esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Rock Embedded Drilled Shaft Resistance Factors in Korea based on Field Tests
###### Abstract  
Load and resistance factor design (LRFD) is a limit state design method that has been applied worldwide. Because the data for determining LRFD factors in Korea has been insufficient, the resistance factors suggested by American Association of State Highway and Transportation Officials (AASHTO) in the US have been used for design in Korea; however, these resistance factors were defined based on the characteristics of the predominant bedrock types in the U.S. As such, it remains necessary to determine resistance factors that reflect the bedrock conditions in Korea. Accordingly, in this study, LRFD resistance factors were determined using 13 sets of drilled shaft load test data. To obtain accurate resistance factors, calibration of the elastic modulus of the drilled shaft and the equivalent load-displacement curve considering the axial load and elastic settlement was conducted. After determining accurate resistance values, a reliability analysis was performed. The resistance factors were determined to be within 0.13-0.32 of the AASHTO factors for the shaft resistance, 0.19-0.29 for the base resistance, and 0.28-0.42 for the total resistance. This is equivalent to being 30-60% of the AASHTO-recommended values for the shaft resistance and 40-60% of the AASHTO-recommended values for the base resistance. These differences in resistance factors were entirely the result of discrepancies in the conditions of the rock in the US and Korea in which the shafts were founded.  
Keywords:load resistance factor design (LRFD); resistance factor; drilled shaft; load transfer analysis; elastic modulus; equivalent load-displacement curve +
Footnote †: journal: Applied Sciences
# Hierarchical Parallel Evaluation of a Hamming Code
## 3 Suggested Solution  
This generic hierarchical scheme can be adapted to the evaluation of the Hamming code as follows. Assume that processor \(i\) has an internal vector \(A[i]\) of size \(m\) bits in which the parity bits will be stored, and an additional space of one bit, \(x[i]\). Let \(B[i]\) be the string of the bits in \(A[i]\) that have already been processed. We start in Step 0 with \(n\) processors storing the \(n\) input bits, one bit per processor, in their \(x\) bits, and with all \(B\) vectors being empty.  
The work to be performed by each processor at any Step \(i>0\) is schematically given in Figure 2, in which the \(B\) vectors appear left justified in \(A\) in light gray, and the \(x\) bits in darker gray. Refer to the processor which is being updated as parent and to the two processors on which the parent acts as its children. The \(B\) vector of the parent is obtained by bitwise XORing the \(B\)-vectors of the children and adding in front of it the single \(x\) bit stored in the right child. In addition, the \(x\) bit of the parent is the XORing of the \(x\) bits of the children.  
The formal algorithm is given in Algorithm 1. The operator \(\parallel\) stands for concatenation. At the end, the \(m\) parity bits are stored in \(B[n-1]\).  
Figure 1: Schematic representation of the hierarchical evaluation.  
Figure 2: Schematical view of the work of a single processor.  
If these four parity bits are inserted in their proper places instead of the 0-stubs, as in Equation (2), we get the 15-bit Hamming code, encoding the eleven-bit data we started with. If this block is transmitted, it can be checked for errors by applying exactly the same apparatus again. The resulting code in the \(B\) vector on the top right will then be 0000, indicating that there is no error.  
Suppose now that there is a single error in the block, namely at bit position 13. Applying once again the hierarchical structure of processors, the code in the top right \(B\) vector is 1101, which is the standard binary representation of the number 13--the index of the wrong bit. All that is left to do then is to flip the bit in the thirteenth bit position to restore the original input.  
