esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Fracture Mechanism and Stability of Slope with Tensile Cracks
## 2 Formation Mechanism and Value of Vertical Crack
### Maximum Value of Tensile Crack  
The formation of tensile cracks in slope is extremely complicated, tension is only one of the possible causes of cracks, since there is experimental evidence about cracks caused and deepened by other process, such as cycles of drying and wetting, desiccation, and weathering [15]. Thus, the methods for determining the depth and location of tensile cracks still need further study. A number of researchers have studied the maximum value of crack to evaluate the stability of slope. [PERSON] [9] derived a formula of crack depth of vertical slope, and it can be expressed as,  
\[\delta=\frac{1.34c}{\gamma\sqrt{K_{a}}} \tag{3}\]  
[PERSON] [10] also believed that the crack depth should not exceed half of the slope height. [PERSON] [16] considered the crack formation as a part of the failure mechanism by calculating the internal energy dissipation along the crack, and proposed that in the absence of pore-water pressure, the crack depth can be expressed as,  
\[\delta=\frac{3.83c}{\gamma\sqrt{K_{a}}} \tag{4}\]  
[PERSON] [31] discussed the effect of Poisson's ratio \(\mu\) on soil, and proposed that the crack depth can be expressed as,  
\[\delta=\frac{2c\sqrt{K_{a}}}{\gamma\left[K_{a}-\left(\frac{\mu}{1-\mu}\right) \right]} \tag{5}\]  
These crack formulas are obtained by different methods. However, determining the crack depth remains a challenge to analyze the stability of slope. Up to now, there has been no report about the unified standard of the crack depth or position. Most of the researchers considered the preexisting cracks; that is, the depth of crack is assumed. In general, when a crack is introduced, the crack should not extend beyond the depth of tension. If the crack depth is overestimated, compressive forces will be eliminated, and the safety factor will be overestimated [32; 33; 34; 35; 36]. [PERSON] proposed that using the static theorem of limit analysis, one can easily prove that the depth calculated from Equation (2) is a rigorous lower bound on the maximum depth of a thin crack in a half-space built of the material with strength governed by the classical Mohr-Coulomb yield condition [16]. However, the crack depth in Equation (4) is the upper bound on the true maximum depth of a stable crack, and a vertical crack with a depth larger than that value will be unstable.  
Since there is no uniform standard for equation of the tensile crack, a dimensionless crack coefficient \(\lambda\) was introduced to better evaluate the influence of crack value on slope stability in this study. Based on results researched by [PERSON], the upper limit \(\lambda\) is set to 2 [16]. It is given by  
\[\delta^{\prime}=\lambda\delta \tag{6}\]  
where \(\delta\) is crack depth, \(\delta^{\prime}\) is calculated crack depth, and \(\lambda\) is the crack depth coefficient and its value range is \(0<\lambda<2\).
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 3 Methods
### Validation Techniques
#### 3.5.1 Qualitative Metrics  
Visual inspection is the most common evaluation technique used to infer the registration performance. In OCT imaging, a certain smoothness should be expected within the retinal structure, mainly between adjacent A-scans. If an abrupt discontinuity between pixels is observed, one may argue that the registration is not performing well.
# Extended-Reach Drilling (ERD)--The Main Problems and Current Achievements
## 4 Analysis of the Presented Case History Data
### Used Directional Drilling Tools  
Table 2 shows that rotary steerable systems (RSSs) were used to drill most of the wells. One well was drilled with a steerable motor and one with a kick sub on mud motor, while for the other wells, there was no information for the drilling assembly. When drilling ERD wells, rotary steerable systems enable the continuous rotation of the drill string from the surface at higher speeds. Different technologies have been used to direct the path into the desired direction: 'push-the-bit', 'point the bit' or their combination (in hybrid RSS). The advantages of RSS technology are: (1) an improved transportation of drilled cuttings to the surface and therefore very efficient hole cleaning and a better hydraulic performance, (2) a reduced torque and drag, (3) an improved rate of penetration (ROP) resulting in a reduced drilling time, (4) a reduced tortuosity of the well and smoother walls of the well, which enables an easier installation of the casing or production strings and better measurements of the formation properties, (5) fewer trips are required because a fixed cutter bit is used and drilling different sections often does not require a change in the BHA design and (6) it is more environmental friendly [12; 75].
