esa-sceva/satcom-chunk-collection huggingface dataset:
# Extended-Reach Drilling (ERD)--The Main Problems and Current Achievements
## 5 Conclusions  
From the first direction well until today, the oil and gas industry has made significant progress in directional drilling techniques and technology. From the first, very modest achievements conditioned by the technology of the time, today, operators and contractors are able to successfully complete the complex design of ultra-extended-reach wells with a measured depth of over 15,240 m (50,000 ft). The drilling of the horizontal and extended-reach well can be observed from economic and ecological points of view. Increasing the contact area between the wellbore and the reservoir also increases the capabilities of the well to produce more hydrocarbons or geothermal water. This means that, at the same time, there is the possibility to cover a large area of the reservoir from one surface location, contributing to a small environmental impact.  
According to data presented in the text, it can be concluded that the present directional drilling technique and technology enable drilling directional or extended-reach wells regardless of their directional difficulty index. Recent research in this area has tried to catch up with solutions for the two-way transfer of a large amount of data between the downhole equipment and surface and solve problems related to high values of torque and drag. The solution for the problem related to high values of torque and drag must be economically and environmentally justified in order to replace often-used oil-based mud.  
Finally, the further shifting of borders is possible, but there is a question related to the expediency of those projects.  
**Author Contributions:** Conceptualization, K.E.S. and B.P.; methodology, K.E.S. and B.P.; validation, N.G.-M.; formal analysis, P.M. and I.M.; investigation, K.E.S.; data curation, N.G.-M. and B.P.; writing--original draft preparation, K.E.S., N.G.-M., P.M., I.M. and B.P.; writing--review and editing, N.G.-M., P.M. and I.M.; supervision, N.G.-M. and B.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available in a publicly accessible repository.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experimental Results
### SD-OCT Imaging Performance  
SD-OCT images of non-biological samples and biological samples were obtained to verify the differences in the images resulting from improved imaging lens characteristics. As a non-biological sample, OCT images of tape with multiple layers were acquired. Two OCT images are displayed in Figure 11. Each OCT image is composed of 1024 (in the axial direction) by 500 (in the lateral direction, 2.55 mm) pixels. By comparing the images in Figure 11a,b, it can be seen that the imaging depth of the OCT image acquired with the S-TFL was deeper than that of S-ADL, as confirmed by the roll-off characteristic. Three horizontal guidelines are drawn in Figure 11 for easy comparison of the image depths. At the third line from the top, the boundary of the tape is more clearly distinguished in Figure 11a compared to Figure 11b. For a more detailed comparison, depth profiles were extracted for the same A-line of each image. The depth profile for the S-TFL has a greater amplitude than the S-ADL. Even at deep depths, this feature was well preserved, as shown in Figure 11c. We already compared the curve of the MTF for each imaging lens after we obtained the simulation results. The MTF value is related to the amount of light that can be collected through the lens. Although the OCT image was obtained under the same conditions, Figure 11b shows a brighter image and improved contrast at a deep depth than Figure 11a. This result reflects the characteristics of the imaging lens, therefore, TFL which has a higher MTF value than the ADL produces better image contrast and depth. This characteristic was also confirmed when a biological sample was used.  
Differences in the brightness and the imaging depth were also distinguished in the OCT image of a human palm. The OCT image shown in Figure 12a was obtained with the S-ADL and has low contrast, while the image in Figure 12b obtained with the S-TFL shows a well-defined boundary inside  
Figure 11: OCT image of a tape obtained using (**a**) S-ADL and (**b**) S-TFL. (**c**) Depth profiles at the same A-line of each image.  
the tissue, even at a deep depth. As in Figure 11a,b, three guidelines are drawn in the horizontal direction. Looking at the second guideline from the top, the image obtained by the S-ADL was not distinguishable, and as the depth increased, the interface inside the tissue was not visible. However, at the same depth, we can see that the interface is more distinct in Figure 12b than in Figure 12a. This difference can be explained more clearly through a specific A-line profile. When looking at depth profiles extracted from the same location in each image, the depth profile obtained for the S-TFL maintained a higher amplitude along the depth than that for the S-ADL.
