esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Stability Analysis of the High Fill Slope
### Slope Stability Analysis of Different Slope Rates under the Maximum Daily Rainfall Condition  
The stability of high fill slopes with slope angles of 30, 40, and 50\({}^{\circ}\) was numerically simulated using the method of intensity reduction. This study reveals the evolutionary patterns of high embankment slope stability under different slope angles in saturated conditions. Furthermore, based on the simulation analysis results, the design scheme for the referenced project's high embankment slope was validated for its rationality.  
Figures 21-23 illustrate that the maximum horizontal displacement of the high fill slope occurs near the toe at various slope gradients, with a gradual decrease in value as one moves up the slope. Similarly, the maximum settlement displacement of the high embankment slope is observed at the point of slope transition at the top, with a decreasing value as one moves down, accompanied by a vertical upward bulge on the toe due to rainwater infiltration and soil saturation.  
As the slope angle increases, the safety coefficient gradually decreases, and the stability state of the slope also transitions from stable to marginally stable and ultimately to unstable. At a slope angle of 50\({}^{\circ}\), the safety coefficient is 0.950, indicating an unstable condition. With a 40\({}^{\circ}\) slope angle, the safety coefficient measures at 1.025, placing it in a critical state. A 30\({}^{\circ}\) slope yields a safety coefficient of 1.318, signifying marginal stability with proximity to the threshold value Fst = 1.30 for overall stability under non-maximum daily rainfall conditions.  
Under natural conditions and maximum daily rainfall, the high fill slope with a 30\({}^{\circ}\) gradient meets the requirements for slope stability. However, it should be noted that the stability analysis in this chapter does not account for slope protection and support structures. Considering numerical simulation under rainfall conditions as the most unfavorable factor, the stability state of the high fill slope with a 30\({}^{\circ}\) gradient can be classified as stable, meeting the safety grade requirements for project engineering at level 1.
## 5 Conclusions and Future Outlook  
The aim of this research was to improve the performance of Cloud-CDNs for end-users and cloud users who stream video and download multimedia content over the internet. A modified CSA approach was used to select edge servers, ensuring that users could receive faster service by accessing the nearest neighbor server via the shortest path determined using the MCS algorithm. This approach led to a significant increase in network performance. Several protocols were evaluated in terms of speed, security, and scalability, with FASP found to work the fastest while preserving content quality to meet user requests and provide a good QoE. Another advantage of the proposed system is its ability to handle rising requests without server downtime, as the user's request can select the optimal edge server. The proposed redirection algorithm based on MCSA showed a 25% reduction in the delay time in test results. For future work, the research plan to utilize other heuristic algorithms to optimize edge server selection for various scenarios. Additionally, deep learning techniques may be employed to reduce the delay of transmitted segments and enhance overall Cloud-CDN performance.  
Conceptualization, M.T.; Methodology, M.T. and A.A.; Software, M.T. and A.A.; Validation, M.T. and A.A.; Investigation, M.T.; Data curation, M.T. and A.A.; Writing--original draft, M.T.; Writing--review & editing, A.A.; Visualization, M.T. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Edge** & **CPU Usage** & **Memory** & **Connected** & **Routes** & **Throughput** & **Delay** & **Packet** & **Jitter** \\
**Server** & **CPU Usage** & **Usage** & **Users** & **Number** & **Throughput** & **Delay** & **Packet** & **Jitter** \\ \hline
95 & 76 & 45 & 11 & 2 & 89 & 0.791 & 4.63834 & 1.4766862 \\ \hline
96 & 68 & 51 & 48 & 3 & 90 & 0.000 & 2.35439 & 0.0833337 \\ \hline
97 & 77 & 46 & 71 & 3 & 92 & 2.839 & 6.59114 & 2.2687105 \\ \hline
98 & 89 & 35 & 87 & 1 & 89 & 1.735 & 6.57669 & 3.1555948 \\ \hline
99 & 63 & 49 & 87 & 1 & 95 & 2.106 & 4.25138 & 3.0403892 \\ \hline
100 & 53 & 51 & 37 & 2 & 89 & 4.572 & 8.05649 & 2.2761992 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 4 Summary  
In summary, we have investigated the temperature dependence of the PL intensity and lifetime in \((\mathrm{Er}_{x}\mathrm{Sc}_{1-x})_{2}\mathrm{O}_{3}\) (\(x=1.0\), 0.054, 0.012) epitaxial thin films grown on Si(111) under the excitation conditions that the energy transfer upconversion process from \(C_{2}\) site is minimized; resonant excitation of the third Stark level (\({}^{4}I_{13/2}\): Y\({}_{3}^{\prime}\)) with a low power was employed. The extension of the averaged inter-Er distance by replacing Er with Sc is more effective to suppression of the non-radiative recombination rather than the increase of non-radiative centers via the induced lattice mismatching. In addition, thermal quenching could be greatly suppressed. These observations lead to the conclusion that the extension of inter-Er distance suppresses the energy transfers in inter and intra Er sites and reduces the captured possibility by the quenching centers. The simple model calculation introducing the inter-site ET, the temperature-sensitive and -insensitive non-radiative relaxations explains well the temperature dependence of the relaxation times as well as the time-resolved PL signals. The obtained results contribute to the coherent population manipulation and development of highly efficient optical devices on Er-related materials on Si substrates.  
