esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper refines an optimized array lateral logging tool designed for small boreholes, leveraging existing technologies. The tool features four investigation depth curves, and resistivity response curves are derived through finite element model simulations considering variables such as borehole size, mud characteristics, invasion zone features, resistive annuli, formation rock properties, and formation resistivity anisotropy. The findings included the following: (1) Increasing the wellbore diameter uniformly decreased resistivity across all four investigation depths, positively correlating with borehole size. When mud resistivity (Rm) exceeded formation resistivity (Rt), resistivity curves became distorted. (2) For high- and low-invasion models, the ratio of the deepest to the shallowest investigation depth curves ranged from 1 to 8 and 0.6 to 0.9, respectively, with maximum separation at an invasion depth of 0.5-0.8 m. (3) Under invasion conditions with annuli, an invasion zone depth and annulus width around 0.4 m yield well separated the resistivity curves for all depths. Low- and high-resistivity annuli of 2 m and 0.7 m, respectively, can cause curve intersections. (4) When the formation thickness exceeded 0.2 m, the tool accurately reflected formation resistivity variations and demonstrated effective layer identification in multi-layer conditions. (5) In anisotropic formations, resistivity was negatively correlated with the anisotropy coefficient (\(\lambda\)) as it changed from 2 to 4. The instrument can be fully utilized in the exploration of thin interlayers in oil and gas, significantly enhancing the accuracy of resource identification and extraction technologies.  
Keywords:small borehole; array lateral logging; mud invasion and annular ring; surrounding rock; anisotropy +
Footnote †: journal: Applied
## 2 Materials and Methods
### Analysis
#### 2.3.2 Major Volatile Aroma Compounds  
Direct injection gas chromatography with flame-ionization detection (GC/FID) was performed to analyze acetaldehyde, ethyl acetate, methanol, and major higher alcohols. A Varian 3350 GC (Varian Inc., Harbor City, CA, USA) was equipped with an Rtx-WAX capillary column (60 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m d.f.) (Restek, Belafonte, PA, USA). Split ratio of 1:20 was applied. Prior to quantification using calibration curves, internal standard 1-pentanol was used for normalization. Other major volatile compounds were extracted by headspace solid-phase microextraction (HS-SPME) using a divinylbenzene/Carboxen/polydimethylsiloxane fiber (DVB/CAR/PDMS; StableFlex, 50/30 \(\upmu\)m, 1 cm; Supelco, Bellafonte, PA, USA), and the analysis was carried out by GC/MS using a Varian 3900 GC coupled to a Saturn 2100T ion trap MS (Varian Inc.). The column used was the same as in the GC/FID analysis. Operation conditions and identification, quantification, and validation parameters were previously described by [PERSON] et al. [23].
# A Recursive Least-Squares with a Time-Varying Regularization Parameter
## 4 Recursive Least-Squares  
We assume that the current dimension of \(\mathbf{y}\) and the number of rows of \(\mathbf{A}\) is \(m\). We denote this by \(\mathbf{y}_{m}\) and \(\mathbf{A}_{m}\), respectively. Moreover, we can denote the individual entries of the observation vector \(\mathbf{y}\) by \(\{d_{i}\}\), and the individual rows of \(\mathbf{A}\) by \(\{\mathbf{u}_{i}^{T}\}\). As time progresses, the dimension of \(\mathbf{y}_{m}\) and the rows of \(\mathbf{A}_{m}\) increase by \(1\) and become \(\mathbf{y}_{m+1}\) and \(\mathbf{A}_{m+1}\), which can be written as follows:  
\[\mathbf{y}_{m+1} =\left[\begin{array}{c}\mathbf{y}_{m}\\ d_{m+1}\end{array}\right], \tag{8}\] \[\mathbf{A}_{m+1} =\left[\begin{array}{c}\mathbf{A}_{m}\\ \mathbf{u}_{m+1}^{T}\end{array}\right], \tag{9}\]  
where \(d_{m+1}\) is the new observed data and \(\mathbf{u}_{m+1}^{T}\) is the new row of \(\mathbf{A}\). Note that the subscript \(m\) can be viewed as a time index as well. The estimate \(\hat{\mathbf{x}}\) at dimension \(m\) can be written as follows:  
\[\hat{\mathbf{x}}_{m}=(\mathbf{A}_{m}^{T}\mathbf{A}_{m}+\gamma\mathbf{I}_{n}) ^{-1}\mathbf{A}_{m}^{T}\mathbf{y}_{m}, \tag{10}\]and for the time-updated estimate, \(\hat{\mathbf{x}}_{m+1}\),  
\[\hat{\mathbf{x}}_{m+1}=(\mathbf{A}_{m+1}^{T}\mathbf{A}_{m+1}+\gamma\mathbf{I}_{ n})^{-1}\mathbf{A}_{m+1}^{T}\mathbf{y}_{m+1}, \tag{11}\]  
Going from Equations (10) to (11) is known as a time-update step because it amounts to employing new data \(\{d_{m+1},\mathbf{u}_{m+1}^{T}\}\) in addition to all previous data [20]. Performing time-updates based on Equations (10) and (11) is costly in terms of computation since it requires inverting two \(n\times n\) matrices, in addition to memory storage, required for storing the entries of \(\mathbf{y}_{m}\) and \(\mathbf{A}_{m}\)[20]. Hence, it is important to seek a low-cost update method. The following section introduces two regularized recursive least-squares (RRLS) algorithms. The first one is well-established in the literature, which assumes a fixed regularization parameter through the update process [2,20]. The second (proposed) algorithm handles the case when the regularization parameter varies during the update process.
