esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Estimation and Prediction of Tunnel Floor Heave
### Estimation of Tunnel Floor Heave Based on Deformation Statistics  
In this paper, under the condition of IIIb-, IVb-, Va-, and Vb-grade gently inclined thin-layered surrounding rock, six kinds of surrounding rock dip angles (\(0^{\circ}\), \(5^{\circ}\), \(10^{\circ}\), \(15^{\circ}\), \(20^{\circ}\), \(25^{\circ}\)), five kinds of lateral pressure coefficients (0.5, 1.0, 1.5, 2.0, 3.0), and seven kinds of calculation spans (8.83 m, 13.0 m, 9.15 m, 10.0 m, 13.22 m, 9.37 m, 13.6 m), a total of 270 working conditions are numerically simulated. The results are shown in Table 5.  
According to the data in Table 5, combined with the structural statics calculation formula and the previous research results, the maximum floor heave calculation formula can be written as  
\[\mu=k\frac{q!a}{EI} \tag{16}\]  
In the formula,  
\(\mu\) is the maximum floor heave value of the tunnel (mm);  
\(q\) is the uniform load acting on the tunnel bottom structure (kN/m);  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Surrounding Rock Grade—** & & & & & & \\
**Span (m)—** & & & & & & \\
**Side Pressure Coefficient** & \(\mathbf{0}^{\circ}\) & \(\mathbf{5}^{\circ}\) & \(\mathbf{10}^{\circ}\) & \(\mathbf{15}^{\circ}\) & \(\mathbf{20}^{\circ}\) & \(\mathbf{25}^{\circ}\) \\
**Inclination (\({}^{\circ}\))** & & & & & & \\ \hline IIIb-8.83-0.5 & 16.79 & 16.21 & 17.99 & 17.56 & 17.14 & 16.53 \\ IIIb-8.83-1 & 15.40 & 14.93 & 15.76  
\(l\) is the calculation of the span (m) for the tunnel inverted arch structure;  
\(E\) is the elastic modulus of tunnel lining (MPa);  
\(I\) is the tunnel lining section inertia moment (m\({}^{4}\));  
\(k\) is the equivalent coefficient.  
Using Matlab software, based on Table 5 data, the equivalent coefficient is fitted, and the following calculation formulas are obtained:  
\[k=\frac{0.007*\lambda}{s^{3}}-8*10^{-5}*(cos\theta)^{20}+4*10^{-7}*(ln^{s})^{ 15}-5*10^{-4} \tag{17}\]  
In the formula, \(s\) is the grade of surrounding rock; \(\lambda\) is the side pressure coefficient; and \(\theta\) is the rock dip angle.  
This estimation formula has high accuracy after fitting \(R^{2}=0.947\). Randomly testing 90 groups of data, the residual is shown in Figure 22.  
Formula (18) is transformed to obtain the calculation formula of tunnel floor heave load as follows:  
\[q=\frac{EI\mu}{kl^{4}} \tag{18}\]  
The above method is used to estimate and predict the tunnel floor heave, which is consistent with the actual situation, as shown in Figure 23.
## 3 Proposed Algorithm for Energy-Efficient Node Mobility
### Procedure of the Proposed Algorithm
Receiving information of neighboring router nodes: When the algorithm is started, DIS messages are transmitted to the neighboring router nodes, and DIO messages required for parent selection are received from the neighboring nodes until \(I_{MTP}\) expires. 3. Scenario-based parent selection: Afterward, if the current time reaches \(I_{MTP}\), a parent node is selected based on the scenarios presented in Section 3.3. 4. Adjusting the time interval: A DAO message is sent to the router node selected as the parent, and the interval used for the selection of the next parent is dynamically adjusted based on Equation (7) described in Section 3.2.3. 5. Reinitialization of parameters: Next, the current time of the mobile node and the PP attributes in the parent list table are initialized. If there are no values for the PP attributes in the parent list table when the current time reaches \(I_{MTP}\), all values necessary for the mobile node to select the parent node are reset to the values when the algorithm was first executed. Figure 3: An example of selecting the parent of a mobile node from preferred parents. ```
1:\(/*\)The input and output of the proposed algorithm * /
2:\(INPUT:\)Information of router nodes within one hop from a mobile node
3:\(OUTPUT\):Parent node ID
4:
5:\(/*\)Macro definition * /
6:define \(I_{min}\)
7:define \(I_{max}\)
8:struct \(nTable\) {\(nID\), OF }
9:
10:\(/*\)1. Initialization phase * /
11:\(I_{MTP}\) = \(I_{min}\), \(CTime\) = 0