# A New Period-Sequential Index Forecasting Algorithm for Time Series Data
## 1 Introduction  
In the big data era, a large number of time series data are continuously generated in the network systems, such as stock price, sales volume, production capacity, weather data, ocean engineering, engineering control, and largely in any system of applied science and engineering which involves investigations of time-varying parameters [1; 2; 3]. In general, the distribution of time series data changes over time, and is non-stationary [4; 5], while some data shows potential periodicity characteristics. Since the 1950s, time series forecasting has received much interest in prediction science.  
Continuously growing numbers of new algorithms have been proposed and studied on time series forecasting. Firstly, the exponential smoothing (ES) method [5; 6] and the moving average (MA) method [7] are simple and widely used, and performed well in forecasting competitions against more sophisticated approaches. Secondly, the autoregressive integrated moving average (ARIMA) model integrates autoregressive models (AR) and moving average models (MA), and is widely used as a linear time series forecasting method [8; 9]. The ARIMA model gives good accuracy in forecasting relatively stationary time series data, but needs a strong assumption that the values of future data are linearly dependent on the values of historical data [10]. Thirdly, the artificial neural network (ANN) [11; 12; 13] and adaptive models [14] have also been used to forecast nonlinear time series data, and improve forecasting accuracy in different time scales. It is also possible to hybrid different methods to improve overall forecasting accuracy [15].  
However, no traditional forecasting methods can meet all the targets [16; 17; 18], while applying heuristic methods are also worth researching [19]. Here, a period-sequential index algorithm with a sigma-pi neural network (SPNN-PSI) is proposed and dedicated to the prediction of time series data. Here a period-sequential index algorithm (PSI) by identifying structures from transformed data, where there are four indexes carrying implicitly structure information usable for forecasting, the period index, sequential index, small period index, and super sequential index, is combined with a sigma-pi neural network algorithm (SPNN) improving the accuracy and robustness of forecasting algorithm. The SPNN-PSI method has a universal application, and a satisfactory prediction quality improved as the correlation coefficient value of the reference historical datasets increased.
## 3. DGSVNNWBM Operator and DGSVNNWGBM Operator  
This section extends DGWBM and DGWGBM to fuse the SVNNs, and proposes the dual generalized SVNN weighted BM (DGSVNNWBM) operator and dual generalized SVNN weighted GBM (DGSVNNWGBM) operator. **Definition 6**. : _Let \(a_{i}=(T_{i},I_{i},F_{i})\)\((i=1,2,\ldots,n)\) be a set of SVNNs with weight \(w_{i}=(w_{1},w_{2},\ldots,w_{n})^{T}\), \(w_{i}\in[0,1]\) and \(\sum_{i=1}^{n}w_{i}=1\)._  
_Thereafter, the dual generalized SVNN weighted BM (DGSVNNWBM) operator is defined as_  
