esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Stirrup on Bond Strength Degradation in Concrete Cracked by Expansion Agent Filled Pipes
## 5 Conclusions  
This study was designed to determine the effect of stirrup on the bond strength between cracked concrete and rebars. From the pull-out test of 36 specimens, the combined effects of induced crack width and stirrups ratio on bond strength were investigated. Finally, to evaluate bond strength in cracked concrete, empirical formulas based on induced crack width were proposed. The following conclusions can be drawn:  
1. By restraining the induced crack widening, stirrups can significantly limit the bond strength degradation in cracked concrete.
2. While ignoring the ambiguity related to corrosion product or rate, crack induced by EAFP can quantify the net amount of damage due to corrosion.
3. The coefficient of variation is 12% for specimens without stirrup from [PERSON] et al. And for specimen without and with stirrup from [PERSON] et al., the coefficients of variation are 14% and 17%, respectively. The proposed formulas for bond degradation in cracked concrete can provide a satisfactory prediction
4. More bond tests based on cracked specimens with smaller stirrup ratios are needed, and the effect on the bond of rebar profile change due to corrosion should be investigated further.  
Figure 17: Comparison of the proposed formulas with study by [PERSON] et al.: (**a**) S0 prediction compared with specimen without stirrup by [PERSON] et al.; (**b**) S1 and S2 prediction compared with specimen with stirrup by [PERSON] et al.; (**c**) typical corrosion-induced cracks in specimen by [PERSON] et al. [13; 15; 29].  
**Author Contributions:** Conceptualization, A.S.S., H.S., and T.K.; methodology, A.S.S., H.S. and T.K.; validation, A.S.S., H.S. and T.K.; formal analysis, A.S.S. and H.S.; investigation, A.S.S. and H.S.; resources, T.K.; writing--original draft preparation, A.S.S.; writing--review and editing, T.K.; supervision, T.K.; funding acquisition, T.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 3 SPO-Based Collision Detection and Obstacle Avoidance Using APF
### Estimation of External Torque Using SPO
#### 3.1.2 Gravity  
In the real world, calculating an accurate dynamic model of the system is problematic. Nonlinearities, unmodelled dynamics, and external disturbances play their roles (gravity, Coriolis, and inertia) in calculating dynamics. The gravity term equations are calculated graphically for our system and are estimated with Simscape gravity torque block (robotics toolbox) [24]. The calculated result has totally comparable conduct with toolbox one when a certain trajectory is provided at joint 2, as shown in Figure 4. The angle \(\theta_{i}\) represents the i-th joint angle and \(l_{i}\) represents a length the of i-th link of the robot manipulator. The gravity equation is calculated as follows:  
\[G_{1} =G_{5} =0 \tag{8}\]  
\[G_{2}=\cos(\theta_{2})*((g*m_{5}*(l_{3}*\sin(\theta_{3}+1.57)+(l_{4})*\sin( \theta_{3}+\theta_{4}+3.1416))\]  
\[+g*m_{4}*(l_{3}\sin(\theta_{3}+1.57)+(0.3*l_{4})*sin(\theta_{3}+\theta_{4}+3. 1416)))\]  
\[+sin(\theta_{3}+1.5708)*m_{3}*g*0.5*l_{3})-7.851*sin(\theta_{2})\]
## 3 Experimental Setup
### Frame Synchronization  
In order to synchronize the keys generated by [PERSON] and [PERSON], the accurate channel BER can be calculated, and then the consistent negotiated key can be obtained by quantizing and encoding. The frame synchronization technology mainly detects whether the signal has appeared by locating the starting point of the signal. Frame synchronization technology  
Figure 5: Experimental setup for key distribution over 200 km SSMF.  
is the most important and basic technology in digital signal processing flow. During the frame synchronization estimation process, the digital signal received by the receiver may be affected by carrier phase noise, fiber channel response, carrier frequency offset, etc. The frame synchronization estimation algorithm needs to have the ability to estimate and compensate for the aforementioned interferences [21; 22]. The position indicated by the arrow in Figure 6 is the starting point of the signal, and the signal before this is the optical noise in the link and the optical carrier that is not completely suppressed.  