12:struct \(nTable\)\(nTable_{old}[]\)
13:struct \(nTable\)\(nTable_{new}[]\)
14:
15:\(/*\)2. Receiving information of neighboring router nodes * /
16:loop
17:ifcTime = 0 then
18:send DIS to Router Nodes by one-hop broadcasting
19:endif
20:
21:whilecTime \(\leq I_{MTP}\)do
22:if received DIO then
23:\(nTable_{new}[i].nID\) \(\leftarrow\)\(router\_nodeID\)
24:\(nTable_{new}[i].OF\) \(\leftarrow\) OF
25:endif
26:endwhile
27:\(/*\)3. Scenario based parent selection * /
28:ifcTime = \(I_{MTP}\)then
29:ifnTable\({}_{new}\)\(\
eq\) NULL then
30:if\(nTable_{old}\) = NULL then
31:\(nTable_{old}.nID\) \(\leftarrow\)\(router\_nodeID\) selected by highest OF in \(nTable_{new}\)
32:elseif parent node ID in \(nTable_{old}\) = \(router\_nodeID\) in \(nTable_{new}\)then
33:if parent node OF in \(nTable_{old}\) < Threshold to request signal level then
34:\(nTable_{old}.nID\) \(\leftarrow\)\(router\_nodeID\) selected by highest OF in \(nTable_{new}\)
35:endif
36:endif
37:send DAO to a selected router node
38:\(/*\)4. Adjusting the time interval * /
39:\(I_{MTP}=x\) \(where\)\(\tau/2\leq x<\tau\)
40:if\(I_{MTP}>I_{max}\)then
41:\(I_{MTP}=I_{max}\)
42:elseif\(I_{MTP}<I_{min}\)then
43:\(I_{MTP}=I_{min}\)
44:endif
45:else
46:\(I_{MTP}=I_{min}\), \(nTable_{old}\) = NULL
47:endif
48:\(/*\)5. Reinitialization of parameters * /
49:\(nTable_{new}\) = NULL, \(cTime\) = 0
50:endif
51:endloop
```  
**Algorithm 1** Proposed algorithm
## 2 Materials and Methods
### Glycerol and Non-Polar Lipid Analyses  
Glycerol content was determined as previously described [15]. Briefly, _C. reinhardtii_ cells were collected by centrifugation, washed in an isotonic solution, and the pellets frozen in liquid nitrogen and stored at \(-70\)\({}^{\circ}\)C until use. Pellets were resuspended in deionized water, briefly vortexed, and boiled for 10 min to release the glycerol from cells. Cell debris was pelleted by centrifugation at 2000\(\times\)\(g\) for 5 min and the supernatant was used for measuring glycerol amounts with the free glycerol reagent (Sigma, St. Louis, MO, USA, F6428). To assess the content of non-polar lipids, cells were stained with the lipophilic fluorophore Nile Red, as previously described [37]. About 100 \(\mu\)L of cells were transferred to a multi-well plate and mixed with 100 \(\upmu\)L of HS medium. Then, Nile Red (Sigma, 72485) was added to a final concentration of 1 \(\upmu\)g mL\({}^{-1}\) and fluorescence (excitation at 488 nm; emission at 565 nm) was measured in a multi-well plate reader (Synergy H1, Biotek, Winooski, VT, USA). Nile Red fluorescence was finally normalized to cell density (determined as absorbance at 750 nm) and expressed in arbitrary units.
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
## 3 CDA Adoptability and Aircraft Descent Times
### Estimation of Aircraft Descent Time  
In this section, we estimate the time an aircraft takes to descend, starting from the TOD point at cruise altitude down to the runway, under CDA and SDA operations, using version 3.11 of Base of Aircraft Data's (BADA) Aircraft Performance Model (APM) [40]. Estimating aircraft descent time under the two distinct approach operations is a fundamental step towards developing our model.  
BADA is an APM developed and maintained by the European Organization for the Safety of Air Navigation, commonly known as EUROCONTROL, through active cooperation with aircraft manufacturers and operating airlines. To estimate aircraft landing time at airports using BADA, we used BADA's web-based Calculation Tool, the Aircraft Performance Calculation (APC), to calculate aircraft performance for the descent phase of flight.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Trailing Aircraft**} \\ \hline \multirow{2}{*}{**Leading**} & \multicolumn{2}{c}{**Separation in Distance (nmi)**} & \multicolumn{2}{c}{**Separation in Time (s)**} \\ \cline{2-6}  & **Heavy** & **Medium** & **Light** & **Heavy** & **Medium** & **Light** \\ \hline Heavy & 4 & 5 & 6 & 105 & 131 & 158 \\ Medium & & 3 & 4 & 79 & 105 \\ Light & & & 3 & & 79 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ICAO Minimum Wake Turbulence Separation Standards.  