\[\text{DGSVNNWBM}^{R}_{w}(a_{1},a_{2},\cdots,a_{n})=\binom{n}{i_{1}i_{2},\ldots, i_{n}=1}\binom{n}{\otimes\limits_{j=1}^{n}w_{j_{i}}a_{j_{j}}^{r_{j}}} \Bigg{)}^{1/\sum_{i=1}^{n}r_{j}}, \tag{10}\]  
_where \(R=(r_{1},r_{2},\ldots,r_{n})^{T}\) is the parameter vector with \(r_{i}\geq 0\)\((i=1,2,\ldots,n)\)._  
Proof. : We can prove that \(R=(r_{1},r_{2},\ldots,r_{n})^{T}\) is the parameter vector with \(r_{i}\geq 0\)\((i=1,2,\ldots,n)\). We can get Theorem 1. **Theorem 1**. : _Let \(a_{i}=(T_{i},I_{i},F_{i})\)\((i=1,2,\ldots,n)\) be a set of SVNNs. Hence, the aggregated result of DGSVNNWBM is a SVNN and_  
\[\begin{split}&\text{DGSVNNWBM}_{w}^{R}(a_{1},a_{2},\cdots,a_{n}) \\ &=\left(\begin{array}{l}\left(1-\prod\limits_{i_{1},i_{2},\ldots, i_{  
Thereafter,  
\[0 \leq\left(1-\prod_{i_{1},i_{2},\ldots,i_{n}=1}^{n}\left(1-\prod_{j=1} ^{n}\left(1-\left(1-T_{i_{j}}^{r_{j}}\right)^{w_{i_{j}}}\right)\right)\right)^{1 /\sum_{i=1}^{n}r_{j}}\leq 1, \tag{17}\] \[0 \leq 1-\left(1-\prod_{i_{1},i_{2},\ldots,i_{n}=1}^{n}\left(1-\prod _{j=1}^{n}\left(1-\left(1-\left(1-T_{i_{j}}\right)^{r_{j}}\right)^{w_{i_{j}}} \right)\right)\right)^{1/\sum_{i=1}^{n}r_{j}}\leq 1.\]  
In addition,  
\[0 \leq\left(1-\prod_{i_{1},i_{2},\ldots,i_{n}=1}^{n}\left(1-\prod_{j =1}^{n}\left(1-\left(1-T_{i_{j}}^{r_{j}}\right)^{w_{i_{j}}}\right)\right) \right)^{1/\sum_{i=1}^{n}r_{j}} \tag{18}\] \[+1-\left(1-\prod_{i_{1},i_{2},\ldots,i_{n}=1}^{n}\left(1-\prod_{ j=1}^{n}\left(1-\left(1-\left(1-T_{i_{j}}\right)^{r_{j}}\right)^{w_{i_{j}}} \right)\right)\right)^{1/\sum_{i=1}^{n}r_{j}}\leq 3,\]  
thereby completing the proof. \(\square\)  
Moreover, DCSVNNWBM has the following properties. **Property 1. (Monotonicity). **_Let \(a_{i}=(T_{a_{i}},I_{a_{i}},F_{a_{i}})(i=1,2,\ldots,n)\) and \(b_{i}=(T_{b_{i}},I_{b_{i}},F_{b_{i}})\)\((i=1,2,\ldots,n)\) be two sets of SVNNs. If \(T_{a_{i}}\leq T_{b_{i}}\) and \(I_{a_{i}}\geq I_{b_{i}}\) and \(F_{a_{i}}\geq F_{b_{i}}\) holds for all \(i\), then_  
\[\text{DGSVNNWBM}_{w}^{R}(a_{1},a_{2},\cdots,a_{n})\leq\text{DGSVNNWBM}_{w}^{R }(b_{1},b_{2},\cdots,b_{n}). \tag{19}\]  
**Proof. ** Let \(\text{DGSVNNWBM}_{w}^{R}(a_{1},a_{2},\cdots,a_{n})=(T_{a},I_{a},F_{a})\), \(\text{DGSVNNWBM}_{w}^{R}(b_{1},b_{2},\cdots,b_{n})=(T_{b},I_{b_{i}},F_{b})\).
## 2 Materials and Methods
### Approach for Estimations
#### 2.5.1 Positioning Domain  
We applied different models' corrections for pseudo-range observables and then compared 3D positioning errors for these solutions.  
As a true position, we used precise coordinates obtained through SOPAC SCOUT ([[http://sopac-old.ucsd.edu/scout.shtml](http://sopac-old.ucsd.edu/scout.shtml)]([http://sopac-old.ucsd.edu/scout.shtml](http://sopac-old.ucsd.edu/scout.shtml)) accessed on 11 April 2023). To check the SOPAC SCOUT results we compared it with our median values of \(X\), \(Y\), \(Z\). As positioning error, we consider the three-dimensional error--root-mean-square deviation from the true position \(X_{0}\), \(Y_{0}\), and \(Z_{0}\) as follows:  
\[\sigma=\sqrt{\Delta X^{2}+\Delta Y^{2}+\Delta Z^{2}}, \tag{1}\]  
where \(\Delta X=X-X_{0}\), \(\Delta Y=Y-Y_{0}\), \(\Delta Z=Z-Z_{0}\).  
