esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, despite these advantages, there has been no research investigating the installation of elastomeric bearings on the shear keys of offshore wind GC, thus motivating this study. The elastomeric bearing, which comprises bearings made by intersecting reinforcing steel plates with elastomers of rubber material, was modeled to surround the shear key, and it was devised on the assumption that it would be possible to prevent grout failure due to the stress concentration through the elastomeric bearings under the extreme loads. The ultimate load of a 3.6 MW class turbine was applied. In general, the load received by the pile in supporting an offshore wind turbine includes an axial load due to the weight of the turbine system and a bending moment due to an environmental load, and these two types of loads have been studied separately [12; 13]. However, according to DNVGL-ST-0126, since the structural response of the grout shear key to two types of loads occurs in a complex way, it is practical to apply them simultaneously for the analysis of the GC. Although the combined action of these two types of loads has been continuously studied [14; 15; 16], it is a relatively realistic approach because GCs are the main subject of analysis. This load causes inclination of the pile head, which may change the direction of the load applied to the GC. According to DNVGL-RP-0419, the sub-modeling method can be applied when analyzing the GC; however, since it tends to ignore the change in the load direction due to the inclination of the pile, in this paper, the full model was analyzed. The changes in contact pressure and shear stress due to the installation of the elastomeric bearings around the shear keys were analyzed. The guidelines for GC during FE analysis are detailed in Section 2.1. ANSYS 2021R1 was used as the FE analysis software. In Section 2.1, some of the design criteria for the monopile GC are presented, along with an identification of what should be paid attention to when designing GC. In Section 2.2, the site of the research model and its ultimate load are explained, and, in Section 3, the FE model is introduced in detail. Section 4 deals with the comparison of contact pressure and shear stress of the grout obtained through FE analysis.
# Equatorial Plasma Bubbles: A Review
## 3 Development of EPBs and Associated Irregularities: Theoretical Aspects
### Electrostatic Rayleigh-Taylor Instability
[65] explored the effect of finite parallel conductivity on the development of EPBs and some features of EPBs in EIA regions. The maximum height over the dip equator to which EPBs rise also varies from one day to another. [PERSON] et al. [66] carried out 3-D simulations to understand this aspect of EPB development. Another set of 3-D simulations evaluated the contribution of collisional shear instability (which can act at low altitudes in the valley region), in combination with GRT instability, to the EPB phenomenon [67]. From a practical point of view, it is necessary to understand how irregularities develop within EPBs when forecasting equatorial and low-latitude scintillations. Towards this end, 3-D simulations of the development of EPBs or plasma plumes were carried out [68]. However, due to limitations regarding spatial resolution, information about the spectral density of fluctuations in vertical total electron content (TEC) could not be obtained for wavelengths shorter than 10 km. A 3-D high resolution bubble (HIRB) model was developed by [PERSON] et al. [69] to simulate the development of structures down to a scale size of 1 km. Even without achieving the resolution required for forecasting scintillations on L-band radio signals, these simulations have yielded important results on the development of irregularities within EPBs, as will be discussed later.
## 3 Performance Analysis
### Bit Error Probability of MSSS-I/Q-IM  
It is worth noting that the total number of transmitted bits in one MSSS-I/Q-IM symbol is \(N=g_{1}+g_{2}\), where \(g_{1}\) denotes the number of index bits and \(g_{2}\) denotes the number of modulated bits. Consequently, the total BER of the MSSS-I/Q-IM system is a function of the BER of index bits and modulated bits. In this vein, assuming all bits are transmitted with equal probability, the BER of the MSSS-I/Q-IM system is calculated as  
\[P=\frac{g_{1}}{g_{1}+g_{2}}P_{ind}+\frac{g_{2}}{g_{1}+g_{2}}P_{mod}, \tag{9}\]where \(P_{ind}\) and \(P_{mod}\) represent the error probability of index bits and modulated bits, respectively. The error probability of index bits depends on the error probability of index detection, which is denoted by \(P_{ed}\), and their relationship can be expressed as  
\[P_{ind}=\frac{2^{g_{1}-1}}{2^{g_{1}}-1}P_{ed}. \tag{10}\]  
In addition, the detection errors of modulated bits are dependent on two completely different cases. In the first case, it still has \(1/2\) correct modulated bits for incorrect index detection [14]. For the second case, modulated bits are wrong with probability \(P_{c}\) when the index detection is correct. In conclusion, the error probability of modulated bits \(P_{mod}\) can be expressed as  
\[P_{mod}=P_{c}(1-P_{ed})+\frac{1}{2}P_{ed}. \tag{11}\]
# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
## 5 Simulation Results
### Performance Evaluation  
The following simulations were performed in a single-user (SU) single-input single-output (SISO) environment. It can be noted that all the proposed schemes in this paper can also be applied to MIMO systems, but the superiority of the proposed scheme over conventional ones can be sufficiently shown through just SISO simulations.