Essentially, BADA's application software provides access to an online implementation of BADA APM, which consists of a database of aircraft operational performance files and formulas derived from the Total-Energy Model that EUROCONTROL relied on to model aircraft performance in categories such as aircraft, aerodynamics (e.g., drag), and engine thrust [41], as follows:
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 2 Related Work
### The Novelty of This Study  
While previous studies have contributed valuable techniques for error correction and anomaly detection, many existing approaches either lack real-time processing capabilities or require extensive computational resources. In contrast, our work integrates a VAE with CNNs to enhance feature extraction and improve detection accuracy while maintaininglow-latency inference. By employing a sliding window mechanism, our approach ensures timely anomaly detection in dynamic V2X environments.  
What sets our study apart is the combination of three design principles: (1) an unsupervised learning approach that eliminates the dependency on labeled attack data, making it suitable for real-world deployment; (2) a lightweight architecture specifically engineered for execution on constrained V2X hardware such as OBUs and RSUs; and (3) a periodic retraining strategy that adapts the model to evolving traffic and environmental patterns while mitigating catastrophic forgetting through selective inclusion of historical data. Together, these contributions result in a scalable, robust, and adaptable solution for securing V2X communications against both known and emerging threats.
# Compliance of Distribution System Reactive Flows with Transmission System Requirements
## 3 Case Study
### Benchmark Case  
The method of Section 2 is compared to the **benchmark** scenario where the reactive support from DERs follows the QV droop curve. A number of curves, discussed in [26], were examined. Results are shown for the most successful of the alternatives, a very responsive curve, which is shown in Figure 7. For reference, both the benchmark and the proposed methods are contrasted to the **zero** case of no reactive power support.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**DER (Node)** & **PV (10)** & **WT (10)** \\ \hline
**Capacity (MW)** & 0.6 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Installed capacity of DERs.  
Figure 6: Measured reactive power exchange between Sundom and the transmission system with respect to the limits set by the TSO. The positive sign indicates power flow from the transmission to the distribution system.  
Note that this comparison is only relative. Our intention is to compare a reactive method (QV droop) with an MPC method in order to show that optimisation-based approaches can achieve full compliance. Our proposed method will not perform the same way when the forecast is not perfect, but the error will be relative to the forecast error, which is expected to be small in very short-time horizons.
# Assessing Blockchain Consensus and Security Mechanisms against the 51% Attack
## 5 The Majority Hash Rate Problem
### Mining Centralization  
Mining centralization is a significant issue in the PoW mining pools due to the dominance of the hashing power. Mining pools with powerful systems can compute more hashes, having a better chance of solving problems than others. Individual pools may involve hundreds or thousands of participants in the mining process being in possession of a large amount of the total network hashing power. Pools with immense hashing power can collaborate to make the network vulnerable. The seven major bitcoin mining pools, out of the top 10, are based on China. Hence, the dominating attributes by a few mining pools have structured the blockchain as a centralized network. Several mining pools were also attacked by DDoS attacks in the past, obstructing their mining activities and demanding a payment to discontinue the attack [31].  
In July 2014, GHash.io reached more than 50% hashing power, becoming a great threat to Bitcoin and the exchanges [45]. They were in charge of the majority hashing for nearly 24 h. However, after breaching the threshold, various participants related to the pool halted their mining power for the sake of the security of the network. The main cause of reaching the majority of hashing was the combined hashing. However, at a later time, GHash.io announced not having accumulated 51% hashing. They agreed to stop accepting distinct mining resources and to implement a feature for participants to mine from various other pools. The pool stopped its mining operation since 2016.  
Figure 5 defines the number of blocks created on 10 March 2019 by only six mining pools in a 24 h period [46]. A total number of 137 bitcoin blocks were generated, where 92 blocks were produced by the six mining pools, making it about 67% of the total blocks during that period. The block counts refer to the total number of blocks generated by a particular mining pool, and continuous block refers to the number of times a pool was able to generate blocks in a sequence. We present that F2 Pool was able to generate 2 or more blocks on three occasions, whereas BTC.com also produced blocks in sequence 2 times. Antpool and ViaBTC generated blocks in sequence once. BTC.TOP and SlushPool could not generate a longer chain of blocks.  