# Acoustic-Based Engine Fault Diagnosis Using WPT, PCA and Bayesian Optimization
## 2 Experimental Work
### Basic Signal Analysis  
Time-domain data were analyzed for all the cases to understand the characteristics of the signals. The time-domain signals of the engine misfire case are shown in Figure 2a, which shows no conceivable difference between healthy and fault signals. The spectrogram is presented in Figure 2b, which reveals that differentiation between the healthy and the faulty engine is more discernable. The variation can  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Type of Engine Data**} & \multicolumn{1}{c}{**Engine Speed [rpm]**} & \multicolumn{1}{c}{**Load Torque [Nm]**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Damage (Main Mode \& Symptoms)** \\ \end{tabular} } \\ \hline \multirow{3}{*}{Engine Misfire} & Healthy & 925 (Idle) & 0 & - \\  & & 1750 & 0.15 & \\  & Faulty & 925 (Idle) & 0 & \\  & & 1750 & 0.15 & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Ignition Timing \\ Variation \\ \end{tabular} } & Healthy & 2250 & 0.5 & Ignition timing at 20.4\({}^{\circ}\) \\  & Advance & 2250 & 0.5 & Ignition timing at 5.4\({}^{\circ}\) \\  & Retard & 2250 & 0.5 & Ignition timing at 35.4\({}^{\circ}\) \\ \hline \multirow{3}{*}{AFR Variation} & Healthy & 3000 & 0.5 & AFR at 14.45 \\  & Lean & 3000 & 0.5 & AFR at 10.10 \\ \cline{1-1}  & Rich & 3000 & 0.5 & AFR at 17.20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fault seeding and operating conditions.  
Figure 1: (**a**) Schematic of the set-up; and (**b**) the instrumented engine.  
be perceived in every third firing order since the third cylinder was cut off. However, in the case of more complex faults such as ignition timing variation, as shown in Figure 3, the difference becomes more inconceivable. Based on the spectrogram analysis, it can be seen that complex faults require more advanced investigations rather than basic time-frequency domain analysis. Thus, further analyses are presented in the following section for improved fault detection and classification.
## 4 Measurement Procedure  
The fabricated chips were mounted on 28-pin dual-in-line packages (DIPs). Wire bonding using aluminum wires was utilized for the electrical connections. The circuit diagram for tensile testing is shown in Figure 4. A multi-axis capacitive accelerometer interface integrated circuit (IC) (AT-1006, ACT-LSI, Atsugi, Japan) was used for capacitance detection at the differential capacitance of the displacement sensor. The sensitivity of the IC was 150 V/pF at its maximum. The actuation voltage was applied with consideration of the bias voltage for the capacitance detection. The moving part was biased at 2.5 V by the capacitance amplifier.  
The tensile testing was conducted first. The voltage at the parallel plate actuator was slowly increased from 2.5 V until the output of the displacement sensor was jumped up and saturated. The saturation indicated a pull-in of the capacitances at the displacement sensor, which was caused by the sudden fracture of the specimens. After checking the specimen's fracture, the obtained  
Figure 4: Tensile test circuit setup. The common input of interface integrated circuit (IC) is biased at 2.5 V, which is half of the power supply. The actuation voltage has an offset of 2.5 V.  
Figure 3: SiNW fabrication process. For further thinning, steps (**e.f**) are repeated twice.  
voltage-displacement curves were converted to stress-strain curves with the following calibration steps. The calibration was undertaken for each specimen because of the fabrication variation that has been described below.  
Firstly, the displacement sensor was calibrated. The device was actuated again to measure the voltage-capacitance relationships. The displacement sensitivity was calculated and the voltage-displacement relationship was calculated with the observed maximum displacement, using a digital microscope. The force that was applied on the specimen was calculated from the finite element analysis that considered the shrinkage of the dimensions, which was caused by the oxidation thinning process. The change in dimensions (width) was measured directly with a scanning electron microscope. Finally, the tapered part at both ends of the specimen was taken into account in the measured displacement. The tensile force-displacement curve was thus obtained. The tensile stress was calculated from the measured width and thickness by assuming that the cross section was ellipsoidal. The tensile strain was calculated by dividing the elongation with the initial length of the test part.