According to the parallel random-access-machine (PRAM) complexity model, the work of the hierarchical parallel Hamming code presented in Algorithm 1 for \(n=2^{m}\) elements is \(O(n)\), while the span is \(O(m)=O(\log n)\), using \(p=\Theta(n)\) processors.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 6 Survey of Some of the Existing Node Clustering Techniques
[24] proposed adaptive clustering for CR-WSNs called low-energy adaptive uneven clustering hierarchy (LEAUCH) for cognitive radio sensor networks. LEAUCH elects candidate CH nodes based on the number of idle channels, and selects a CH based on distance from the sink. To avoid the energy hole among CHs, it employs an uneven-sized clustering method (i.e., fewer members in the clusters near the sink). [PERSON] et al. [25] proposed a distributed solution to establish a common control channel in a geographic area using spatial spectral correlation for virtual clustering for CR-WSNs. It reduces route failure and energy consumption. [PERSON] and [PERSON] [26] studied clustering for CR-WSNs. This work formulates the throughput maximization problem as mixed-integer nonlinear programming to find optimal sensor clustering and to utilize the Branch and Bound algorithm. [PERSON] and [PERSON] [27] proposed a spectrum-aware cluster-based routing (SCR) protocol for CR-WSNs. Nodes are clustered based on their relative spectrum awareness and residual energy. The protocol selects CHs based on energy and relative spectrum awareness, such that non-contiguous available spectrum bands are clustered and scheduled to provide continuous transmission opportunities. [PERSON] et al. [28] proposed a cognitive radio-based hybrid data-type clustering (CR-HDC) algorithm for CR-WSNs. It uses blocking and forced termination probabilities for CH transmission range and channel occupancy. General objectives of clustering in CR-WSNs should include CCC establishment, stability, energy efficiency, cooperative tasks, and PU protection. Clustering metrics should be channel availability, geographic location, signal strength or channel quality, number of optimal clusters or cluster heads, etc. Many existing clustering schemes are extensions of clustering schemes for WSNs, with some performance enhancements, such as a lower number of clusters, lower clustering overhead, lower energy consumption, etc. Table 2 summarizes the existing clustering schemes in the literature regarding their objectives, the metrics they use for clustering, performance enhancements, and CH selection methods. The clustering algorithms described in Table 2 consider all nodes as being equipped with CR. Many of them have no CCC or rendezvous notion. These protocols do not consider PU protection as one of the main objectives. CHs do not have dedicated hardware, and are mostly iteratively selected based on residual energy and number of idle channels.
## 4 Conclusions  
In this paper, we systematically investigated the nonlinear failure mechanism of transpose oil and gas pipelines under strong earthquake conditions using ANSYS Workbench finite element analysis software and a fluid-solid coupling method. Additionally, we considered the potential failure modes of oil and gas pipelines resulting from the interaction between cable-stayed pipe bridges and the structural components of the pipeline. The following conclusions were drawn from this study:  
1. By employing ANSYS Workbench finite element software, we analyzed the failure mode of long-span oil and gas pipelines subjected to strong earthquakes. The failure mode remained consistent for both unidirectional and multi-directional ground motion input. The maximum deformation occurred at the midpoint between the left and right spans, while maximum stress was observed at the inlet and outlet ends of the pipeline.
2. Under unidirectional seismic waves, longitudinal seismic waves (X direction) had a greater influence on pipeline deformation compared to vertical seismic waves (Y direction), with maximum deformation concentrated in the left and right spans,  
Figure 9: Time-history curve of lateral acceleration at the top of the bridge tower.  
while transverse seismic waves (Z direction) exerted a stronger impact on the top of bridge towers.
3. The deformation of the pipe bridge system is influenced by the pipeline medium in a complex manner: when longitudinal and vertical seismic waves had a significant impact, the pipeline deformation was mitigated, when longitudinal and transverse seismic waves acted together, the top of the bridge tower experienced intensified deformation, and when three-directional seismic waves occurred, a further reduction in the pipe bridge system deformation was observed.
4. Strong earthquakes can cause failure and damage to pipelines due to interactions among various components within the pipe bridge system. The interaction between the support and the pipeline led to a longitudinal slip, resulting in stress concentration and buckling failure of the pipeline. Additionally, large displacements of the bridge tower during strong earthquakes can lead to its collapse. As the primary support for the entire cable-stayed tube bridge system, such collapse damage will ultimately result in pipeline failure.  
X.Z., formal analysis, methodology, validation, visualization, and conceptualization; G.W., formal analysis, methodology, software, and editing. All authors have read and agreed to the published version of the manuscript.  