# An Efficient Carrier Synchronization Scheme for Demodulation Systems
## 2 Conventional Carrier Synchronization Algorithm
### Proposed Carrier Synchronization Scheme  
The frame header position needs to be detected before the carrier synchronization algorithm. Therefore, frame header detection is the key to the whole carrier synchronization scheme. As shown in Figure 3, the proposed carrier synchronization scheme is simplified from three steps to two steps. Differential detection of the SOF and PLSCODE algorithm is adopted to detect the frame header and calculate the location of the pilot block, the distance between pilot blocks, and the index according to the position of the frame header. The RLR algorithm is used to calculate the CFO, the loop filter, and the numerically controlled oscillator (NCO), which are used for CFO compensation. The RLR algorithm locked at a 33.2% CFO with high accuracy. A PA phase linear interpolation algorithm is applied to CPR.
## 3 Results and Discussion
### Amino Acid Composition of Canned Goat Meat  
Analysis of amino acid composition of the control and experimental canned samples showed that replacement of 20 and 30% of goat meat with carrots significantly increased the histidine and lysine content in the T30 sample (Table 3). Also, in T30 samples, there was a significant increase in levels of leucine + isoleucine from 1737 (control) to 1822 mg/100 g (T30). However, levels of other amino acids were decreased, which is expected due to a decrease in protein content. These data are consistent with past experimental findings [38; 40; 41]. Among the samples with added carrot, the highest total amino acid content (8508 mg/100 g) was identified in the T20 sample, but T30 showed the highest total content for essential amino acids (5956 mg/100 g), which is important for complete nutrition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name of Amino Acids**} & \multicolumn{5}{c}{**Canned Goat Meat Samples**} & \multirow{2}{*}{_p Value_} \\ \cline{2-2} \cline{4-6}  & **Control** & **T10** & **T20** & **T30** \\ \hline Non-essential & 3061 \(\pm\) 109 b  & 2506 \(\pm\) 89.6 a  & 2561 \(\pm\) 91.5 a  & 2311 \(\pm\) 82.5 a  & \textless{}0.05 \\ \hline Alanine & 718 \(\pm\) 7.71 c  & 583 \(\pm\) 8.72 a  & 602 \(\pm\)10.1 b  & 544 \(\pm\) 6.41 a  & \textless{}0.05 \\ \hline Glycine & 702 \(\pm\) 9.12 b  & 583 \(\pm\) 7.38 a  & 586 \(\pm\) 7.65 a  & 528 \(\pm\) 6.73 a  & \textless{}0.05 \\ \hline Tyrosine & 483 \(\pm\) 9.20 c  & 405 \(\pm\) 5.88 b  & 402 \(\pm\) 5.63 b  & 362 \(\pm\) 3.92 a  & \textless{}0.05 \\ \hline Proline & 603 \(\pm\) 11.2 c  & 481 \(\pm\) 7.30 b  & 506 \(\pm\) 7.15 b  & 458 \(\pm\) 6.51 a  & \textless{}0.05 \\ \hline Serin & 555 \(\pm\) 10.5 b  & 454 \(\pm\) 6.55 b  & 465 \(\pm\) 9.40 b  & 419 \(\pm\) 8.36 a  & \textless{}0.05 \\ \hline Essential & 6199 \(\pm\) 112 c  & 2480 \(\pm\) 91.2 a  & 5947 \(\pm\) 98.7 b  & 5956 \(\pm\) 90.5 b  & \textless{}0.05 \\ \hline Arginine & 741 \(\pm\) 14.9 c  & 622 \(\pm\) 6.43 b  & 617 \(\pm\)8.08 b  & 555 \(\pm\) 8.42 a  & \textless{}0.05 \\ \hline Methionine & 322 \(\pm\) 5.05 b  & 297 \(\pm\) 2.65 a  & 318 \(\pm\) 4.71 b  & 316 \(\pm\) 4.58 b  & \textless{}0.05 \\ \hline Valine & 754 \(\pm\) 13.1 c  & 686 \(\pm\) 7.15 a  & 738 \(\pm\) 11.2 b  & 730 \(\pm\) 9.76 b  & \textless{}0.05 \\ \hline Histidine & 409 \(\pm\) 5.03 b  & 356 \(\pm\) 4.26 a  & 427 \(\pm\) 6.67 c [FONOTE:c]Footnote a: footnotemark: [ENDFOOTNOTE] & 436 \(\pm\) 6.87 c [FONOTE:c]Footnote a: footnotemark: [ENDFOOTNOTE] & \textless{}0.05 \\ \hline Threonine & 533 \(\pm\) 6.07 c  & 460 \(\pm\) 5.44 a  & 496 \(\pm\) 8.46 b  & 478 \(\pm\) 6.20 a  & \textless{}0.05 \\ \hline Leucine + isoleucine & 1737 \(\pm\) 33.5 b  & 1578 \(\pm\) 26.08 a  & 1730 \(\pm\) 29.8 b  & 1822 \(\pm\) 22.45 c  & \textless{}0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Amino acid composition of canned goat meat, (mg/100 g).  