In recent times, the majority hash rate problem has been a serious issue to cryptocurrencies. The low hashing coins constitute higher threats of 51% exploitation. Attackers are focused on coins where the attack cost is minimal to maximize profit. Only a few mitigation techniques have been introduced in recent times to defend against a 51% attack. In the next section, we discuss those protection techniques to assess their effectiveness later.
## 2 Background Knowledge
Cities can decrease the impact of disasters on key infrastructure and services such as hospitals, schools, and transportation networks by making the built environment more resilient [7; 65; 61]. However, challenges like cost and complexity, especially in resource-limited settings, can impede effective risk management [33]. Asset management (AM) involves maximizing the value and performance of tangible and intangible assets [66]. It plays a significant role in UR by helping cities identify and prioritize very important assets. Regular maintenance and the upgrading of assets contribute to a city's financial sustainability and resilience [67]. However, challenges include inconsistent data on asset conditions and the high cost of implementing asset management systems, particularly in cities with limited resources [68]. Asset management objectives, integral to UR, must align with overall organizational goals and comply with financial, technical, legal, and regulatory requirements [4; 69; 70]. These objectives form the strategic asset management plan (SAMP), guiding decisions on managing assets effectively for UR. The regular monitoring, updating, and communication of these objectives are vital for transparency and accountability [71; 72]. AM enhances UR by prioritizing the repairing, upgrading, replacement, or maintenance of assets based on their condition and role in resilience. Decision making involves evaluating cost, risk, and performance, considering factors like asset lifespan and functionality [73]. The choice between actions like repairing, upgrading, or replacing depends on the specific needs of the city and its resilience goals. The contemporary landscape of urban resilience literature presents a multifaceted view of community preparedness, particularly in the face of multihazard scenarios. [PERSON] et al. (2023) explored innovative methods to enhance urban community resilience, particularly through the use of virtual reality and serious game training prototypes (Tal  
the lens of a rigorous, quantitative validation of resilience measures. The proposed Risk-Informed Asset-Centric (RIACT) decision-making process aims to bridge this gap by integrating a range of indicators, including socio-economic and infrastructure metrics, through an entropy weighting algorithm. This approach not only refines the assessment of urban resilience by reducing subjective bias but also substantiates the decision-making process with a transparent and replicable methodology. The iterative nature of the RIACT process, accommodating regular updates based on the latest data and stakeholder feedback, further distinguishes this study from existing studies. It ensures that urban resilience strategies are not static but are instead responsive and adaptive to the dynamic urban environment. This research gap, therefore, is addressed by introducing a comprehensive RIACT framework that provides a scientifically rigorous, evidence-based approach for urban planning and policy making, capable of guiding municipalities in Portugal and potentially other regions in the development of nuanced and locale-specific resilience strategies.
# Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
## 2 Literature Review  
Many methods such as machine learning and data mining are used for churn prediction. The decision-tree algorithm is a reliable method for churn prediction [6]. In addition, a neural network method [7], data certainty [8], and particle swarm optimization [9] are used for churn prediction.  
Moreover, an artificial neural network (ANN) and decision trees are compared for customer-churn predictions [10], and the literature review shows that the decision-tree algorithm is better than ANNs for customer churn prediction.  
[PERSON] [11] studied the effect of customer loyalty on customer churn in prepaid mobile phone companies. In this study, features were segmented, and multiple algorithms such as decision trees and neural networks were used to predict the processed data. Results showed that a hybrid approach is better than a single algorithm. The KNN-LR is a hybrid approach using logistic regression and. the K-nearest neighbor (KNN) algorithm [12]. Researchers surveyed KNN-LR, logistic regression and the radial basis function (RBF) network and found that KNN-LR showed the best performance. [PERSON] [13] proposed a distributed framework for data mining techniques to predict customer churn. The framework improves the CRM quality of service.  
[PERSON] proposed a hybrid genetic programming approach [14]. This study used the K-means algorithm and genetic programming to predict customer churn. [PERSON] used AdaBoost based on the boosting algorithm [15], which summarized existing machine-learning techniques and proposed that AdaBoost showed the best results. [PERSON] proposed a hybrid swarm intelligent neural network model [16], which proposed an intelligent hybrid model based on particle swarm optimization and feedforward neural network for churn prediction. The results show that the model can improve the coverage of churn customers.