## 2 Sector-Specific Applications
### Energy  
Energy sectors leverage low-code for predictive maintenance and grid optimization. A notable application is ENEL's use of Siemens' MindSphere to monitor renewable energy assets. By deploying low-code apps on edge devices, field technicians receive real-time alerts for turbine anomalies, cutting downtime. Similarly, [PERSON] et al. [54] propose a low-code knowledge graph framework for smart grids, enabling dynamic load balancing through visual rule configuration [57].  
Low-code also enhances SCADA system adaptability [58; 59]. The hybrid model resolves the tension between low-code's speed and traditional coding's precision [20].
## 3 Results
### Analysis on Influencing Factors of Plant Diversity in the Yellow River Delta
#### 3.3.2 Relationship Between Plant Diversity and Influencing Factors
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Riverbank** & **Plant Diversity** & **Regression model** & \(R_{adj}\)2  \\ \hline \multirow{4}{*}{Main stream} & [PERSON] (R) & R = \(-\)0.841 HID + 0.927 & 0.554 \\ \cline{2-4}  & [PERSON]–[PERSON] (H) & H = \(-\)1.201 HID + 1.177 & 0.580 \\ \cline{2-4}  & [PERSON] (D) & D = \(-\)0.639 HID + 0.626 & 0.509 \\ \cline{2-4}  & [PERSON] (J) & J = \(-\)0.104 SEC + 0.586 & 0.548 \\ \hline \multirow{4}{*}{Tributaries} & [PERSON] (R) & R = \(-\)0.080 SEC + 0.476 & 0.326 \\ \cline{2-4}  & [PERSON] (H) & H = \(-\)0.102 SEC + 0.613 & 0.354 \\ \cline{1-1} \cline{2-4}  & [PERSON] (D) & D = \(-\)0.055 SEC + 0.338 & 0.338 \\ \cline{1-1} \cline{2-4}  & [PERSON] (J) & J = \(-\)0.061 SEC + 0.586 & 0.255 \\ \hline \hline \end{tabular} Note: \(R_{adj}\)2 represents the adjusted R-squared; HID refers to human interference degree; SEC refers to soil electrical conductivity.[ENDFOOTNOTE]  
\end{table} 
Table 10: Stepwise regression analysis of plant diversity and influencing factors in the main stream (n = 14) and tributaries (n = 19). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Factors** & **SPH** & **SEC** & **SMC** & **DR** & **Slope** & **Height** & **Lat** & **Lon** & **HID** \\ \hline VIF & 1.14 & 4.47 & 1.77 & 2.33 & 1.51 & 3.65 & 2.17 & 2.35 & 1.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of the collinear analysis. A Mantel test analysis (Figure 11) represented that the SEC had an extremely significant effect on the four diversity indexes (\(p<0.01\)), and the correlation with the four indexes was the largest. The DR and Lon were significantly correlated with the D, H, and J indexes (\(p<0.05\)). The height was significantly correlated with the R, D, and H indexes (\(p<0.05\)). Therefore, the SEC was the key limiting factor of plant diversity, which was consistent with the result of the RDA. Based on the RDA and Mantel test analysis, we found that there were differences in the responses of these plant diversity indexes to environmental factors,  
Figure 10: The contribution of influencing factors to plant diversity. SEC: soil electrical conductivity; SMC: soil moisture content; HID: human interference degree; DR: the nearest distance from the Yellow River; Lon: longitude. Figure 9: RDA results of plant diversity index and influencing factors. The red arrows indicate the plant diversity index, and the blue arrows indicate the influencing factor. R: [PERSON] index; H: Shannon–Wiener diversity index; D: Simpson index; J: Pielou index; SPH: soil pH; SEC: soil electrical conductivity; SMC: soil moisture content; Lat: latitude; Lon: longitude; DR: the nearest distance from the Yellow River; HID: human interference degree. but SEC was always the most closely related to plant diversity, which had a very important effect on the development of species diversity.