Frame synchronization estimation is performed based on the training sequence [23]. The position of the initial point of the signal is obtained by searching the training sequence. The accuracy of frame synchronization estimation depends on the design of the training sequence. The sender signal is:  
\[a(t)=\sum_{n=-\infty}^{+\infty}a_{n}g(t-nT_{s}) \tag{1}\]  
where \(T_{s}\) is the symbol period and \(a_{n}\) is the training sequence. Assuming that both the channel and the carrier frequency offset have short-term invariance, after being affected by the response \(a_{n}\), \(g(t-nT_{s})\) means gate function, the frequency offset \(\Delta f\), and the additive noise \(w_{n}\); the received signal is:  
\[R(t)=\sum_{n=-\infty}^{n=+\infty}a_{n}*h_{n}e^{j2\pi\Delta f}+w_{n} \tag{2}\]  
In general, frame synchronization technology is a process in which the sender gives the initial mark of each frame in the digital time division multiplexing system, and the receiver detects and extracts the mark. In a frame structure, the inserted frame synchronization code word is 400 consecutive 0 bits and 256 0/1 random bits, and the synchronization code is searched and identified from the received data stream for frame synchronization. As shown in Figure 6a, 400 zero bits are used for coarse synchronization. In order to quickly locate and find a general position and maintain synchronization, the peak position in the figure is the best coarse synchronization position. In Figure 6b, 256 randomly generated 0/1 bits are used for fine synchronization. The peak position in the figure is the exact fine tuning synchronization position. Using frame synchronization technology, the frame structure of the receiving end and the sending end is kept the same, ensuring that both parties of legal communication can work normally under the condition of synchronization and recognize the accurate transmission of information.  
Figure 6: (**a**) Coarse synchronization. (**b**) Fine synchronization.
# Quorum Sensing Regulation of Competence and Bacteriocins in _Streptococcus pneumoniae_ and _mutans_
## 2 A Brief Introduction to Quorum Sensing  
Quorum sensing is a method of communication employed by bacteria to coordinate a response amongst a population. This ability is shared between both Gram-negative and Gram-positive bacteria (for a recent review of each, see [PERSON] and [PERSON] [2], and [PERSON] et al. [3], respectively). Briefly, bacterially derived signals, referred to as pheromones, are excreted by bacteria and sensed,in a concentration-dependent manner, by neighboring bacterial cells to elicit a response. These signals can be small molecules, such as acyl-homoserine lactone derivatives, commonly employed by Gram-negative bacteria, or small peptides, generally preferred by Gram-positive bacteria (reviewed by [PERSON] et al. [4]). Since this process is dependent upon the concentration of signals, any condition that influences pheromone concentration (for example, signal degradation, diffusion, or sequestration) will impact the ability of cells to transmit information. The coordinated responses generated by pheromones regulate a wide variety of physiological processes ranging from the expression of virulence factors, a function the PapR/PlcR and NprX/NprR QS systems control in _Bacillus_; to the formation of biofilms, in part, modulated by the SHP/Rgg circuit in _Streptococcus pyogenes_; and the entrance into the competent state, governed by the XIP/ComR pathway in _S. mutans_ (recently reviewed by [PERSON] et al. [5]).  
Competence, a prerequisite of natural transformation, is the capacity of cells to take up extracellular DNA (eDNA) and incorporate it into their genome (see [PERSON] et al. [4]). In this way, cells capable of natural transformation can use eDNA to repair damaged DNA or, alternatively, to acquire new traits, such as antibiotic resistance or the expression of a novel toxin. The nature of the eDNA limits the usefulness to the acquiring cell because homologous recombination is necessary for a successful integration event. Therefore, the more closely related the eDNA is to the genome of the competent cell, the more likely a productive recombination event will occur (reviewed by [PERSON] and [PERSON] [6]). This review focuses on the strategies that _S. pneumoniae_ and _S. mutans_ use to ensure access to related eDNA, a tactic that appears to have co-evolved with the ability to lyse neighboring species and/or strains residing within the same microbial niche.
# Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks
## 2 Establishment of Dataset
### Input Parameters for Models
#### 2.1.2 Porosity of Rocks  
As shown in Table 2, the porosity had an inverse and significant relationship with TC, which represented the more effective parameter for the TC of sedimentary rocks ([PERSON] _et al._, 2016). The void ratio of sedimentary rocks (from 1% up to 80%) could be higher than that of the volcanic and metamorphic ones (at most 1%). The wide range of substances that could fill the void spaces, such as air, water, chemical sediments, or organic matters, was the other parameter controlling TC. However, the reported datasets of the TC rocks, which were used in our study, were obtained in the laboratory, considering dry conditions and the ISRM standards.  
Figure 1: The points show the measured values, and the curve shows the relationship between TC and P-wave velocity. The grey arrow indicates the increase of the porosity.  
Figure 2: Measured values and relationship between TC and P-wave velocity.
## 3 Results and Discussion
### Practical Application of Research Results  
Through the analysis of the residual strength parameters of soils in overburden sediments, it is possible to significantly contribute to the optimization of the slope angle of the final slope of surface mines. This practice is crucial in the design and dimensioning of operational and final slopes in surface mining. By considering the geomechanical characteristics of the sediments, mines can more effectively manage slope stability and reduce the risk of landslides.  
Increasing the angle of the final slope, with a proper understanding and application of soil strength parameters, can lead to significant cost savings in mining operations and the opening of mines. Additionally, this approach can improve the overall efficiency of mining operations, allowing for greater production and a reduction in the resources needed to support slope stability.  
Therefore, it is important to consider not only the current geological conditions when designing slopes but also the long-term stability and behavior of materials under changing working conditions.
## 2 Overview of Time-Series RSS Observation Outlier Detection Methods
### Interquartile Range Method  
This method calculates the interquartile range (IQR) of the time-series RSS observation, which is the difference between the 75 th and 25 th percentiles. RSS observations that are outside of the range defined by the IQR are considered outliers [28]. To use the IQR method for outlier detection, the following steps are followed:  
1. Calculate the first quartile (Q1), the third quartile (Q3), and the interquartile range (IQR = Q3 - Q1) of the data.
2. Identify the lower and upper bounds, which are defined as Q1 \(-\) 1.5 \(\times\) IQR and Q3 + 1.5 \(\times\) IQR, respectively.
3. Any RSS observation that falls below the lower bound or above the upper bound is considered an outlier.  
The 1.5 multiplier used in the upper and lower bound calculations corresponds to approximately 3 SD from the median. This covers 99.7% of the data in a normal distribution. The value 1.5 was chosen at random and can be adjusted to be strict in detecting outliers. I is important to note that the IQR method assumes that the data is approximately normally distributed. Therefore, it may not be appropriate for observations with non-normal distributions or highly skewed observations [26]. Alternative methods, such as the Z-score method or the modified Z-score method, may be more appropriate.
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
###### Abstract  
The location of user equipment (UE) should always be maintained in order to connect any incoming calls within a mobile network. While several methods of location registration have been proposed, most mobile networks have adopted zone-based registration due to its superior performance. Even though recommendations from research on these zone-based systems state that multiple zones can be stored in a zone-based registration system, actual current mobile networks only employ a zone-based registration system that stores a single zone. Therefore, some studies have been conducted on zone-based registration using multiple zones. However, most of these studies consider only two zones. In this study, through the development of a semi-Markov process approach, we present a simple but accurate mathematical model for zone-based registration using three zones. In addition, our research results in zone-based registration systems where one, two and three zones are used to suggest the optimal management scheme for zone-based registration. Given that most mobile networks have already adopted some kind of zone-based registration, these results are able to directly enhance the performance of the actual mobile network in the near future with the minimum of effort required for implementation.  