The financial support for this work was provided by the National Natural Science Foundation of China (No. 52174061), the Natural Science Foundation of Shaanxi Province, China (2022 JM-279), the Yulin Science and Technology Plan Project (CXY-2020-095), the Xi'an Shiyou University Youth Science and Technology Innovation Fund Project (2015 BS55), and the Key Research and Development Program of Shaanxi (Program No. 2022 GY-431).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Analysis
### Analysis of Causes of Horn Use in Green Light Phases When Left Turns Were Possible  
To explore measures to reduce horn honking, we conducted a microscopic analysis of how drivers use their horns [41]. Video data were analyzed in detail for instances where horns were used during signal phases allowing left turns (specifically, phase II at Intersections 1 and 2 and phase IV at Intersection 3). First, we categorized the situations in which vehicle horns were used into four types: (**1**) cutting in line, (**2**) slowness, (**3**) sudden stops, and (**4**) stopping state. Situation (**1**) is a circumstance in which a driver of a car who interrupted the line to make a left turn was honked at by the driver of another car. Situation (**2**) refers to a circumstance where a driver of a car driving slowly was honked at by another driver. Situation (**3**) is a circumstance in which a driver had to stop suddenly because the vehicle in front of them stopped, and they used their horn. Situation (**4**) refers to a circumstance in which a driver honked at another car that remained stopped at the stop line even though he/she could turn left after the green light turned on. Table 8 shows the number of these situations in which drivers used their horns during the above phases of the entire measurement period. Excluding the situation where the cause of horn use was unclear (situation (**5**)), horn use was frequently found in situation (**4**) (stopping state situations) at each intersection.  
Figure 6: Estimation of the frequency of horn use if the duration of each green light phase is 10 min. The upper part shows 10 min spans in every hour. The middle part shows the counting procedure of the frequency of horn use in each of the four green light phases.  
Next, regarding situation (**4**), the duration from when the light turned green until the car in front of the stop line started moving (the start delay time) was measured using video data, and then the start delay times were compared between when a horn was used and when a horn was not used. Intersection 1 had two lanes for left turns. Therefore, the same analysis was performed in each lane at this intersection. Table 9 presents the mean start delay times with and without horn use at each intersection. The mean start delay times were over 4 s when horn use occurred, but only about 2 s when horn use did not occur. T-test results revealed significant differences of the mean start delay times. The results suggest that delayed departure of the vehicle in front is one of the major causes of driver horn use at intersections.
# 2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization
## 3 Statement of Problem and Notation  
Because some real life situations are not considered in [PERSON] model except the main assumption that returns are normally distributed, novel research directions are needed for portfolio models. One method is considering some constraints that have to be taken into account; e.g., restriction of the number of assets in the portfolio, limitation of investing in assets with common characteristics (investing in vehicle manufacturing industry, petrochemical industry, bank assets, etc.) and avoidance of very small holdings which are cardinality, class and quantity constraints, respectively. Admittedly, these constraints need discrete variables to be used to transfer the search space into a non-convex set. The present paper considers the following model:  
\[\begin{split}\min F_{1}&=\sum_{i=1}^{n}\sum_{j=1}^ {n}x_{i}x_{j}\sigma_{ij}\\ \max F_{2}&=\sum_{i=1}^{n}x_{i}\mu_{i},\end{split} \tag{1}\]  
subject to:  
\[\sum_{i=1}^{n}x_{i} =1, \tag{2}\] \[l_{i}\delta_{i} \leq x_{i}\leq u_{i}\delta_{i},\] (3) \[L_{m}\leq\sum_{i\in C_{m}}^{n}\leq U_{m},\quad m=1\ldots M,\] (4) \[L_{m}\leq\sum_{j\in C_{m}}^{n}\mu_{i}\leq U_{m},\quad m=1\ldots M,\] (5) \[\sigma \in\{0,1\},\quad i=1\ldots n,\]  
where, \(n\) is the number of available assets, \(k\) is the number of assets in the portfolio, \(\sigma_{ij}\) shows the covariance between assets \(i\) and \(j\), \(\mu_{i}\) determines the mean return of the portfolio, and \(x{=}(x_{1},\ldots,x_{n})\) represents the proportion weights of initial budget that is allocated to each asset. Equation (2) is budget constraint that insures all available capital is invested. Equations (3) and (4) are cardinality and quantity constraints, respectively, both of which have binary variable of \(\delta_{i}\). If asset \(i\) holds in the portfolio \(\delta_{i}=1\) else \(\delta_{i}=0\). The former shows that \(k\) assets exist in the portfolio and the latter considers lower and upper bounds for each existent asset in the portfolio. Equation (5) shows class constraint and for all \(i{\
eq}j\), \(C_{i}\cap C_{j}=\emptyset\) that means there are unique sets of assets. Two points should be considered in this constraint:  
1. \(L_{m}>0\) for \(m=1,\ldots,M\), This limitation ensures that at least one asset from each class should be chosen;
2. \(k\geq M\), which means the portfolio should have more assets that classes (this note is concluded from the first point).  
