esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Multi-Level EV Charging Station Infrastructure Model
### Analytical Model  
The conceptual framework of EVs queuing model at multi-level EV charging station infrastructure is shown in Figure 9. The state transition diagram of EVs at the charging station is shown in Figure 10. The stochastic model is based on the \"\(M/M/S\)/\(K\)\" queuing model, where the first \(M\) indicates the Poisson distribution for EV arrivals, the second \(M\) indicates the Exponential distribution for service time at the charging station, \(S\) represents a maximum number of charging sockets at charging stations, and \(K\) represents the length of the queue at charging station. In this queuing model, all EVs charging stations have plug-in sockets with identical characteristics, queue access or charging socket service is based on first come first service (FCFS), and the EVs service time depends upon the number of EVs waiting and the amount of time the station takes to charge the vehicle.  
Figure 8: Stress Analysis of the multilevel charging station. (**a**) Von-Mises Stress Distribution of the designed structure. (**b**) Maximum Displacement of the designed structure.  
In the transition diagram as shown in Figure 10, there are two major functions that will be carried out  
1. Arrival rate (\(\lambda\)): \(\lambda_{i}=\lambda\forall ieK\).
2. Service rate (\(\mu\)): \(\mu_{i}=\begin{cases}i\mu&0\leq i\leq S\\ S\mu&S<i\leq K\end{cases}\)  
The steady-state probabilities of EVs at multi-level charging stations are denoted as \(P_{n}\) and are given as follows:  
\[P_{n}=\begin{cases}\frac{(\rho)^{n}}{n!}P_{0},0\leq n<s\\ \frac{(\rho)^{n}}{S^{(n-s)n!}},s\leq n\leq K\end{cases} \tag{1}\]  
where \(\rho=\lambda/\mu\) is the utilization of charging points at the charging station. The basic condition in the [PERSON]'s theorem and Markov chain is the sum of all the probabilities in the system is equal to one, that is, \(\sum_{n=0}^{K}P_{n}=1\). Using the above condition we can get \(P_{0}\) as follows:  
\[P_{0}=\left[\sum_{n=0}^{s-1}\frac{(\rho)^{n}}{n!}+\sum_{n=s}^{K}\frac{(\rho)^{ n}}{S^{(n-s)}S!}\right]^{-1} \tag{2}\]  
The average number of EVs in the system is given as  
\[EV^{avg}=\frac{P_{0}}{S!}\left(\sum_{n=0}^{s-1}n\rho^{n}+S^{s}\sum_{n=s}^{K}n \frac{\rho^{n}}{s^{n}}\right) \tag{3}\]  
The probability of the charging station is full, that is, the probability of blocking the EVs to get charged at the charging station which is an equal probability that the charging station is in the state \(K\), and is given as  
\[P_{B}=P_{m}=\frac{\rho^{K}}{K!}\cdot P_{0} \tag{4}\]  
Figure 10: Stochastic EVs queuing model at Multi-level Charging Station Infrastructure: Discrete Time Markov Chain \(M/M/S/K\) Model.  
Figure 9: Conceptual Framework of EVs queuing model at Multi-level EV Charging Station Infrastructure.  
\[P_{B}=\frac{\rho^{K}}{K!}\left[\sum_{n=0}^{s-1}\frac{(\rho)^{n}}{n!}\right]^{-1} \tag{5}\]  
\[P_{B}=\frac{\rho^{K}/K!}{\sum_{n=0}^{K}\frac{(\rho)^{n}}{n!}} \tag{6}\]  
The above Equation is called as Erlang-B formula used for blocking the EVs from charging at the multi-level charging station.