To avoid issues due to ephemeris errors, we excluded data when the positioning error exceeds 50 m.  
The better the model in the positioning domain, the smaller error \(\sigma\).
# Influence of Multiple Openings on Reinforced Concrete Outrigger Walls in a Tall Building
## 2 Design of Reinforced Concrete Outrigger Wall with Multiple Openings
### Arrangement of Multiple Openings
It is essential to install openings in the regions subjected to low stresses. Figure 5: Outrigger walls with multiple openings. The blue lines represent the steel reinforcements to resist tension forces. Figure 6: Arrangement of multiple openings in a reinforced concrete outrigger wall. The height of openings was assumed to be 2 m for it to be used as a passageway. Through geometrical symmetry, the right triangular region was the same as the left one with the length, \(\Delta x_{2}\), and height, \(\Delta y_{2}\). On the other hand, the bottom triangular region was the same as the top one with length, \(\Delta x_{1}\), and height, \(\Delta y_{1}\). A half-side outrigger wall has a shear span, \(a\), an effective depth, \(d\), and overall depth of outrigger wall, \(h\), as shown in Figure 6. The effective width of the tie, \(l_{t}\), is equal to \(2(h-d)\) and is also a limitation to the location of the top and bottom openings. In order to get the maximum rectangular openings, rectangular openings should be inscribed in the triangular zones. In a practical view, the height of rectangular openings is assumed to be 2 m since it used to be the passageway. With the specified opening height, \(l_{op}\), the maximum length of the top and bottom openings, \(l_{op,1}\), as well as the maximum length of the left and right openings, \(l_{op,2}\), can be obtained by the following geometrical calculation. \[l_{op,1}=\Delta x_{1}\frac{(\Delta y_{1}-2)}{\Delta y_{1}}\ \ \ \ l_{op,2}=\Delta x_{2}\frac{( \Delta y_{2}-2)}{\Delta y_{2}} \tag{8}\]  
where \(\Delta x_{1},\ \Delta y_{1},\ \Delta x_{2}\), and \(\Delta y_{2}\) are defined in Figure 6.  
If the four openings are assumed to be the same size, among the length of the four openings, \(l_{op}\) is the smaller one compared to \(l_{op,1}\) and \(l_{op,2}\). \[l_{op}=min\left(l_{op,1},\ l_{op,2}\right) \tag{9}\]  
The total area of openings \(A_{op}\) can also be obtained using the equation below. \[A_{op}=4l_{op}l_{op} \tag{10}\]
## 1 Introduction
(2012) showed that infants placed in the prone or the side position were no more likely to experience an extreme cardiorespiratory event, compared with infants placed in the supine position [20]. This study concluded that, other than decreasing extreme cardiorespiratory events, exactly how the supine sleeping position decreases the risk of SIDS is unclear. The difference between the supine position and the same position inclined at 30\({}^{\circ}\) has not yet been analyzed in healthy children. [PERSON] et al., (2012) studied the influence of posture on breathing effort in patients with difficult weaning [21]. They hypothesized that posture could modulate the breathing effort in difficult-to-wean patients. Their prospective, crossover, physiological study was performed in 24 intubated patients breathing with pressure support who had already failed a spontaneous breathing trial or extubation episode. The results highlighted that the 45\({}^{\circ}\) position helps to unload the respiratory muscles, moderately reduces PEEP(i), and is often considered as comfortable. The semiseated position may help the weaning process in ventilator-dependent patients. The results of another study on the changes in body positions of adult patients receiving mechanical ventilation in the intensive care unit showed that the sitting position (with a thorax angularation of \(>\)30\({}^{\circ}\) from the horizontal plane) was associated with an improvement in functional residual capacity (FRC), oxygenation, and reduced breathing effort [22]. To the best of our knowledge, there are no other studies on SpO2 variations in relation to these two positions; however, according to the Italian Guidelines for Apparent Life-Threatening Events (ALTE) [23], gastroesophageal reflux may be related to episodes of apnea. In these cases, the supine position at 30\({}^{\circ}\) inclination, dubbed the \"anti-reflux position\", is advised in order to reduce the frequency of these episodes. Gastroesophageal reflux is considered physiological in the first year of life (unless severe or recurrent). The purpose of our study was to compare SpO2 variations, registered with an oximeter, in relation to pacifier use and different positions (supine and supine inclined at 30\({}^{\circ}\)).