Also, \(F_{1}\) and \(F_{2}\) are the risk and return of the optimal portfolios, respectively. Since there are two contradictory objectives, it is not possible to obtain single optimal solution; rather, a set of solutionscalled Pareto-optimal solutions is found and contains all feasible solutions not dominated by any other solution in the feasible set. It is said that solution \(x_{1}\) dominates solution \(x_{2}\) if:  
\[F_{2}(x_{1})>F_{2}(x_{2})\;\;and\;\;F_{1}(x_{1})\leq F_{1}(x_{2})\]  
or  
\[F_{2}(x_{1})\geq F_{2}(x_{2})\;\;and\;\;F_{1}(x_{1})<F_{1}(x_{2})\]
# Study on the Evolution Law of Overlying Rock Collapse Induced by Mining Based on BOTDR
## 5 Vertical Optical Fiber Strain Monitoring Analysis
### Overlying Rock Collapse Height and Optical Fiber Strain Analysis
Unlike the caving zone, the strain curve in this region shows a gradual upward trend, reflecting a progressive failure mechanism in the rock mass. Bending Subsidence Zone: Situated above the fracture zone, this area is primarily influenced by bending deformation rather than fracturing. The optical fiber strain values here are relatively low and stable, suggesting no large-scale fracturing or collapse. Although strain values change slightly with continued face advancement, their overall variation remains minimal, indicating that the rock mass retains significant integrity in this zone. The optical fiber strain curve clearly delineates the three-zone structure of overlying rock failure: Caving Zone: Characterized by violent strain fluctuations due to collapse. Fracture Zone: Marked by increasing tensile strain as fractures develop. Bending Subsidence Zone: Strain remains relatively stable, indicating overall rock integrity. These findings demonstrate that optical fiber strain monitoring provides a reliable method for characterizing overlying rock failure in coal seam mining, offering valuable insights for collapse prediction and stope stability assessment.
# Blockchain-Based Quality Assurance System for Academic Programs
## 4 Experimental Results and Discussion
### Features of the Proposed Blockchain-Based QAS
#### 4.3.5 Efficiency  
Within the current blockchain-based ecosystem, smart contract technology is utilized to enhance the efficiency of quality assurance transactions. These transactions are automated and can be processed much faster than manual QAS processes. For instance, once the HEI representative signs the smart contract, the request for evaluation is automatically transmitted to the accreditation authority. Similarly, when quality members approve a particular quality report by signing the corresponding smart contract, the subsequent quality assurance procedure is automatically triggered at a higher level of authority. This streamlines quality processes, saving time and effort and ensuring promptness.  
Moreover, by employing smart contracts, the need for a third party to oversee QAPs is avoided, as all transactions are automated and transparent. This reduces the workload for all parties involved in the QAS and conserves valuable resources.