## 4 Case Study Results
### The Origin and Evolution of the AeroLogic Joint Venture
Today, AeroLogic operates a fleet of ten Boeing B777-200 LRF freighter aircraft over route network compromising 20 destinations in Europe, Asia, the Middle East, and North America [(115)]. The following sections examine the development of the AeroLogic joint venture and its progress to date.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 3 Feature-Based Modulation Recognition Methods
### Summary of Section 3  
Feature-based (FB) modulation recognition methods construct classification criteria by extracting multi-dimensional features from signals, effectively balancing the reduction in computational complexity with noise robustness. These methods are widely utilized in VLC and UOWC scenarios. The core concept involves mining the distinguishing features of modulated signals from various domains--such as the time domain, frequency domain, statistical properties, and geometric distribution--and integrating them with classifiers to achieve efficient recognition. This chapter focuses on six types of feature-extraction techniques (as presented in Table 1), each characterized by distinct application scenarios and performance profiles, necessitating trade-offs based on channel conditions and system requirements. However, these methods also have limitations; for instance, their feature distinguishability often relies on a high SNR and is sensitive to channel conditions. Moreover, manual feature extraction depends heavily on prior knowledge, making it challenging to adapt to new modulation formats or non-stationary channels. This increases potential maintenance costs for future large-scale hardware deployments.  
Figure 6: Accuracy of different recognition algorithms for OFDM-IM using (**a**) BPSK, (**b**) 4 QAM, and (**c**) 8 QAM in reference [24].  
Future work should continue to explore multi-domain feature fusion and adaptive extraction, investigate algorithms such as self-attention mechanisms to dynamically allocate feature weights and reduce reliance on manual design, retain the interpretability of traditional features while leveraging collaborative optimization between deep learning and feature engineering, and focus on feature enhancement under complex channel conditions, integrating channel estimation to jointly optimize classification performance.
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Encoding Techniques** & **Features** & **Applications** & **Achievements** & **Limitations** \\ \hline Chirp Spread Spectrum (CSS) [7] & IoT communication & Customized application of MDMA for the requirements of battery power & Enables IoT communication on a large scale & Uses the radiofrequency range-300 MHz to 3 GHz with this more harmful to the human body and causes cancer \\ Unipolar [12] & Digital to a digital encoding scheme & Digital short-range systems & It requires low bandwidth & Synchronization is required and DC components exist \\ Non-return-to-zero level (NEX-2) [13] & Digital to a digital encoding scheme & Wired Lan & Uses low bandwidth & Lack of synchronization for the large \\ Non-return-to-zero inverted (NEX-2) [13] & Digital to a digital encoding scheme & Wired Lan & Uses low bandwidth & A systematic problem with polar is that the polarity can be backward. \\ Non-Return to Zero (NEX) [14] & Digital to a digital encoding scheme & Wired Lan-B5232-based protocols & It is simpler and requires low bandwidth & No decay is present. \\ Non-Return to Zero (NEX) [16] & Digital to digital encoding scheme & Wired Lan & It is simple. & Loss of synchronization is likely to occur \\  & & & & (especially for large stages of 1 s and 0 s). \\ Return to Zero (NEX) [16] & Digital to digital encoding scheme & Wired Lan & It is simple. & No decay presents. \\  & & & No low-frequency components are present. & No error correction. \\  & & & & (Couples twice the bandwidth of NEX) \\ Manchester [18] & Digital to a digital encoding scheme & Used in 802.5 token ring with & No DC component & Modulation rate for Manchester is twice \\  & & & & (the data rate \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 1: Related work summary against the benchmark techniques.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## Appendix C Complexity Analysis  
We analyzed the time complexity of the main components of the proposed ELP-Net model. The summary of the complexity results is presented in Table 11. In the transformer module, the time complexity of the multi-head self-attention (MSA) is \(O(H(4N^{2}d_{hid}+2 Nd_{hid}d_{x}))\), and the position-wise feed-forward neural network (FFN) has a time complexity of \(O(2 Nd_{hid}d_{x})\), where \(H\) is the number of attention heads, \(N\) represents the number of pieces on one side of the wargame, \(d_{x}\) represents the dimension of the input node features, and \(d_{hid}\) is the hidden dimension of node features. In the GNN module, the time complexity of the graph learning layer becomes \(O(T(2N_{hid}d_{s}+4N^{2}s_{hid}))\), where \(T\) is the length of the input sequence, \(d_{s}\) represents the dimension of the input node features, and \(s_{hid}\) represents the hidden dimension of node features. The quadratic term arises due to the pairwise calculation of hidden feature vectors between nodes. The graph convolution module has a time complexity of \(O(L(N_{e}d_{s}+2 Nd_{s}d_{out}+4N^{2}d_{s_{1}}))\), where \(L\) is the propagation depth, \(d_{out}\) is the output dimension of the node states, \(d_{s_{1}}\) is the hidden layer dimension of the self-attention, and \(N_{e}\) is the in-degree of the node. During the information propagation step, each node receives information from its neighbors, and the total number of connections equals the sum of in-degrees across all nodes. The time complexity of the temporal convolution module is \(O(2 NTc_{i}c_{o}/a)\), where \(c_{i}\) is the number of input channels, \(c_{o}\) is the number of output channels, and \(a\) is the dilation factor.