# Experimental Evaluation of Load Distribution between Piles in Case of Block Failure
## 4 Conclusions  
In this study, similar to previous studies, it was determined that corner piles carry more load than center piles at small settlement values.  
In pile raft foundations, as the settlement amount increases, center piles start to carry more load compared to corner piles.  
In the experiments, it has been observed that the soil between the piles behaves like a block and acts as a single piece in the soil. This block is caused by a fracture surface formed around the pile group after loading. This fracture surface becomes evident when the pile foundation starts excessive settlement.  
In pile raft foundations after excessive settlement, failure occurs first in the corner piles. Therefore, in allowable settlement conditions, the pile raft foundations should be designed by taking the bearing capacity of the corner piles into consideration.  
After failure caused by excessive settlement, the load carried on the corner piles should be taken into consideration and failure analyses of the structure should be carried out accordingly.  
Figure 14: Consolidation pressure vs. \(\mathrm{q}_{\mathrm{cor}}/\mathrm{q}_{\mathrm{gen}}\) ratio.  
Figure 15: File space vs. \(\mathrm{q}_{\mathrm{cor}}/\mathrm{q}_{\mathrm{gen}}\) ratio.  
Another result is that the center piles have a higher bearing capacity than the corner piles in case of failure due to excessive settlement.  
As the consolidation pressure increases in cohesive soils, the soil becomes brittle; therefore, failure occurs at smaller settlement values in soils consolidated under high pressure.  
Similar to other studies in the literature, for soils which consolidated at low pressures, center piles carry more load compared to the corner piles in allowable settlement limits. However, as the consolidation pressure increases, corner piles start to carry more loads than the center piles.  
As the space between piles increases in case of excessive settlement, the amount of load carried by the center pile increases. However, the effect of the increase in the space between the piles on the \(\mathrm{q_{cor}}/\mathrm{q_{cen}}\) ratio decreases after a certain value.  
A.O. (conceptualization, formal analysis, methodology, validation, writing--original draft preparation); H.S.A. (formal analysis, funding acquisition, supervision, review and editing). All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Firat University Scientific Research Projects Coordination Unit (FUBAP) with the project number MF.18.43. FUBAP unit carries out the evaluation, acceptance, support of scientific research project proposals carried out by Firat University faculty members, execution of related services, monitoring and evaluation of their results.  
Not applicable.  
All data, models, and code generated or used during the study appear in the submitted article.  
The authors declare no conflict of interest.
## 4 Algorithm Description
### Meta-Data Extraction
#### 4.2.2 Contact Meta-Data  
Nodes also record their encountered nodes as _contact log_ (\(CL\)). Tuple in \(CL\) is in the form of \((node\_id,t_{b},t_{e})\), where \(node\_id\) is the \(id\) of encountered node, \(t_{b}\) and \(t_{e}\) denote the beginning and the end of the contact respectively, and the contact duration \(|t_{e}-t_{b}|\) should be greater than a predefined threshold. Based on the contact logs, the _number of distinct contacts_ of \(s_{i}\), denoted as \(\eta_{i}\), could be calculated. \(\eta\) is a key parameter for proxy node selection. The larger \(\eta\) is, the more active the node would be, and the more suitable for the node to be a proxy node.  
Also, ADG adopts an opportunistic strategy when forwarding messages in MON. A node, e.g., \(s_{i}\), could calculate the encounter probability with any other node given an observing time window \(W\):  
\[p2(s_{i},s_{j})=\sum_{e_{k}\in W}g(s_{i},s_{j},e_{k})=\sum_{e_{k}\in W}\frac{ sgn(s_{i},s_{j},e_{k})}{2^{|W|+1-k}} \tag{7}\]  
where \(g\) is a user-defined weighting function, \(sgn\) is a function signifying 1 or 0. When \(s_{i}\) encounters \(s_{j}\) at \(kth\) epoch, \(sgn(s_{i},s_{j},e_{k})\)=1, and the weight is \(\frac{1}{2^{|W|+1-k}}\); otherwise, when they do not encounter each other, \(sgn(s_{i},s_{j},e_{k})\)=0, and the weight is \(0\). The calculation is similar with that in Equation (4), and we assume later contacts has greater impact on the weight. For example, when |W|=4, if node \(s_{i}\) encounters \(s_{j}\) at the 2 nd and 4 th epoch, the encounter probability \(p(s_{i},s_{j},\Delta)\)=\(\frac{1}{2^{4+1-2}}+\frac{1}{2^{4+1-4}}\) = 0.625.
