma_{x,y}^{FSO}\Big{)}=\int_{0}^{\gamma_{x,y}^{FSO}} f_{\gamma_{x,y}^{FSO}}(\gamma)d\gamma_{\Gamma}\gamma_{y}\big{(}\gamma^{FSO} \big{)}=\int_{0}^{\gamma_{x,y}^{FSO}}f_{\gamma^{FSO}}(x)dx,\] the cumulative distribution function (CDF) of \(\gamma_{x,y}^{FSO}\) without PE can be expressed as: \[F_{\gamma_{x,y}^{FSO}}\Big{(}\gamma_{x,y}^{FSO}\Big{)}=\Bigg{\{}1-\exp\left[ -\Bigg{(}\frac{1}{\eta_{x,y}}\sqrt{\frac{\gamma_{x,y}^{FSO}}{\gamma_{x,y}^{FSO }}}\Bigg{)}^{\beta_{x,y}}\right]\Bigg{\}}^{\alpha_{x,y}}. \tag{4}\]  
Similarly, according to Equation (2), the cumulative distribution function (CDF) of \(\gamma_{x,y}^{FSO}\) with PE can be obtained as:  
\[F_{\gamma_{x,y}^{FSO}}\Big{(}\gamma_{x,y}^{FSO}\Big{)}=B_{x,y}\Big{(}\gamma_{x,y}^{FSO}\Big{)}\sum_{l=0}^{\infty}C_{x,y}G_{2,3}^{2,1}\bigg{(}D_{x,y}\Big{(} \gamma_{x,y}^{FSO}\Big{)}\bigg{|} \begin{array}{c}E_{x,y},1\\ 0,E_{x,y},E_{x,y}\end{array} \bigg{)}. \tag{5}\]  
\[\text{where }B_{x,y}\Big{(}\gamma_{x,y}^{FSO}\Big{)}=\frac{\alpha_{x,y} \rho_{x,y}^{E}}{\beta_{x,y}\big{(}\eta_{x,y}A_{y,y}\big{)}^{\alpha_{x,y}^{E}} \Big{(}\sqrt{\frac{\gamma_{x,y}^{FSO}}{\gamma_{x,y}^{FSO}}}\Big{)}^{\rho_{x,y} ^{E}}},C_{x,y}=\frac{(-1)^{l}\Gamma\big{(}\alpha_{x,y}\big{)}}{t\Gamma\big{(} \alpha_{x,y}-l\big{)}(1+t)^{1-\frac{\rho_{x,y}^{E}}{\gamma_{x,y}^{E}}}},\,D_{ x,y}\Big{(}\gamma_{x,y}^{FSO}\Big{)}\]  
\[=\frac{1+t}{\big{(}\eta_{x,y}A_{y,y}^{E}\big{)}^{\beta_{x,y}}}\bigg{(}\sqrt{ \frac{\gamma_{x,y}^{FSO}}{\gamma_{x,y}^{FSO}}}\bigg{)}^{\beta_{x,y}},\text{ and }E_{x,y}=1-\frac{\rho_{x,y}^{E}}{\beta_{x,y}}.\]
# Landslide Susceptibility Modeling Using a Deep Random Neural Network
## 4 Results and Discussion
### Landslide Susceptibility Mapping  
On the basis of landslide conditioning factors, landslide models estimate the LSI for each unit of the study area. The LSI values were reclassified into five susceptibility levels, namely, very low, low, moderate, high, and very high, by using the geometrical interval classification method. The resulting landslide susceptibility maps using various models are shown in Figure 7. These models yield a similar landslide susceptibility distribution pattern, that is, highly susceptible areas are usually distributed along the Nujiang River Grand Canyon. The results are highly consistent with the true state of the study area because these landslide-prone areas are easily affected by tectonic movements, water erosion, and road construction. Loose soil and soft rock are further conducive to slope failure under low vegetation coverage and frequent rainfall.  
The detailed statistic results on landslide susceptibility distribution are exhibited in Table 2. The DRNN model predicts that 16.66% of the whole area shows very high landslide susceptibility, whereas 19.06%, 11.08%, 19.05%, and 34.15% of the area have high, moderate, low, and very low susceptibility levels, respectively. CNN predicts that 16.98% of the area has a landslide occurrence probability, whereas 23.46%, 10.36%, 9.04%, and 40.17% of the total area lie in high, moderate, low, and very low susceptibilities, respectively. Regarding the DNN, 66.59% of the landslides are located in a very high class that accounts for 18.22% of the whole study area. As for DRNN, 68.04% of the landslides fall into a very high vulnerability class, while these regions account for 18.57% of the whole region.  