## 4 Results and Discussion
### Comparison
#### 4.6.3 Comparison of Work Productivity Achieved on Various Construction Sites
Due to the complexity of the given structure, this value is 10% lower than the value stated in the database. The labor productivity of individual monolithic structure processes was met with a deviance of up to 10% (note: compare Figures 13 and 14), which meant that no fundamental personnel measures were necessary. The analysis of the process of bricked structures recorded a performance of 1.61 m\({}^{3}\)/shift/worker, which represents a decrease compared to the values complying with the database. Based on the publications by [PERSON] and [PERSON]. [77], the recorded time norm at the apartment project construction site in Dhule was 0.73 Nh/m\({}^{2}\), which is a performance that is 6% better than the values achieved at the Klementova Street construction site. For bricked structures, an important role is played by the format size of the given masonry material. The labor productivity of the plastering works is explored using various statistical methods. Gypsum wall plasters implemented as a part of the KRG construction projects were assessed using the ANOVA test. In accordance with [PERSON] et al., the given statistical analyses demonstrated performances of 9.2 m\({}^{2}\)/shift, which is a comparable value to the labor productivity of the structure assessed by us, for which the achieved performance value, after considering the conducted patching on a part of the ceiling structure, was also approximately 9.0 m\({}^{2}\)/shift. A comparable performance of 9.04 m\({}^{2}\)/shift was achieved for plastering the exterior bricked walls based on the study by [PERSON]. Labor productivity of the tiling works was the subject of the publications by [PERSON] et al., in which a productivity of 1.156 Nh/m\({}^{2}\) was determined for the floor tiling work of the shopping center based on the given statistical analyses. It is a comparable performance when compared with the values pursuant to the CONTECT database (1.26 Nh/m\({}^{2}\)), the productivity of which is the empiric average of the ratio of the implemented floor and wall tiles contrary to the values determined by [PERSON], where only the productivity of the implementation of floor tiles with a lower labor intensity was explored. The labor productivity of the facade works at the Klementova Street construction site is 25% lower than the planned values pursuant to the database. This is caused by a lack of qualified labor and a high labor intensity of the given construction process.
## 4 Results and Discussion
### Impact on the Risk-Based Maintenance Program at the Utilization Phase
However, utilizing these data inputs for training and reinforcing planning algorithms (diagnostic, predictive, or prescriptive) has created a stronger need and added value to collect these data. Several case studies have highlighted the incomplete data collection that maintenance reports face, e.g., missing failure mode, failure cause, starting date, completion date, and no categorization (running into categories such as unknown or others). In addition, maintenance data are not synchronized with operating data or maintenance logs, for example,  
Figure 5: Management framework of risk-based maintenance based on NORSOK Z-008. if an abnormal condition is triggered but the maintenance log is registered a day later. The desired manner is to have synchronization or at least traceability between the health conditions measured by the condition monitoring system and the operating conditions or process parameters collected by SCADA, and both shall be traceable to any associated logs or notifications (which are used as labels in supervised machine learning training). Some case studies indicated that more censored data (data for items that have not yet failed, i.e., potential failure) have increased, as these potential failures are fixed before they reach the functional failure stage. This could generate two categories of datasets (potential failures and functional failures) and requires awareness in the analysis when the mean time to failure is extracted.
# Effects of Polypropylene Fiber on the Liquefaction Resistance of Saturated Sand in Ring Shear Tests
## 3 Results
### Shear-Torque-Controlled (STC) Tests
In addition to the increase in effective contact area between fiber and sand grains, the fiber surface roughness also becomes stronger with greater sand density. The roughness of the fiber surface plays a vital role in fiber sliding resistance [39]. Thus, the shear stress of reinforced sand increased with the increase in sand density and more energy was spent to overcome the resistance. The shear energy until peak shear stress (\(W_{\rm peak}\)) and total shear energy until liquefaction (\(W_{\rm total}\)) also increased with the fiber content. It can be seen from Figure 6b and Table 2 that the shear energy in both unreinforced and reinforced specimens mainly dissipated after reaching the peak value. The dissipated total shear energy \(W_{\rm total}\) in reinforced specimens were approximately 17,757 J/m\({}^{2}\), 253,479 J/m\({}^{2}\) and 325,217 J/m\({}^{2}\) when the fiber contents were 0.2%, 0.4% and 0.6%, respectively. These energies were 3.2, 45.1 and 57.9 times that of the unreinforced specimens, respectively.