# Design and Implementation of Inverse Kinematics and Motion Monitoring System for 6 DoF Platform
## 4 Monitoring System Design
### Gyroscope and Angular Velocity Meter Jig  
Three angular velocity sensors were fixed on the cube to measure angular velocity in the x, y, and z directions. The attitude gyroscope and the cube were fixed on a magnetic bottom plate, the magnetic attraction was approximately 47 kg. The adherence distance was adjusted using three screws to avoid the strong impact of direct magnetic adherence from damaging the sensors. Two powerful magnets at the bottom of the jig were adhered to the six-axis platform to be tested. To avoid the impact of powerful magnetic adherence, three knobs were designed. Before adherence, the knobs were turned clockwise to the end such that the three screw threads formed three points of support. After aligning with the x direction of the platform, the jig was adhered to the platform, and the three knobs were turned counterclockwise for the bottom plate to fit closely to the platform, the jig was thus firmly adhered to the platform. The jig could be easily dissembled by turning the three knobs clockwise to the end, as shown in Figure 6.
## 2 Related Work
### Detection-Based Counting Methods  
Detection-based counting methods rely on object detection models, which are trained using processed images or video frames. After the model is trained, it can detect objects that generate bounding boxes and count them by counting the number of bounding boxes. For example, Faster R-CNN was used to detect cotton seedlings and corn tassels [10; 11], while [PERSON] et al. [12] fine-tuned Mask R-CNN to perform individual segmentation on potatoes and lettuce. [PERSON] et al. [13] improved Faster R-CNN, using the Inception-v3 module as a feature extractor, and combined intelligent decomposition and label smoothing techniques to solve the representation bottleneck and overfitting problems, thereby improving the accuracy of detection and counting. [PERSON] et al. [27] combined Faster R-CNN and VGG16 models to detect rice planthoppers and improved the counting accuracy through transfer learning and data augmentation techniques, but there are still certain limitations when dealing with high-density overlapping objects. The YOLO series of models are also widely used in this field. [PERSON] et al. [28] introduced the C3 FS module and CBAM based on YOLOv5s and designed the CMPDIOU loss function, which achieved a lightweight model while improving centipede detection and counting accuracy. [PERSON] et al. [29] introduced PConv and CA based on the improved YOLOv7 model for the rapid detection and counting of pigs. [PERSON] et al. [30] proposed the PDC-YOLO model based on YOLOv7, combined this with the SPD-Conv structure and AFPN module, optimized the small-scale object detection and multi-scale feature fusion capabilities, and achieved efficient pig counting in complex environments.
# Fast Type-II Hartley Transform Algorithms for Short-Length Input Sequences
## 1 Introduction
### Related Papers
In [27], a split-radix DIT algorithm for the fast calculation of the DHT-II was proposed. The splitting of DHT-II of length N into one DHT-II of length N/2 was implemented for even-indexed samples. The splitting of DHT-II of length N into two DHTs-II of length N/4 was applied for odd-indexed samples. The proposed algorithm considerably reduced the arithmetic complexity compared to the existing DHT-II algorithms. In particular, the number of multiplications could be decreased by 29% compared to the radix-2 DHT-II algorithm [26], and the number of additions was reduced by 10%. In [28], a modular VLSI algorithm for the fast calculation of DHT-II was introduced. The author efficiently incorporated the obfuscation technique with low overheads, which presented the additional advantage of hardware security. The proposed VLSI algorithm applied modular and regular computational structures named quasi-band correlations. In addition, the use of the systolic arrays architectural paradigm allowed for the efficient implementation of this algorithm on a VLSI chip. The aforementioned split-radix algorithms were focused on input sequences, with length \(N\) being the power of two. Fast algorithms for calculating DHT-II with length \(N\) being the power of three were also developed. For example, in [29], a fast radix-3 algorithm for the computation of the DHT-II was developed for input sequences of length \(N\), where \(N=3^{m}\), \(m\geq 2\). Then, this algorithm was applied to the direct computation of DHT-II when three adjacent sequences of length \(N/3\) DHT-II were given. The arithmetic operations could be saved by up to 29%. Furthermore, the new algorithm could be easily implemented in VLSI.