# Study on Strength of Slip Soil-Tuff Contact Surfaces in Tuff Landslide Based on Model Test
## 4 Conclusions  
In the exploration tunnels for the Xiageliao landslide, scratches caused by sliding were found both within the slip soil and on its contact with bedrock. This means that landslide failure is not always along a single surface nor present in a single geotechnical layer. Tests were performed on undisturbed slip soil samples collected from the exploration tunnel. The following conclusions have been drawn regarding the shear strength and mode of failure of the contact surfaces of slip soil and tuff:  
1. At low vertical stress, the stress-displacement curves show a folded line shape at all roughness levels. With the increase in vertical stress, the stress-displacement curves of all roughness levels are gradually transformed into a hyperbolic shape.
2. The roughness of the contact surface of the slip soil and tuff determines the slip soil's failure mode and shear strength. In nature, shear failure of low roughness contact surfaces is a sliding failure in the partial contact region. Shear failure of a contact surface with high roughness is an elastic-plastic failure within the slip soil. When the roughness is low, the shear strength of the contact surface is less than the shear strength of the slip soil itself; when the roughness is high, the shear strength of the contact surface is higher than the shear strength of the slip soil itself.
3. Based on [PERSON]'s law, a formula for calculating the shear strength of the contact surface of the slip soil as a function of roughness was found. The formula has a correlation coefficient of R\({}^{2}\) = 0.982, which is compatible with the test data and can be used as a reference for evaluating the shear strength of the interface between slip soil and rigid objects.  
Figure 11: Measured values and fitted curves.  
These conclusions can provide some references in geotechnical engineering practice. When evaluating the stability of slopes, the mechanical parameters of the contact surfaces of the rock and soil layers can be taken into account, thus making the calculated parameters more realistic. Similarly, these results can better explain the problem of inconsistency in the direction of sliding and the amount of displacement in different parts of large landslides.  
Conceptualization, G.X. and M.X.; writing original draft preparation, M.X. and J.Z.; writing review and editing, G.X. and J.Z.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Zhejiang Zhezhong Geological Engineering Survey Institute Co., Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focused on Placement Related Problems in WSNs
#### 3.4.1 Node Density  
Finding optimal sensor node density in the field of observation has significant influence on the quality of observation and the cost of the network. Various multi-objective optimization algorithms have been proposed in the literature to find the optimal node density along with considering other objectives that may or may not conflict with it. For example, in [115], the authors have proposed a multi-objective optimization algorithm to find the optimal quantity and location of sensor nodes for stay cable damage identification of cable-stayed bridge under uncertainty. A multi-objective heuristic localization technique for wireless sensor network has been proposed in [116] which is based on harmony search algorithm. The proposed approach is focused on minimization of squared error between the estimated and measured spacing between the nodes and the number of connectivity neighborhood constraints violated by the candidate topology. In [117], a multi-objective optimization formulation has been used to maximize the observations of the smart grid system while keeping the number of phasor measurement units as minimum as possible. The authors also considered contingency constraints and optimal allocation of these sensor devices on utility systems. Simultaneous minimization of the number of nodes and the energy consumption for wireless sensor network has been addressed in [118] by using a multi-objective optimization based on hybrid evolutionary algorithm. Optimal sensor network placement for the observation of water distribution system has been addressed by [119], where authors have proposed a multi-objective optimization algorithm to minimize the number of sensors and their optimal placement to ensure a prescribed reliability level for the network.