## 4 Approaches of Optimizing Geolocation Measurements
### Artificial Intelligence (AI)
#### 4.5.2 Dempster-Shafer (D-S)  
[PERSON]'s work on the theory of probabilities with upper and lower limits gave rise to the Dempster-Shafer (D-S) theory of proof. It has since been expanded by a number of researchers and popularised in the literature on AI and expert systems, but only to a limited extent, as a technique for modelling reasoning under uncertainty. In this regard, it has a number of advantages over more traditional statistical and Bayesian decision-making methods. [PERSON] remarked that actual, functional applications of D-S methods have been uncommon, but subsequent to these remarks, there has been a marked increase in the applications integrating the use of D-S. Although D-S is not widely used, it has been successfully applied to target identification and target tracking [93].  
In [96], the authors have made a comparison between D-S and Bayes theories. They have derived the KF from the basic Bayesian fusion equation using a new method to estimate the Chapman-Kolmogorov prediction integral. Eventually, they concluded that the D-S theory is better than Bayes theory.  
A multi LEO satellite was considered to geolocate and track the UAV using D-S to optimize high non-linear filtering. Various simulation scenarios were implemented with Continuous-Discrete Gauss Hermite \(H_{\infty}\) filtering (C-DGH\(H_{\infty}\)F) in the presence of uncertainty, as shown in Figure 18.
## 2 Methods
\begin{array}{l}\theta_{1}=\frac{1}{L-1}\times\frac{1}{3}\times\sum_{n=1}^{3 }g_{n}(S,1),\\ \theta_{2}=\frac{1}{L-2}\times\frac{1}{3}\times\sum_{n=1}^{3}g_{n}(S,2),\\ \ldots\\ \theta_{\lambda}=\frac{1}{L-\lambda}\times\frac{1}{3}\times\sum_{n=1}^{3}g_{n} (S,\lambda).\end{array} \qquad\qquad(\lambda<L) \tag{6}\]  
where \(\theta_{k}(k=1,2,\ldots,\lambda)\) is called the \(k\)-th tier correlation factor, \(g_{n}(S,k)\) represents the coupling mode function as given by  
\[g_{n}(S,k)=\sqrt{\sum_{i=1}^{L-k}\left(P_{n}(S_{i})-P_{n}(S_{i+k})\right)^{2}} \tag{7}\]  
Factor \(\theta_{1}\) reflects the coupling mode between the most contiguous elements along a six-letter sequence (Figure 1a); \(\theta_{2}\) reflects the coupling mode between the second-most contiguous (Figure 1b); \(\theta_{3}\) reflects the coupling mode between the third-most contiguous (Figure 1c), and so on. \(\lambda\) is the highest rank of the coupling mode. Consequently, a protein sequence can be characterized by a \((21+\lambda)\) dimensional vector \(V\):  
\[V=(v_{1},v_{2},\ldots,v_{20},v_{20+1},\ldots,v_{20+\lambda},v_{20+\lambda+1}), \tag{8}\]  
where  
\[v_{i}= \begin{cases}f_{i}&1\leq i\leq 20\\ w_{1}\theta_{i-20}&20+1\leq i\leq 20+\lambda\\ w_{2}\lambda^{\prime}&i=20+\lambda+1\end{cases}  \tag{9}\]  
Here \(w_{1}\) and \(w_{2}\) are weight factors. It is easy to see that the first 20 components reflect the effect of the amino acid composition, whereas the last \(\lambda+1\) components reflect the effect of sequence order and property of the residues. For convenience, a set of such \(21+\lambda\) components as formulated by Equations (8) and (9) is called the generalized pseudoamino acid composition of a protein sequence, and denoted by G-PseAAC.
# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 3 Materials and Methods
### Positioning Module of RHLS
#### 3.4.3 Output Layer  
The output layer of the recurrent network positioning module is responsible for the final metric calculation. Each element of feature vector \(h_{t}\) at time \(t\) generated through the middle layer has a value between \(-1\) and \(1\). This is passed through another sigmoid layer to process the vector once more. This is expressed as follows:  
\[y_{t}=\sigma(W_{1}\cdot h_{t}+b_{1}). \tag{16}\]  
Figure 7: Structure of the middle layer.  
As a result of the final regression through the above processes, \(y_{t}\) becomes a two-dimensional vector in which each element value is scaled between zero and one. The actual coordinate value is calculated by increasing this to the original map scale as shown in Figure 8. For example, if the area where the signal is collected is an area of 10 m in width and 5 m in length, the output horizontal value is 10 times, and the vertical value is five times, which are converted into coordinates in meters.