# Experimental Test and Prediction Model of Soil Thermal Conductivity in Pernafrost Regions
## 5 Thermal Conductivity Prediction for Three Soil Clusters
### Thermal Conductivity Prediction Models
#### 5.1.3 Ternary Fitting for Frozen Soil  
The soil distribution in the QTEC is diverse and can be divided into different types according to geotechnical specifications. However, the information of mineral composition and particle size distribution cannot be fully specified by the geotechnical engineering classification method. The dry density and water content are not the exclusive influence factors for soil thermal conductivity. Hence, this will lead to certain deviation for predicting the thermal conductivity of frozen soil only using the above two indicators.  
Furthermore, compared to the frozen soil sample, the unfrozen soil sample is relatively easier to prepare and test, and its thermal conductivity measurement result naturally implies the information of specimen's mineral composition and particle size distribution. The correlation analysis proves strong positive correlations between frozen and unfrozen thermal conductivity either. So the ternary fitting formula, using dry density, water content and an unfrozen specimen's thermal conductivity as fitting parameters, of frozen sample's thermal conductivity has been developed in this work:  
\[\lambda_{f}=a_{2}\lambda_{u}+b_{2}+c_{2}\ln(w)+d_{2}\ln(p_{d}) \tag{3}\]  
where \(a_{2},b_{2},c_{2}\) and \(d_{2}\) are the fitting coefficients of the formula (shown in Table 5).
## 2 Fast Finite Element Simulation Method for PAUT
### Fast Finite Element Simulation Method
All numerical algorithms must have more than second-order accuracy in order to meet the needs of practical engineering applications [29]. Therefore, in order to verify that the calculation accuracy of the improved explicit integration algorithm can meet the actual needs, we used the single free system as an example to perform a preliminary analysis of the accuracy and stability of this method. Firstly, it is assumed that a single degree of freedom system satisfies the following dynamic equation:  
\[\ddot{x}(t)+2\xi\omega\dot{x}(t)+\omega^{2}x(t)=f(t) \tag{5}\]  
According to Equation (4), the following relationship is obtained in the case of a single degree of freedom:  
\[\dot{x}_{t+\Delta t}=-2\Delta t\omega^{2}x_{t}-4\Delta t\xi\omega\dot{x}_{t}+ \dot{x}_{t-\Delta t}+2\Delta tf_{t}+o_{1} \tag{6}\]  
\[x_{t+\Delta t}=x_{t}+\frac{\Delta t}{2}(\dot{x}_{t+\Delta t}+\dot{x}_{t})+o_{2} \tag{7}\]  
where \(o_{1}\) and \(o_{2}\) represent the calculation errors of velocity and displacement introduced by the difference schemes, respectively. According to the exact expression of the central difference method and Taylor expansion,  
\[\frac{\dot{x}_{t+\Delta t}-\dot{x}_{t-\Delta t}}{2\Delta t}=\ddot{x}_{t}+O \Big{(}\Delta t^{3}\Big{)} \tag{8}\]  
\[x_{t+\Delta t}=x_{t}+\Delta t\dot{x}_{t}+\frac{\Delta t}{2}\ddot{x}_{t}+O \Big{(}\Delta t^{3}\Big{)} \tag{9}\]  
Substitute Equation (8) into Equation (6) to obtain \(o_{1}\)\(=\) O\(\left(\Delta t^{4}\right)\), and substitute Equation (9) into Equation (7) to obtain \(o_{2}\)\(=\) O\(\left(\Delta t^{3}\right)\). Therefore, the calculation cut-off error of Equation (5) is between O\(\left(\Delta t^{3}\right)\) and O\(\left(\Delta t^{4}\right)\) in the case of a single degree of freedom. Through the above theoretical analysis, it can be seen that the calculation accuracy of the improved time-domain explicit integration algorithm is between the second-order and the third-order, and may even reach third-order calculation accuracy. It can be seen that, compared with the traditional explicit integration method ([PERSON]'s method), this method not only needs less storage and calculations, but also improves the calculation accuracy, to a certain extent. Moreover, this paper combines the non-zero element one-dimensional compressed storage method with the new explicit integration algorithm to minimize the amount of calculations and storage, because the coefficient matrixes \(\left[\widetilde{\mathbb{K}}\right]\) and \(\left[\widetilde{\mathbb{C}}\right]\) in Equation (4) are sparse matrixes. Most of the elements in the matrixes are zero elements. As shown in Figure 1, only the non-zero elements in the coefficient matrix are stored, and their corresponding row and column numbers are recorded. Compared with the traditional half bandwidth storage method, the storage capacity of this method is further reduced. At the same time, when solving Equation (4), only non-zero elements need to be calculated. Therefore, the number of calculations is also greatly reduced.