Y.K., T.T., and S.A. conceived and designed the experiments; Y.K. and T.T. performed the experiments; Y.K., R.K. and S.A. analyzed the data; H.O. supplied the high quality samples; [PERSON] wrote the paper.  
The authors would like to acknowledge [PERSON], [PERSON], and [PERSON] (NTT Basic Research Laboratories) for fruitful discussions and careful reading. This work was supported in part by JSPS KAKENHI (Grants No. JP25247047 and JP16H03821).  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
The simulation results show that our method can eliminate GNSS spoofing and provide relatively reliable and smoothing localization results for mobile terminals.
## 5 Case Study
### Sensitivity Analysis
#### 5.4.3 Sensitivity Analysis under Different Load Rates  
Figure 12 shows the thermal power electricity and total cost for systems respectively equipped with only FS-PSUs, only VS-PSUs, and only T-PSUs under three load rates (0.71, 0.725, and 0.73). It is evident that as the load rate increases, the thermal power electricity trends downward. This is because a higher load rate indicates a smaller peak-to-valley difference in the load curve, resulting in the reduced standard deviation of the net load curve obtained from the upper-level optimization. As analyzed previously in Figures 8 and 9, a lower standard deviation of the net load curve leads to a lower wind power curtailment rate and decreased thermal power electricity. Similar to the findings in Figure 11, the system equipped with only T-PSUs still exhibits superior robustness in response to load rate variations compared to systems with only FS-PSUs. The systems equipped with only VS-PSUs provide better robustness than the systems equipped with only FS-PSUs. For instance, as the load rate changes from 0.73 to 0.71, the total system cost (\(\times 10^{4}\) CNY) rises from 6.47 to 9.68 with only FS-PSUs, from 5.16 to 8.16 with only VS-PSUs, and from 2.05 to 4.31 with only T-PSUs. This also indicates that a higher flexibility in pumped storage regulation can enhance the robustness of the system to different load rates. Furthermore, regardless of load rates, the system with only T-PSUs consistently exhibits the lowest thermal power electricity and total system cost. This verifies that the models of the T-PSUs established in this paper have consistent performance under different load rates.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 3 Experimental Setup
### Testbed Setup  
We prepared the testbed in a VirtualBox [45] virtual machine with Ubuntu 16.04 LTS [46] operating system. First, we downloaded the NS3-DCE [24] using bake. Then, we modified the bake configure file so that NS3-DCE downloads NS-3.33 [47] instead of the latest version of NS-3. Then, we replaced the downloaded NS-3.33 directory's files with the NS-3.33 mmWave [26] module's files. We modified some functions' code in \"model/linux/ipv6-linux.cc\" and \"model/linux/ipv6-linux.h\" files in the NS3-DCE directory so that the NS-3.33 mmWave module worked properly with NS3-DCE. Then, we configured and compiled NS-3.33 mmWave module using waf. Next, for downloading MPTCP Linux Kernel to work in NS3-DCE, we configured bake to download net-next-libos with MPTCP Linux Kernel v0.90 [25], which is the most recent NS3-DCE compatible MPTCP Linux Kernel. We configured the kernel to install the MPTCP path manager, schedulers, and CCAs. Additionally, we recoded the Redundant, ECF, and BLEST schedulers available in the newer version of MPTCP Linux Kernel to work in MPTCP Linux Kernel v0.90. Then, we compiled the kernel and configured it to work with NS3-DCE. Finally, we configured NS3-DCE using waf so that it successfully recognized all the changes in the modules and was ready to conduct the simulation experiments. For interested readers, we have uploaded the testbed files available at [48].