## 3 Results and Discussion  
### The Necessity of the Multiplex PCR Technique for the Environmental LMO Monitoring Project  
The process of the natural environmental LMO monitoring and post-management project developed by the MOE and NIE was conducted in three steps: (i) environmental LMO monitoring, (ii) LMO detection analysis, and (iii) post-management (Figure 1). LMO environmental monitoring sites were determined according to the information available for the import ports, roadsides, feed factories, and stockbreeding farmhouses. The LMO monitoring was conducted in the surrounding natural environments of the sites that were identified as having a potential risk for LMO release. Tissue samples were collected from suspicious LMOs and they were identified using PCR. Every year, the NIE develops detection methods for single events, to help detect the LMOs. However, these single detection methods require many PCR reactions. To efficiently analyze the many suspicious canola samples collected in four years, a new LMO identification process using the developed multiplex PCR method, which is more efficient than the conventional PCR methods, is necessary. It takes seven reactions to analyze seven LM canola events using the single PCR method, but would take only one reaction using a multiplex PCR system (Figure 1B). The development of multiplex PCR methods is thus necessary to overcome the disadvantages of the current methods, such as high cost and time requirements.
## 6 Conclusions  
In this paper, a numerical model was established according to actual tunneling conditions to investigate the effects of jack thrust, thrust direction change, nonuniform distribution of cylinder pressure and segment misalignment on segment fracture width when the tunnel segment is designed with an outer diameter of 6.2 m and an inner diameter  
Figure 22: Numerical simulation figure of fracture width under 2.75-3.75 mm misalignment of segment.  
of 5.5 m and the shield machine uses 16 grippers with an area of 0.1 m\({}^{2}\) for propulsion. The following conclusions are drawn, and measures to prevent the segment fracture width from exceeding the allowable value during shield tunneling are proposed:  
1. The empirical formula of segment fracturing in the Japanese Code for the Design of Segments for Shield Tunnels is optimized. The empirical formula does not provide a method to solve the increase in stress of the steel bar; The maximum stress at the fracture of the segment is used instead.
2. When the attitude of the shield and segment are exactly the same, the total thrust of the shield is recommended not to exceed 21,000 kN, and is strictly limited to 24,000 kN.
3. When the inclination angle between the shield machine and the segment is less than 1\({}^{\circ}\), the influence on the quality of the segment is small. When this inclination angle reaches 2\({}^{\circ}\), the total thrust of the shield is recommended not to exceed 16,000 kN, and is strictly limited to 18,000 kN; when the inclination reaches 3\({}^{\circ}\), a fracture is easily produced.
4. When the total thrust is 16,000-19,000 kN, it is recommended that the loading increase or decrease in the left and right four grippers should not exceed 20-30%, and are prohibited to exceed 30-50%.
5. The fracture width increases exponentially with increasing dislocation between adjacent segment rings. The maximum fracture width usually arises in the 9 o'clock part of the segment.  
Conceptualization, T.F. and X.Z.; methodology, C.Z.; software, B.Z.; validation, C.Z., B.Z. and G.R.; formal analysis, C.Z.; investigation, S.H.; resources, C.Z.; data curation, B.Z.; writing-original drift preparation, C.Z.; writing-review and editing, B.Z.; visualization, G.R.; supervision, X.Z.; project administration, T.F.; funding acquisition, T.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No.52378336).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
This work was sponsored by the National Natural Science Foundation of China (No. 52378336). The authors are grateful to these institutions for their support.  