location registration; zone-based registration; performance analysis; semi-Markov process; mathematical model +
Footnote †: journal: Applied  
1
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### Evaluating Security and Privacy
#### 5.2.3 Deanonymization  
Malicious actors often try linking user identities across blockchain transactions to compromise privacy. Clustering and classification algorithms on user graphs generate fingerprint profiles that quantify re-identification risks [78]. Figure 13 demonstrates how advanced analysis techniques, including clustering and classification, apply to network structure to uncover hidden patterns and compromise user privacy. By simulating clusters and highlighting transaction activity levels, it provides a visual context for understanding the deanonymization risks in blockchain networks.  
Nodes represent individual wallets in the blockchain network. Directed edges (arrows) signify transactions between these wallets. The transaction activity level of each wallet can help identify highly active wallets relevant to the analysis for deanonymization. Node Color simulates clustering results, with nodes color-coded to represent hypothetical clusters identified in a deanonymization effort. These clusters could indicate wallets with common spending patterns or other characteristics that algorithms have grouped together, potentially revealing user identities.  
Figure 12: 51% attack.  
**Assumption 12**.:
* _The blockchain transaction network is a graph_ \(G(V,E)\) _with wallets as nodes_ \(v_{i}\in V\) _and transactions as edges_ \(e_{ij}\in E\)_._
* _User identities are anonymized behind wallet addresses._
* _Transactions have attributes like timestamps, and coin values transferred._  
**Application Statement 12**.:
* _Clustering algorithms applied on user transaction subgraphs can reveal common spending patterns that fingerprint user identities in blockchain networks._  
Proof.:
* Construct transaction subgraphs \(G_{k}\) induced by each user \(k\)'s wallets \(W_{k}\subseteq V\).
* Apply graph clustering methods (e.g., K-means) on \(G_{k}\) based on transaction attributes and temporal patterns to separate coin mixes.
* Use resulting clusters as features to train classifier model \(M_{k}\) identifying user \(k\).
* Apply \(M_{k}\) to full transaction graph \(G\) to predict the presence of user \(k\)'s payments.  
Thus by extracting distinctive transaction features and styles from known user subgraphs, deanonymization classifier models can be constructed to compromise wider blockchain privacy. Graph clustering enables the creation of fingerprint profiles that quantify re-identification risks for blockchain users.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 6 Time-Domain Characterization
### System Fidelity Factor (SFF)  
SSF can be described as the cross-correlation between the normalized input waveform \(T_{s}(t)\) and normalized received waveform \(R_{s}(t)\) and can be evaluated by Equation (9)  
\[SFF=\max_{T}\int\limits_{-\infty}^{\infty}\hat{T}_{s}(t)\hat{R}_{s}(t+\tau)dt \tag{9}\]  
Figure 16: Normalized received pulse for triple band-notched SWB antenna system in face-to-face and side-by-side arrangements.  
Figure 15: Two different orientations of the presented radiator for time-domain analysis: (**a**) side-by-side and (**b**) face-to-face orientations.  
where  
\[\hat{T}_{\rm s}(t)=\frac{T_{\rm s}(t)}{\left[\int\limits_{-\infty}^{\infty}|T_{ \rm s}(t)|^{2}dt\right]^{\frac{1}{2}}} \tag{10}\]  
\[\hat{R}_{\rm s}(t)=\frac{R_{\rm s}(t)}{\left[\int\limits_{-\infty}^{\infty}|R_{ \rm s}(t)|^{2}dt\right]^{\frac{1}{2}}} \tag{11}\]  
where, \(T_{\rm s}(t)\) is the normalized transmitted signal pulse, and \(R_{\rm s}(t)\) is the normalized received signal pulse for the presented triple band-notched SWB antenna.  