## 3 Methods
### Training Loss
#### 3.3.2 Translation Loss  
(1) Adversarial loss  
The adversarial loss, \(L_{GAN}(TP,D_{Y},X,Y)\) and \(L_{GAN}(TR,D_{X},X,Y)\), is utilized to match the distributions of the transformed image with the target image. Therefore, the overall adversarial loss \(L_{GAN}\) is  
\[\begin{split} L_{GAN}&=L_{GAN}(TP,D_{Y},X,Y)+L_{ GAN}(TR,D_{X},X,Y)\\ &=\mathbb{E}_{y\sim Y}[logD_{Y}(Y)]+\mathbb{E}_{x\sim X}[1-logD_{Y}( TP(X))]\\ &+\mathbb{E}_{x\sim X}[logD_{X}(X)]+\mathbb{E}_{y\sim Y}[1-logD_{ X}(TR(Y))]\end{split} \tag{13}\]  
(2) PatchNCE loss  
Inspired by CUT, we introduce PatchNCE loss to enforce content consistency during bidirectional image translation, which can ensure the maximum preservation of the shape of the input image. PatchNCE loss in the transformation directions of \(X\) to \(Y\) and \(Y\) to \(X\) can be estimated by \[L_{PatchNCE_{X}}(TP,H_{X},H_{Y},X)=\mathbb{E}_{x\sim X}\sum_{l=1}^{L}\sum_{s=1}^{ S_{l}}\ell(\hat{z}_{l}^{s},z_{l}^{s},z_{l}^{S|s}) \tag{14}\]  
\[L_{PatchNCE_{Y}}(TR,H_{X},H_{Y},Y)=\mathbb{E}_{y\sim Y}\sum_{l=1}^{L}\sum_{s=1} ^{S_{l}}\ell(\hat{m}_{l}^{s},m_{l}^{s},m_{l}^{S|s}) \tag{15}\]  
where \(S_{l}\) represents the number of spatial locations in the generator encoder of each layer \(l\), \(z_{l}^{s}\in\mathbb{R}^{C_{l}}\), \(m_{l}^{s}\in\mathbb{R}^{C_{l}}\), \(z_{l}^{S|s}\in\mathbb{R}^{(S_{l}-1)C_{l}}\), \(m_{l}^{S|s}\in\mathbb{R}^{(S_{l}-1)C_{l}}\), and \(C_{l}\) represents the number of channels in each layer \(l\).  
(3) Similarity loss  
To prevent mode collapse during the generation of the transformed images into the translation network, the overall similarity loss \(L_{sim}(TP,TR)\) is used to encourage the translated image's depth features to resemble the original image. The \(L_{sim}(TP,TR)\) is defined as  
\[\begin{split} L_{sim}(TP,TR)&=L_{sim_{X}}(TP,TR)+L _{sim_{Y}}(TP,TR)\\ &=\Big{[}\Big{\|}H_{xr}(H_{X}(G_{enc}^{x\to y}(X)))-H_{xf}(H_{X}(G_ {enc}^{x\to y}(TR(Y))))\Big{\|}_{1}^{sum}\Big{]}+\\ &\Big{[}\Big{\|}H_{yr}(H_{Y}(G_{enc}^{y\to x}(Y)))-H_{yf}(H_{Y}( G_{enc}^{y\to x}(TP(X))))\Big{\|}_{1}^{sum}\Big{]}\end{split} \tag{16}\]  
where \(H_{xr}\), \(H_{xf}\), \(H_{yr}\), and \(H_{yf}\) are four light networks, which are used to project the embedded features to 64-dim vectors. Each light network is composed of a convolutional layer, which is subsequently followed by a ReLU activation function, average pooling, a linear transformation, another ReLU activation function, and, ultimately, a linear transformation.