## 4 Applications in Clock Recovery and Fiber Optic Supported Radio Frequency Communications
### Clock Recovery
The integrated timing jitter over a 90-kHz bandwidth was below 2 ps. A comprehensive way to characterize the performance of a recovered clock signal is to measure the BER. First, the quality of data detection was investigated for a direct clock situation where the clock signal from the pattern generator was used to trigger the BERT equipment. Next, without illumination, because of the drifting of the RTD oscillation frequency it was not possible to provide a clock signal to trigger the BERT instrument. Then, after illumination the optical injection locking mechanism provided a stable oscillation reference source and clock recovery was performed where the optically injection locked RTD-PD electrical output was used to trigger theBER equipment. Figure 9b shows the BER at 1.24 Gb/s as a function of the received power for both direct and recovered clocks using a data pattern length of \(2^{7}-1\). The two BER curves are similar, confirming the low jitter of the extracted clock signal. It is believed that the mismatch in some of the BER points results from environmental instabilities (vibration, temperature fluctuations) which can be reduced significantly with further optimizations in the measurement setup. Figure 8: (**a**) The 1.24 GHz clock recovery in the frequency domain from an optically injected RZ data signal using an in fiber optical power level of \(\sim\)3 mW. (**b**) Power spectra of the free-running signal and the 1.24 GHz recovered clock (span of 10 MHz). The inset shows the recovered clock in the time domain. In summary, clock recovery using RTD-PD oscillators was demonstrated by taking advantage of the optical injection locking capabilities of the devices. One of the attractive properties include the power of the recovered signal is independent of the input power of the signal to be recovered. Other attractive properties include frequency tunability and relatively wide tracking range.
# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 3 Experiment and Results
### In-Depth Analysis of the Proposed Method  
#### 3.3.1 Verifying Robustness to Initial Pose Deviation  
In this section, the repeatability and robustness to initial pose deviation of Proposed-A and -B were tested. The DL-B method was employed in the rough positioning process, and the test was repeated 20 times for each image. Furthermore, different max Gaussian  
Figure 8: Visualization of estimation performance. (**a**) The error is reflected by the projected deviation of a component with an 8-point box. (**b**) Performance in dealing with dense noise and geometric differences. image noises were added to the coordinates of the two 8-point bounding boxes recognized by DL-B. In Table 2, \(\overline{\tilde{a}}_{e}\) represents the average value of \(\overline{\tilde{a}}_{e}\) in all datasets, \(\overline{\tilde{a}}_{e}\_std\) is the standard deviation of all \(\overline{\tilde{a}}_{e}\), \(\overline{\tilde{a}}_{e}\_min\) and \(\overline{\tilde{a}}_{e}\_max\) denote the extreme value of \(\overline{\tilde{a}}_{e}\), and the same is true for \(\overline{\tilde{t}}_{e}\), \(\overline{\tilde{t}}_{e}\_std\), \(\overline{\tilde{t}}_{e}\_min\), and \(\overline{\tilde{t}}_{e}\_max\). It can be clearly seen that the proposed method exhibits the ability to adapt to rough poses within the error range of 6.12\({}^{\circ}\),187.53 mm. Moreover, as shown by the results of \(\overline{\tilde{a}}_{e}\_std\) and \(\overline{\tilde{t}}_{e}\_std\) in Noise-0, it is reasonable to believe that the proposed method has suitable repeatability, and this repeatability can be maintained until the noise exceeds the tolerance (e.g., Noise-10). As shown in Figure 9, the pose estimation process was visualized according to the number of iteration rounds. It can be found that the rough pose with large error requires more exploration, which means that the effective update of the feasible region decreases and the value of \(\overline{iter}\) tends to increase. The update lag of \(\mathcal{F}\) reduces the probability of occurrence of offspring with suitable \(vp^{q}\) before termination, thereby increasing \(\overline{\tilde{a}}_{e}\_max\) and \(\overline{\tilde{t}}_{e}\_max\). Due to the existence of evolution function optimization and multiple rendering mechanisms, the performance gap between -A and -B is obvious, which can also be found by the \(1-vp^{q}\) and projection errors of -A and -B with the same \(iter\). Once the error of the initial pose exceeds the acceptable range, such as Noise-12, the performance of both groups is destroyed. Presently, it is necessary to modify the method parameters to improve the degree of exploration, such as increasing the initial ellipsoid size, \(s_{0}\), \(\tau_{0}\), and \(\sigma_{0}\).