Normalization is performed to ensure that only the shape of the signals is considered without taking its magnitude into account. The ideal value of \(SFF\) lies between 0 and 1. If it is 0, then the received signal is completely different from transmitted signal, and if it is 1, then both the transmitted and received signals are identical. The signal is unrecognizable when the \(SFF\) < 0.5. Hence, a practical \(SFF\) should be near to 1 for ensuring reliable antenna performance over wide frequency ranges.  
The \(SFF\) is evaluated by finding the similarity between \(T_{\rm s}(t)\) and \(R_{\rm s}(t)\) using Equation (9). The calculated SFFs for the triple band-notched SWB antenna system for face-to-face and side-by-side configurations were 93% and 90%, respectively.  
As depicted in Figure 16, the undesired oscillations in the received signal for the triple band-notched SWB antenna system in both configurations were considerably small. It could be observed that the received signal had lower distortion and relatively maintained the shape of the transmitted pulse. This confirmed that the received signal was less prone to inter symbol interference (ISI).
## 4 Discussion  
The industrial experiments in acquiring the 3D shearer position conducted by the LASC and CUMT have all demonstrated satisfactory performance, which indicates that integrated navigation using the INS and axial encoder is an effective approach for addressing the shearer automation positioning. One difference is that the LASC uses the IXSEA PHINS INS, while the Spatial FOG INS is adopted in CUMT. The cost of the Spatial FOG INS is only one fifth that of the IXSEA PHINS INS. The same performance as a military INS  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cutting Cycles**} & \multicolumn{2}{c}{**MFS**} & \multicolumn{2}{c}{**GOM**} \\ \cline{2-5}  & **North** & **East** & **North** & **East** \\ \hline First & 0.1795 & 0.2017 & 0.0631 & 0.0781 \\ Second & 0.1583 & 0.1975 & 0.0725 & 0.0820 \\ Third & 0.1272 & 0.1669 & 0.0617 & 0.0868 \\ Fourth & 0.1553 & 0.2018 & 0.0562 & 0.0673 \\ \hline Mean error & 0.1551 & 0.1920 & 0.0634 & 0.0786 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The maximum position error (m) with MFS and the GOM during four cutting cycles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Cutting Cycles**} & \multicolumn{1}{c}{**MFS**} & \multicolumn{1}{c}{**GOM**} \\ \hline First & 1.6942 & 202.5673 \\ Second & 1.8814 & 260.5997 \\ Third & 1.9999 & 293.0323 \\ Fourth & 1.7098 & 229.5243 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The elapsed time (s) with MFS and the GOM during four cutting cycles.  
Figure 19: The position error of the third cutting cycle in the (**a**) north and (**b**) east.  
Figure 20: The position error of the fourth cutting cycle in the (**a**) north and (**b**) east.  
is achieved because the authors of this paper develop a series of error reduction algorithms through fully analyzing the longwall mining method as introduced in Section 3.1.  
For the measurement of longwall retreat and creep displacements, the proposed SAR imaging method has two advantages. First, the propagation of radar signals in underground mines is hardly affected by the presence of dust and moisture. Second, this method is an autonomous measurement technology which does not depend on external information. This method images the bolt-plates fixed in the roadway wall as the targets due to their higher reflective intensity. Although the surrounding objects may affect the imaging result, this method still has strong robustness from the final result of image registration.  
Compared with MFS, the GOM has better performance in backward-correcting the shearer trajectory. Because it needs to iterate to find the optimal value with GA, the GOM has more working time. In the experiment, there are about 2000 data in each cutting cycle, which takes 4 min for the GOM to complete. If a typical longwall face is 300 m long, the running time of the GOM (more than 12 min) will be greater than the time when the shearer is stationary at the end of the longwall face, which limits its practical application. Therefore, it is an urgent problem to find a backward calibration method with high precision and small calculation load.