# The Influence of Chestnut Flour on the Quality of Gluten-Free Bread
## 3 Results and Discussion
### Physical Properties
The highest specific mass (Table 1) was found in the control sample (0.267 g/cm\({}^{3}\)), the second in terms of this parameter was the bread with the 20% addition of chestnut flour (0.252 g/cm\({}^{3}\)). The values of the specific weight of the breads with the 5% (0.197 g/cm\({}^{3}\)) and 15% additions of chestnut flour (0.198 g/cm\({}^{3}\)) did not differ statistically significantly. The performed statistical analysis showed a significant influence of the recipe composition on the specific weight of the tested bread. The largest statistically significant difference occurred between the control sample and the bread with a 10% addition of chestnut flour, which was characterized by the lowest value of the specific weight (0.174 g/cm\({}^{3}\)). The porosity is the ratio of the volume occupied by the pores to the total volume of the bread. High-quality bread is distinguished by a crumb with thin-walled and evenly spaced pores. For wheat bread, the porosity should be from 73 to 83%, and for rye bread, from 55 to 70% [41]. The lowest crumb porosity was characteristic for bread with the 20% addition of chestnut flour (64.8%), which was also characterized by the lowest volume. No statistically significant differences were found between the control sample (73.5%) and the bread with the 10% addition of chestnut flour (74.1%). These breads were characterized by the highest porosity among all the tested variants. Taking into account the obtained results and comparing them with the requirements concerning the porosity of the crumb of wheat bread, it was found that, apart from the control sample, the bread with the 10% addition of chestnut flour met the requirements specified in the standards for traditional bread. Similar results were obtained by [PERSON] and [PERSON] [42], who investigated the effect of the addition of amaranth flour on the physicochemical and sensory properties of gluten-free bread. Amaranth flour was added (partially replacing the corn flour) in the amounts of 2.5, 5, 7.5, 10 and 12.5% of the total weight of the concentrate. The authors observed that the breads with the addition of amaranth flour in the amount of 5, 7.5 and 10% showed the highest porosity among all those baked in the series. Amaranth flour, added to the recipe in the amount of 12.5%, reduced the porosity of the gluten-free bread.
## 3 Construction of Knowledge Graph for Spectrum Sensing Data from LEO Satellite
### Knowledge Extraction  
The fundamental unit of the spectrum knowledge graph is called triple [14]. There are two forms of the triples: (Entity, Relationship, Entity) and (Entity, Attribute, Attribute-value). Triples are formed by extracting from knowledge within the LEO satellite spectrum sensing scenario. By observing the data obtained, most of knowledge in the fields of LEO spectrum sensing are semi-structured or unstructured data. Among them, spectrum data and ephemeris data belong to semi-structured data, while satellite platform and payload information belong to unstructured data. Different methods are considered to extract knowledge according to different data structural types so as to generate knowledge entities, relationships and attributes. For semi-structured data, algorithms such as energy detection and higher order statistics are utilized to extract the entities and relationships. For unstructured knowledge, the deep learning method of BiLSTM-CRF [15] is applied to obtain important information. The preliminary extracted information is stored in CSV format. The knowledge extraction process extracts entities, relationships, and attributes from raw data, and unstructured or semi-structured data are converted into structured knowledge. By identifying entities, relationships and attributes, the base knowledge structure is established.
# CAD-Based Feature Recognition for Process Monitoring Planning in Assembly
## 2 Literature Review
### CAD Feature Recognition  
Different feature recognition approaches have been developed in the past few decades to support engineers during the design and manufacturing planning phases [28; 29]. In particular, in CAD and CAM tools, these approaches are used to improve the identification of features for manufacturing processes (e.g., inspection criteria for milling processes), as can be seen in Table 1. Most of the approaches rely on an analysis of the CAD model of the product as a bounding representation (B-Rep). In B-Rep models, volumes are described internally by the surfaces bounding them and, thus, by the boundary edges. In addition to the standard analytical curves and surfaces (cylinders, planes, circles, ellipses), free-form surfaces and curves are also used. The direction of the normal vector of the surface pointing toward the material is used to uniquely define on which side of the surface the enclosed volume is located. Based on the representation of individual parts, different approaches have been developed to identify form and geometrical features in CAD models [28; 29]. Table 1 below shows some approaches for feature recognition. Depending on the objective and focus, the approaches vary for specific use cases (e.g., critical milling features). The column \"focus\" shows the main characteristics of each approach and describes the best fit of each approach. As can be derived from the table, a hint-based approach has been proven to be especially applicable for machining features from 2D orthographic projections. Up to a certain point, all approaches presented in Table 1 rely on a rule-based approach. Nevertheless, the rule-based approach is often described in isolation, as the focus is on logical rules rather than volumetric decomposition or hint-based methods.  