# Higher-Order Belief Propagation Correction Decoder for Polar Codes
## 3 Design of Higher-Order Belief Propagation Correction Decoder
### A Metric to Establish the Correction Set \(\mathcal{S}\)  
Inspired by the fact that the channel noise and graph structure dominate the errors in BP decoding, we propose a new metric, which combines the reliability of the code bit and the characteristic of the stopping tree where the code node is located. Specifically, eruption by channel noise is the leading cause of decoding errors in low to medium SNR regions,while the graph structure dominates the decoding result in high SNR regions. Thus, the metric combines the two factors to identify the valid corrected node.  
A stopping set, defined as a subset of all variable nodes in the Tanner graph realization, is one of the critical factors affecting the BP decoding result and causing error floor [24]. In the stopping set, the adjacent check nodes of each variable node are connected to the set at least twice. For polar codes, a stopping set can be shaped like a tree rooted at a single information bit with leaves at code bits and includes at least one variable node from each middle column of the graph. A stopping tree rooted with it can be easily found for each information bit. An example of such a stopping tree is shown in Figure 2 with black nodes. Conversely, for each code bit \(x_{j}\), we can find the stopping trees with that specific code bit as a leaf node. Let \(ST(j)\) denote the number of stopping trees that the code bit \(x_{j}\) belongs to. According to [25], the chance of recovery for the code bit with small \(ST(j)\) is higher than others by iterations. In other words, errors in the code bits with large \(ST(j)\) have more impact on the decoding results. Therefore, we select the corrected code bit according to \(ST(j)\) combined with the reliability \(\mathcal{L}(j)\) of code bit.  
Denoting the reliability of \(j\)-th code bit as \(\mathcal{L}(j)=R_{n+1,j}+L_{n+1,j}\), a new metric \(M(j)\) for code bit \(x_{j}\) is defined in Equation (5), where \(\alpha\) is used to select the unreliable code bit and \(\beta\) is a penalty indicated by a stopping tree.  
\[M(j)=\alpha\cdot|\mathcal{L}(j)|+\beta\cdot(n+1)\cdot(1/ST(j)). \tag{5}\]  
By sorting the resulting metrics in ascending order, i.e., \(M(j_{1})\leq M(j_{2})\leq...\leq M(j_{N})\), the nodes with the smallest metrics are selected to establish the set \(\mathcal{S}\), i.e., \(\mathcal{S}=\{j_{1},j_{2},...,j_{T}\},|\mathcal{S}|=T\). The values of \(\alpha\) and \(\beta\), related to the dimension and channel noise, can be optimized by off-line Monte Carlo simulation.