Author [PERSON] was employed by the China Communications Construction Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Proposed System
### Loop Closure Detection
#### 3.3.1 Searching for Matching Candidates  
When an image, \(I_{t}\), captured at time index \(t\), is acquired, a list of images that have common words with the query image \(I_{t}\) can be generated using the inverse index. For each image in the list, it is necessary to compare its region features with those of the query  
Figure 2: An example of clustering image blocks. Starting from a core object to look for its reachable objects. For each reachable object, add its reachable objects until no new objects can be added.  
image and remove the mismatched region feature pairs. When two images do not have a significant viewpoint change, their matching image regions generally have the same relative position in the image, and two images usually have only a few feature matching pairs (rather than tens or hundreds in the feature point method). Therefore, by exhaustively computing the average center of mass of these image regions, we can calculate the average relative positions of all matching pairs and remove any outliers.  
Finally, we exclude images that do not reach the required number of matching features, resulting in a final list of matching images of image \(I_{t}\), denoted as \(I_{t_{1}},I_{t_{2}},\ldots\). These steps are crucial in accurately identifying similar images and ensuring reliable results.
# Ambiguity-Resolved Model Tests for Carrier-Phase GNSS
## 4 The Ambiguity-Resolved Tests  
The previous example has shown that there can be quite a difference in performance between the AK and AF tests in the case of carrier-phase GNSS. This is of course due to the very high precision of the GNSS carrier-phase observables--a precision that really becomes  
Figure 1: Tropospheric AK and AF power function curves, computed using their noncentral Chi-square CDF. **Left**: single-epoch GPS skylplot; **Right**: single-epoch, L1 GPS, \(\alpha=0.05\), ambiguity-float (AF) and ambiguity-known (AK) power function curves for tropospheric zenith delay testing.  
exploited once the ambiguities are known. However, the problem is that, strictly speaking, the AK test cannot be applied in the case of GNSS. Although the ambiguities are known to be integer, they are still unknown. Hence, the best one can do is to estimate the ambiguities as integers and formulate the proper test statistic by taking the uncertainty of the integer estimators into account. For sufficiently high ambiguity success rates, i.e., probabilities of correct integer estimation, one can then generally expect the performance of the MIM test to be better than the AF test and approaching that of the corresponding AK test. We now consider different versions of the MIM test. We start with the case of detection.
## 6 Analysis of the Approximation Ratio  
**Theorem 1**: \[|D|\leq 6|M|.\]  
Let us consider the estimation of the size of the dominating set \(D=D_{1}\cup D_{2}\):  
\[|D|=\sum_{m\in\mathrm{M}}c(m)=\sum_{m\in\mathrm{M}}c_{IN}(m)+\sum_{m\in\mathrm{ M}}c_{OUT}(m)\]  
Using the partition of set \(M\) into \(M_{HIGH}\) and \(M_{LOW}\) from Definition 11 and Lemma 12, we obtain  
\[\sum_{m\in\mathrm{M}}c_{IN}(m)\leq 2|M_{HIGH}|+2|M_{LOW}|=2|M|\]  
For external vertices, using Lemma 13, we have  
\[|\Gamma_{1}^{OUT}\cup\Gamma_{2}^{OUT}|\leq 4|M|\]  
Hence, we obtain  
\[\sum_{m\in\mathrm{M}}c_{OUT}(m)\leq 4|M|\]  
Combining the above estimations, we obtain  
\[|D|=\sum_{m\in\mathrm{M}}c(m)=\sum_{m\in\mathrm{M}}c_{IN}(m)+\sum_{m\in\mathrm{ M}}c_{OUT}(m)\leq 2|M|+4|M|=6|M|\]  
Therefore, we have achieved a 6-approximation for the minimum dominating set in triangle-free graphs.  
This is currently the best known approximation ratio for the minimum dominating set problem in triangle-free graphs, improving upon previous results in this graph class.