CAD feature recognition relies on the extraction of data. The data extraction can also be seen as a pre-process for feature recognition. Hereby, external and internal approaches for the extraction of features can be distinguished [24; 25]. Internal approaches are included in commercial CAD systems and can be addressed via application programming interfaces (APIs). These approaches extract geometric data and recognize features directly from the corresponding CAD systems. External approaches are independent of CAD systems and use neutral data formats, such as STEP and XML files. Currently, almost every CAD system supports neutral data formats, including the import and export of neutral format files. This promotes the development of a computer-aided approach for the automated identification of features. An external approach is very appealing, especially in the area of assembly features relevant for process planning, where CAD files are often transferred between different commercial tools.
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 3 Results
### Transmission Upgrade Analysis  
The information rate of Information B represents the system transmission capacity enhancement capability.  
To ensure the BER performance of Information B, one Information B code element cycle, \(\mathrm{T_{B}}\), corresponds to j frequency-hopping cycles, \(\mathrm{T_{H}}\), in which \(\mathrm{j\geq 1}\). The code elements of each Information B are orthogonal within the code element cycle. The M frequency-hopping points are divided into \(2^{\mathrm{k}}\) groups, with each group including j frequency points. Each Information B code element carries k bits of information.  
Together, the number of code elements, the code element rate, and the coding efficiency of Information B determine the information rate of Information B.  
\[\mathrm{r_{B}=\frac{k\mu_{B}}{jT_{H}}=\frac{kh\mu_{B}}{j}=\frac{h\mu_{B}\log(M/ j)}{j}} \tag{21}\]  
where h is the hopping speed, which is the reciprocal of the hopping period, \(\mathrm{T_{H}}\); \(\mu_{B}\) is the coding efficiency of Information B.  
Equation (21) shows that the larger the number of frequency-hopping points M, the higher the frequency-hopping rate and the higher the information enhancement capacity.  
If \(\mathrm{M=1024}\), the rate of frequency hopping is \(\mathrm{r_{H}=10k}\) and the information rate of Information A is \(\mathrm{r_{A}=5\ kbps}\). To enable Information B to obtain sufficient BER to support Information A for carrier reconstruction, every four frequency-hopping cycles correspond to one code word cycle of Information B, in which \(\mathrm{j=4}\). Then, the equivalent Information B code element rate is 2.5 kbps; each code element carries four bits of information (1/2 channel coding). At this stage, the information rate of Information B is \(\mathrm{r_{B}=10\ kbps}\).
# Simulation of Aerosol Evolution within Background Pollution for Nucleated Vehicle Exhaust via TEMOM
## 2 Governing Equation
### PBE Solution  
Several studies has shown how the mechanism of PM evolution is established. It is only since [PERSON]'s work [19] that the study of coagulation kinetics has gained momentum. The work describes monodisperse systems of spherical particles. Systems are complex in most cases, in which particles are not the same size. [PERSON] [20] expanded the [PERSON] theory to polydisperse systems.  
Population balance equation (PBE), as an extension of the Smoluchowski equation, is used to describe the time evolution of particle size distributions. To date, several numerical solutions for the PBE had been developed, including moment method (MM) [21], sectional method (SM) [22], and the Monte Carlo method (MC) [23]. The above three methods can be divided into sub-methods. We used TEMOM (Taylor-series Expansion Method Of Moments) [24] in this study, a type of method of moments. The present study concerns the new particle formation by binary homogeneous nucleation in a given aerosol situation and growth by coagulation and condensation. Thus, the three source terms are described in PBE as follows:  
\[\begin{split}\frac{\partial n(v,t)}{\partial t}=&\left\{ \frac{1}{2}\int\limits_{v_{\min}}^{v_{\max}}\beta(v-u,u,t)n(v-u,t)n(u,t)\mathrm{ d}u-n(v,t)\int\limits_{v_{\min}}^{v_{\max}}\beta(v,u,t)n(u,t)\mathrm{d}u \right\}_{\text{coagulation}}\\ &-\left\{\frac{\partial(I(v,t)n(v,t))}{\partial v}\right\}_{ \text{condensation}}+\left\{J(v,t)\delta(v_{\text{mult}},v)\right\}_{\text{ nucleation}}\end{split} \tag{8}\]  
where \(n(v,t)\) is number density function; \(n(v,t)\mathrm{d}v\) is the number density of particles size between \(v\) and \(v+\mathrm{d}v\) at the time \(t\); \(\beta(v,u,t)\) is the collision kernel for particles of volume \(v\) and \(u\) at the time \(t\); \(I(v,t)\) is the condensation kernel for particles of volume \(v\) at the time \(t\); and \(J(v,t)\) is the nucleation kernel for particles of volume \(v\) at the time \(t\).