## 3 Results
### M. japonicus Mj-HSP60 Expression Changes after DEHP Exposure  
To confirm the effects of DEHP exposure on _Mj-HSP60_ expression, we conducted quantitative RT-PCR analysis using mRNA acquired from the gill and hepatopancreas samples after exposure to DEHP for 1, 4, and 7 days. _Mj-HSP60_ was expressed approximately 8.2-fold higher after exposure to 1 \(\mu\)g L\({}^{-1}\) DEHP (\(P<0.01\)), 3.2-fold higher for 10 \(\mu\)g L\({}^{-1}\) (\(P<0.05\)), and 9.4-fold higher for 30 \(\mu\)g L\({}^{-1}\) (\(P<0.01\)) in the gill tissue on day 1 (Figure 4A). With the passage of time, expression levels gradually decreased in all DEHP concentration groups. By day 4, for the 10 and 30 \(\mu\)g L\({}^{-1}\) treatment groups, expression levels were restored to control levels. By day 7, _Mj-HSP60_ expression levels were lower than those of the control. In particular, sharp decreases in expression levels were found in 10 \(\mu\)g L\({}^{-1}\) (0.3-fold) and 30 \(\mu\)g L\({}^{-1}\) (0.21-fold) (\(P<0.05\)) groups. In the hepatopancreatic tissue, expression levels of _Mj-HSP60_ exhibited an overall increased pattern compared to the expression levels in the controls on day 1 (Figure 4B). Expression levels significantly increased by 2.4-fold for 1 \(\mu\)g L\({}^{-1}\), 2.6-fold for 10 \(\mu\)g L\({}^{-1}\), and 2.9-fold for 30 \(\mu\)g L\({}^{-1}\) DEHP (\(P<0.05\)). By days 4 and 7, _Mj-HSP60_ expression levels returned to control levels for the 1 \(\mu\)g L\({}^{-1}\) group. In the 10 \(\mu\)g L\({}^{-1}\) DEHP group, _Mj-HSP60_ expression decreased to \(<\)0.5-fold on day 4, and then recovered slightly toward that of control levels by day 7.
# Design, Fabrication, and Control Algorithm of Self-Reconfigurable Modular Intelligent Vehicles
## 5 Experimental Validation
### Results and Discussion  
Table 2 gives the experimental results of the reconfiguration of the proposed intelligent modular vehicle group with one central control vehicle and three submodular vehicles.  
In Table 2, reconfiguration success is defined as: when each submodule car correctly enables the magnetic connection mechanism in the corresponding orientation, the car itself will not have a large deviation. Average time is calculated as the average time for all successful reconfiguration experiments. All experiments start from the disorderly dispersed state of the submodule vehicles and end with the completion of the overall configuration.  
The \" \(\mid\) \" shape configuration is relatively easy, the success rate is high, and the average time is short. The \" -- \" shape configuration takes a longer time because of the lateral translation of the Mecanum wheel. The \" L \" shape configuration is more complicated, and the fusion of the \" \(\mid\) \" shape configuration and the \" - \" shape configuration needs to be considered in the reconfiguration process.  
Table 3 gives the experimental results of the operation time of the proposed intelligent modular vehicle group with one central control vehicle and three submodular vehicles. Only communication means the whole vehicle group is working in a preset route but not reconfiguration. Keep reconfiguration means the whole group keeps reconfiguring from one shape to another. Heavy load means a 3 kg weight, which is almost five times the weight of the car, is attached to each submodular vehicle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \" = \" Shape & \" \(\mid\) \" Shape & \" L \" Shape \\ \hline Reconfiguration times & 20 & 20 & 30 \\ Success rate & 95\% (19/20) & 100\% (20/20) & 93\% (28/30) \\ Average time & 1 min 10 s. & 1 min 8 s. & 1 min 31 s. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reconfiguration data.  
Figure 24: \" \(\mid\) \" shape reconfiguration procedure.  
Modular robots are generally limited in working time due to volume constraints. The modular vehicle design in this paper intends to maximize the power density of each submodule. It is proved by experiments that the vehicle system has longer working endurance.  
The system proposed in this paper includes a homogeneous submodule car and a heterogeneous host car with submodule cars. This ensures that the main vehicle can complete precise control, while the subvehicle can have sufficient redundancy when completing various tasks. From handling heavy objects to refined distribution, modular cars can be used in different scenarios and have good scalability.  
A comparison between different modular cart systems is presented in Table 4.