Figure 3: \(\beta\)-carotene content in canned goat meat samples (the values for bars sharing different letters (a–c) are significantly different (_p_ \(<\) 0.05)).
## 3 Methods and Data
### Data
The features are mentioned and explained concisely below:
* type of structural member--called \"Element\"(which took one of the values: COLUMN or WALL or SLAB);
* building storey--called \"Level\" (the values varied from 0 to 2);
* size of a gang assigned to a certain task--called \"Number of workers\" (the values varied from 2 to 5);
* Gang experience and skills (which took the values: MS for moderately skilled and experienced gang or AS for the averagely skilled and experienced gang, or HS for highly skilled and experienced gang);
* duration of work within one working day in hours--called \"Duration\" (which varied from 2 to 10 h);* \"Total labour\"--calculated as follows: Total labour = Number or workers \(\times\) Duration, (11)
* (standing for a number of hours spent by a certain number of workers to complete work);
* The temperature in \({}^{\circ}\)C for a certain working day--called \"Temperature\" (the values varied from \(-2\)\({}^{\circ}\)C to \(14\)\({}^{\circ}\)C);
* information about the occurrence of falls on a certain working day--called \"Falls\" (which took one of the values: YES or NO);
* information whether the work took place at the beginning, middle or end of a week--later called \"Day of the week\" (which took values M--for Monday, TWT--for Tuesday or Wednesday or Thursday, FS--for Friday or Saturday);
* the output of work--called \"Total quantity\" given in m\({}^{2}\) of assembled formwork's surface in a certain day (values varied from 3.60 m\({}^{2}\) to 133.5 m\({}^{2}\));
* \"Efficiency\"--calculated as follows: Efficiency = Total quantity/Total labour (12)  
standing for output of work per one hour with respect to the number of workers assigned to a certain scope of work. Altogether, the dataset includes 11 types of features for each record. Table 2 presents a random sample of dataset records, including the discussed 11 features and their corresponding values. In the case of the three features presented in Table 2, some additional comments seem necessary. \"Gang experience and skills\" were assessed, taking into account the individual abilities of workers belonging to a certain gang. The contractor's site engineers simply put an average score expressed by one of the three possible values into the dataset. Concerning \"Temperature\", the values represented the temperature measured in the middle of a particular working day. The ranges of the temperatures are adequate for the autumn \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **Formwork Type** & **Number of Observations** & **Observations Shares** \\  & & & **in the Dataset** \\ \hline COLUMN & system formwork & 37 & 24.34\% \\ WALL & system formwork & 51 & 33.55\% \\ SLAB & system formwork & 64 & 42.11\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of observations according to the type of element.
## 3 Analytical Model Calculation and Validation
### Validation of Calculation Results
#### 3.3.2 Magnetic Field Validation  
The analysis of the magnetic flux density around the maglev train is research content that can not be ignored in the design and manufacture of a maglev train. It is necessary to evaluate the magnetic flux density of passengers, sensors and various electrical parts.  