## 4 Main Result
### Numerical Study
#### 4.4.3 Satellite Position Transmission Over the Digital Communication Channel with Finite Capacity  
Secondly, consider the case when the satellites exchange each other their LVLH coordinates over the binary communication channels, described in Sections 2.2 and 4.2. It is assumed that each satellite transmits its coordinates \(x_{i}\), \(z_{i}\), \(i=1,2\) to the accompanying one, where the estimates \(\hat{x}_{i}[k]\), \(\hat{z}_{i}[k]\) and \(\hat{\hat{x}}_{i}[k]\), \(\hat{\hat{z}}_{i}[k]\) are obtained. The following quantization and data transmission algorithms are used: Binary quantization (4), adaptive zooming (5), and embedded state estimation (17). For the numerical study, the coding-decoding procedure parameters are picked up as follows: \(M_{0}=1\), \(m_{c}=0\), \(\rho=e^{-0.01T_{0}}\), gains \(l_{1}\), \(l_{2}\) of observer (17) are found by the pole-placement technique for the discrete-time system \(\hat{y}_{i}[k+1]=\hat{y}[k]+T_{0}\hat{V}[k]+l_{1}\sigma_{i}[k]\), \(\hat{V}[k+1]=\hat{V}[k]+l_{2}\sigma_{i}[k]\), where \(y[k]\) and \(V[k]\) are related to estimates \(\hat{x}_{i}[k]\), \(\hat{z}_{i}[k]\) and \(\hat{\hat{x}}_{i}[k]\), \(\hat{\hat{z}}_{i}[k]\) depending on the transmitted coordinate (\(x\) or \(z\)) and the satellite number \(i=1,2\). Initial conditions \(y[0]\), \(V[0]\) are set to zero for all four channels. Therefore, two data transmission channels are supposed to be used on each satellite, so the data transmission rate for each binary communication channel \(R=1/T_{0}\) should be doubled to find the overall channel load.  
The simulation results for \(T_{0}=0.667\) s (\(R=1.5\) bit/s for each coordinate \(x_{i}\) and \(z_{i}\), transmitted from \(i\)-th satellite) and initial conditions \(x_{12}(0)=200\) m, \(\hat{x}_{12}(0)=0.025\) m/s, \(z_{12}(0)=-50\) m, and \(\hat{z}_{12}(0)=-0.025\) m/s are plotted in Figures 6 and 7, where time histories of \(x_{12}(t)\), \(z_{12}(t)\), and \(u(t)\) and the satellites relative trajectories on the (XZ) plane are depicted. Regulation time \(T^{*}=4.84\) h is found by the simulation, which is a bit greater than the regulation time for non-restricted communication channel ( \(T^{*}=4.61\) h), compared with the plots on Figures 4 and 5. The time histories and trajectories for the ideal and limited capacity communication channels differ due to the coder transient at the beginning of the coding procedure, which leads to oscillating control action during the certain transient time of the data transmission procedure. For avoiding these oscillations on practice, such measures as filtering of the control signal at the beginning of the process or the delayed start of the regulation, can be used.
# A Privacy-Preserving Routing Protocol Using Mix Networks in Opportunistic Networks
## 3 Anonymous Routing Using a Mix Network
### Message Routing  
Routing messages in our opportunistic mixnet approach differs greatly from a common mixnet implemented in a more classical network. Messages might not be easily routed, and source routing cannot generally be used. Thus, when the source node chooses the path of mix nodes, delivering the message to such nodes might require routing it through other intermediary nodes. To that end, our proposal uses epidemic routing.  
In epidemic routing, each node forwards a message to all its neighbors until the message reaches its destination. It can produce much flooding in the network and, although it is not the most efficient routing strategy for OppNets, it is the most generic and basic approach, and served as a good basis to evaluate our proposal.  
As an example, Figure 1, shows a possible message delivery from source node \(V_{s}\) to destination node \(V_{d}\). The source node chooses three mix nodes, \(M_{1},M_{2},M_{3}\). The delivery of the message needs to use intermediary nodes, denoted as \(V_{i}\) for \(=1,\ldots,8\). The dashed lines denote epidemic routing, which is used to deliver the message from \(V_{s}\) to \(M_{1}\), then from \(M_{1}\) to \(M_{2}\), and so on. We denoted these routes between each mix node, and source and destination nodes as routing steps. The number of intermediate nodes used in each routing step depend on the network behavior at the moment. As described before, each mix node has to wait until receiving \(k_{i}\) messages, which introduces an additional penalty to message-delivery time.