## 3 Results and Discussion
### Optimization and Validation  
Taking the walnut samples with conventional storage MC (RMC) as a contrast, walnut samples with CMC were crushed using the pilot-scale walnut-crushing apparatus (as seen in Figure 2). The experimental results are shown in Table 7. Compared to the RMC, the CMC could raise the \(D_{\text{b}}\) by 0.12 and the \(d_{\text{M}}\) by 5.43 mm, indicating a more broken shell and a more whole kernel after walnut crushing. Hence, one can conclude that kernel over-crushing and shell under-crushing can occur for the walnut with RMC during the shell breaking, whereas the desired walnut-crushing state that is broken shells and whole kernels can be obtained through an optimized HA-MVD process. In addition, we found that the difference in crushing performance of walnuts with CMC through either HAD or HA-MVD is small (Table 7, Figures 9 and 11). A similar result was also reported by [PERSON] et al., where there was a slight difference in the mechanical properties of shells and kernels in terms of different drying treatments when the MC of peanuts was in the lower range [37]. Therefore, it is reasonable to ignore the effect of drying process conditions and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index Evaluation** & **Predicted Value** & **Test Value** \\ \hline Unit time drying rate (DR), \%/min & 0.215 & 0.211 \(\pm\) 0.01 \\ Unit mass drying energy consumption (DQ), kW\(\cdot\)h/kg & 3.03 & 2.99 \(\pm\) 0.08 \\ Mean square error of moisture content (MSE), \% & 2.93 & 2.89 \(\pm\) 0.65 \\ Kernel color parameter (\(\Delta\)E*) & 6.42 & 6.49 \(\pm\) 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Validation test of evaluation indexes of walnut drying process.  
further optimize a set parameter based on the HA-MVD process with regard to walnut shell breaking in this study.  
According to the results of this study, drying the walnuts to CMC using an optimized HA-MVD process could both maximally improve the crushing performance and significantly reduce the drying time. The study proposes an idea of continuous processing of drying and shell breaking from the perspective of improving quality and efficiency, which will provide a new perspective on the processing of walnuts and other nuts. In addition, we found in the walnut-drying experiments that it was difficult for the samples to precisely achieve the target MC (CMC) simultaneously. This may be attributed to uneven drying due to the differences in intrinsic properties (e.g., IMC, structure) of individual walnuts [11; 12]. Therefore, to clarify the influence of individual walnut differences on drying characteristics as well as distribution and variation in MC during drying is very important for determining the most suitable drying conditions and then improving drying uniformity.
## 4 Case Study and Results Discussion
### Analysis 1: Covariate Weight Sensitivity Analysis
#### 4.2.2 Results Discussion  
Considering all the results, it is evident that the anticipated similarities between cases M2 and M1 did not materialize. A key contributing factor was the lower weight assigned to covariate \(\gamma_{R,Die}\), as shown in Table 5. This lower weight resulted in a significant divergence in the data composition, as observed in Figure 3, which was also reflected in case M3. These findings highlighted the significant influence of the covariate weights on the resulting policies.  
Conversely, preliminary findings indicated that imposing constraints on covariate domains, whether fixed, boundless, or with IPCRidge boundaries, prior to optimizing the weights, had minimal impact on the results. This was evident in the original cases 3, 5, and 8, where identical Weibull parameters and covariate weights were obtained.  
Furthermore, a sensitivity analysis conducted on case M2 provided crucial insights into the impact of scaling methods on reliability metrics. Specifically, cases using the NS transformation exhibited dispersion in state curves similar to case M1, whereas cases utilizing the MinMax scaler showed greater dispersion, leading to increased penalization for each state.  
The results also indicated that the use of GA did not yield satisfactory outcomes across all reliability metrics. This was surprising given that the Weibull parameters in Table 5 and the cluster results in Figure 3 closely resembled the reference case. However, the weights of the covariate \(\gamma_{R,Die}\) were not close to those obtained in case M1, reinforcing the high significance of this covariate to the PHM.  
Additionally, policies employing Ipopt as the solver method and the NS transformation for data scaling most closely resembled the reference case, as demonstrated in case M4.  
Figure 6: Predictive maintenance policy for each case.  
Overall, the sensitivity analysis was necessary to reveal that achieving a superior log-likelihood score does not guarantee a feasible maintenance strategy.  
Finally, this analysis emphasized the importance of covariate weights in shaping the final decision policy and highlighted the need to accurately test and estimate covariate weights and state bands. The key question now is whether the selected covariates in this analysis were optimal or if superior alternatives existed. This is explored in subsequent analyses.