According to Equations (28)-(30), the surrounding magnetic flux density of the suspension system can be obtained by \(B=\sqrt{\left({B_{\mathrm{x}}}^{2}+{B_{\mathrm{y}}}^{2}+{B_{\mathrm{z}}}^{2} \right)}\). Taking the upper surface of the conductive plate \(y=0\) as an example, the results of the magnetic flux density on the surface calculated by theoretical method and FEA method are shown in Figure 15, where the parameters of the model are the same as Table 1, and the deflection angle of the PM is \(15^{\circ}\). It can be seen from the comparison that the results of the analytical method and the FEA method are almost the same. The maximum value of the magnetic flux density on the upper surface of the conductive plate is about 0.37T, and the area with the strongest magnetic flux density appears in the strip-type area corresponding to the upper side of the PM. At the same time, it can be observed that due to the translational motion of the PM in the \(x\)-direction, a slight trailing effect occurs behind the tail of the PM.  
Figure 14: Variations in drag force due to the speed with different plate materials.  
Figure 13: Variations in levitation force due to the speed with different plate materials.
# Fast and Efficient Lunar Finite Element Gravity Model
## 1 Introduction
current LP-100k lunar SH gravity model with a series of local orthogonal polynomial approximations implemented by the finite element method of modeling lunar surface gravity. In [PERSON]'s dissertation [21], a FEM surface gravity model was implemented for the geopotential, which holds significant relevance to the current applications on the lunar FEM model. With this paper, steps have been taken to produce a cost-effective and accurate FEM lunar surface gravity model that mirrors that of the paper. Furthermore, parallelization of the lunar FEM surface model is implemented, building upon the research of [22], where a parallel evaluation of the Chebyshev approximation was studied. Parallelization of the original FEM lunar model and astrodynamics applications using the FEM model further encourages computational speedup in both regards. With this, creating a FEM lunar surface gravity model elevates the surface gravity modeling field with these main contributions:  
1. Computational efficiency;
2. Memory allocation;
3. Precision;
4. Parallelization. The following sections present the key background related to the proposed method and applications. First, the FEM is reviewed based on the Chebyshev polynomial interpolation. Then, the orbit propagation is adopted to integrate the approximated force function (i.e., FEM) to generate the satellite trajectory.
## 3 Program Development and Implementation
### Implementation of Object Recognition  
Object recognition is a key aspect of this program, which utilizes YOLOv5 for object detection. YOLOv5 is widely adopted for object recognition tasks due to its high efficiency in real-time detection and high accuracy.  
Given the absence of a specialized dataset for marine engine simulators, this paper develops a dataset specifically for the simulator's pipeline system. First, SVG images were converted into PNG format (as shown in Figure 1) and elements such as Up Arrow, Left Arrow, Down Arrow, Right Arrow, Node, Pipe, and Tank were annotated. Additionally, WPF control diagrams were hidden in the background and saved as PNG files (as shown in Figure 4) and elements like Valve, Up Pump, Down Pump, Left Pump, and Right Pump were annotated. These elements were randomly combined during annotation, using Labelme (5.3.1) for labeling.  
Labelme, similar to LabelImg, provides a graphical user interface for annotation but offers more functionality, supporting various annotation shapes such as polygons, rectangles, circles, and polylines, making it ideal for tasks like semantic segmentation [25]. After normalizing the annotated JSON files and converting them to TXT format, the elements were trained using the YOLOv5X-Seg model. This model deepens and widens the network compared to others, enhancing AP accuracy and enabling the training of nonrectangular objects. The parameter settings used are shown in Figure 5.  
Figure 4: Illustration of the control recognition before saving.  
Upon completing the training, the confidence-accuracy curve is shown in Figure 6. The pipeline recognition reached an accuracy of 0.964, while the control recognition achieved 0.93, indicating the program's ability to efficiently recognize and locate components within the marine pipeline system.  
Figure 5: Parameter settings for training.  
Figure 6: Confidence-accuracy curve of the training results.  
For the converted SVG images, the positions of components such as Up Arrow, Left Arrow, Down Arrow, Right Arrow, Node, Pipe, and Tank were recognized, including co-ordinates for the top-left, bottom-left, top-right, bottom-right, and center points, which were saved to a specified CSV file. Similarly, for WPF control diagrams, the positions of components like Valve, Up Pump, Down Pump, Left Pump, and Right Pump were recognized with the same co-ordinates and automatically saved to a CSV file.  