Additionally, the reliability of LSM was quantitatively measured using the landslide density (LD) index, which is a proportion of PL (percentage of landslide pixels) and PC (percentage of pixels in a susceptibility class). Table 2 shows that the LD of various landslide models consistently increases as the susceptibility level improves. The results indicate that these network models achieve reasonable landslide susceptibility assessments. Particularly, the proposed DRNN achieves the highest LD value, suggesting that it may be highly appropriate for predicting landslide susceptibility.
## 5 Secondary lining Support Characteristic Curve
### Calculation Results and Analysis of SCCs
#### 5.2.1 SCCs of Secondary Lining for Different Grades of Surrounding Rock
In the later phase, while the increase in reinforcement ratio can enhance the peak load-bearing capacity of the secondary lining, when the reinforcement ratio increases from 0.2% to 1.0%, the peak stress of the secondary lining for Grade III, IV, and V surrounding rocks increases by only 10.01%, 5.02%, and 10.06%, respectively. 2.2 Change Characteristic of Crack Width in Secondary Lining for Different Grades of Surrounding Rock  
The change characteristics of crack width in the secondary lining for different grades of surrounding rock (Grade III, IV, and V) are shown in Figure 11.  
As the stress increases, the crack widths in the secondary lining for Grade III, IV, and V surrounding rocks show an initial slow linear growth. A rapid linear growth is observed after the crack width reaches about 0.1 mm. Subsequently, a small increase in  
Figure 11: Curves of secondary lining crack width and support stress for different grades of surrounding rock. (**a**) Grade III surrounding rock with 0.8 m thickness; (**b**) Grade IV surrounding rock with 1.2 m thickness; and (**c**) Grade V surrounding rock with 1.35 m thickness. stress can lead to a significant increase in crack width. The reinforcement ratio has little effect on the initial crack expansion, but a higher reinforcement ratio can effectively delay the development of concrete cracks. For example, for the Grade V surrounding rock with a 1.35 m thick lining, the crack widths corresponding to a stress of 3.6 MPa are approximately 0.12 cm, 0.15 cm, 0.20 cm, 0.30 cm, and 0.62 cm for reinforcement ratios of 1.0%, 0.8%, 0.6%, 0.4%, and 0.2%, respectively. The crack widths corresponding to a stress of 4.8 MPa are approximately 0.24 cm, 0.32 cm, 0.45 cm, 0.71 cm, and 1.55 cm. The slope \(k\) is defined as \(k=\) crack width/reinforcement ratio. Under a surrounding rock pressure of 4.8 MPa, as the reinforcement ratio increases from 0.2% to 1.0%, the curve slope decreases from 419 to 35, as shown in Figure 12. After the reinforcement ratio reaches 0.6% or 0.8%, further increasing the reinforcement ratio to limit crack expansion becomes economically unreasonable.
## 2 Model of the System and Problem Setting
The cooperation among vehicles is modelled according to the group communication paradigm [15]. Vehicles may dynamically join and leave the group. A vehicle joins the group upon starting its mission. A node leaves the group, or is forced to, when the node has finished its mission, has been lost, or has been compromised (or believed so). With reference to Figure 2, the group of vehicles is managed by a Group Controller (GC) that is composed of four main components: a Group Membership Service (GMS), a Key Management Service (KMS), an Intrusion Detection System (IDS) and a Secure Dispatching Service (SDS). The  
Figure 2: Group Controller. GMS maintains the membership of the group by keeping track of sensor nodes that join and leave the group. The IDS probes/monitors network activities and vehicles behaviour to detect compromised vehicles [16]. Upon detecting a compromised sensor node, IDS forces the vehicle to leave the group by invoking the GMS leave operation and specifying the vehicle identifier as argument. Whenever a sensor node leaves, or is forced to leave the group, the group-key is renewed in order to guarantee the backward and forward security requirements. KMS is the component that is responsible to perform rekeying. Upon handling a change in the group membership, GMS invokes the KMS rekeying operation. Furthermore, KMS also performs periodic rekeying aimed at reducing the amount of material encrypted with the same key available to an adversary. Finally, the SDS provides a cryptographic suite for message encryption and authentication. In UAN, the GC is implemented by the Base Station (see Figure 3) that has plentiful of resources and will not be compromised. In the rest of the paper we focus on the KMS and SDS components.