These steps ensure the efficient recognition and localization of all components in the marine pipeline system, providing accurate and complete data for further processing and analysis. This approach facilitates complex pipeline design and lays a strong foundation for optimizing data processing workflows and enhancing system efficiency.
## 3 Results
### Trichome Density  
EOs are produced and stored in particular histological units called glandular trichomes. Glandular trichomes were recorded on both sides of the leaf, but were more numerous on the abaxial side (Table 3), with the density of peltate (PT) and capitate (CT) being approximately 2-2.5 times higher, respectively, on the abaxial face, in all the conditions evaluated (Table 3). In fact, PT are registered to be the primary places where EOs are synthesized ([PERSON] et al., 2000).  
Peppermint plants inoculated with GB03 or WCS417r showed a higher density of PT of approximately 70% on both sides of the leaf in relation to control plants. When subjected to stress, these increases fell to 50% on the abaxial and 20% on the adaxial side (\(p<0.05\)). In inoculated plants subjected to stress, they did not show significant differences with the corresponding controls (MS or SS) on either side of the leaf (\(p>0.05\)) (Table 3). In relation to the CT density, the response in general was similar to that observed for the PTs, except that on both faces of the leaf these values decreased by 25% in relation to their respective controls (\(p<0.05\)) for plants stressed (MS and SS) and inoculated with WCS417r.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Treatment** & \multicolumn{2}{c}{**Adaxial Face**} & \multicolumn{2}{c}{**Abaxial Face**} \\ \hline  & **Peltate (n per mm\({}^{2}\))** & **Capitate (n per mm\({}^{2}\))** & **Peltate (n per mm\({}^{2}\))** & **Capitate (n per mm\({}^{2}\))** \\ \hline Regularly watered & 1.31\(\pm\) 0.09 a & 4.89 \(\pm\) 0.24 a & 2.70 \(\pm\) 0.21 a & 11.83 \(\pm\) 0.69 a \\ GB03 & 1.65 \(\pm\) 0.13 ab & 5.31 \(\pm\) 0.30 a & 4.17 \(\pm\) 0.24b cd & 12.90 \(\pm\) 0.43 ab \\ WCS417r & 2.07 \(\pm\) 0.16 c & 6.46 \(\pm\) 0.35 b & 5.43 \(\pm\) 0.34 e & 16.65 \(\pm\) 0.58 c \\ \hline MS & & & & \\ Non-inoculated & 1.66 \(\pm\) 0.13 ab & 7.06 \(\pm\) 0.44 b & 4.20\(\pm\) 0.23 cd & 17.77 \(\pm\) 0.68 c \\ GB03 & 1.97 \(\pm\) 0.14 bc & 6.55\(\pm\) 0.41 b & 5.18 \(\pm\) 0.18 de & 16.87 \(\pm\) 0.65 c \\ WCS417r & 1.70 \(\pm\) 0.14 b & 5.24 \(\pm\) 0.28 a & 3.93 \(\pm\) 0.20 bc & 14.12 \(\pm\) 0.62 b \\ \hline SS & & & & \\ Non-inoculated & 1.61 \(\pm\) 0.14 ab & 6.94 \(\pm\) 0.41 b & 3.82 \(\pm\) 0.26 bc & 17.05\(\pm\) 0.91 c \\ GB03 & 1.79 \(\pm\) 0.12 bc & 6.68 \(\pm\) 0.37 b & 4.08 \(\pm\) 0.29 bc & 16.61\(\pm\) 0.86 c \\ WCS417r & 1.57 \(\pm\) 0.09 ab & 5.14 \(\pm\) 0.29 a & 3.39 \(\pm\) 0.29 ab & 13.22 \(\pm\) 0.57 ab \\ \hline \hline \end{tabular}
\end{table}
Table 3: Glandular trichome density from peppermint subjected to moderate (MS) or severe drought stress (SS) drought stress and inoculated with different PGPR strains (mean \(\pm\) SE). Means followed by different letters indicate significant differences between treatments based on [PERSON]’s LSD test (n = 15–20, \(p<0.05\)).  
Figure 2: Variations in VOC emissions from peppermint plants subjected to moderate (MS) or severe drought stress (SS) and inoculated with different PGPR strains (mean \(\pm\) SE). Different letters indicate significant differences between treatments based on [PERSON]’s LSD test (n = 15–20, \(p<\)0.05).
# Conical Emission Induced by the Filamentation of Femtosecond Vortex Beams in Water
###### Abstract  
Conical emission is a typical nonlinear phenomenon that occurs during the filamentation of femtosecond laser pulses in transparent media. In this work, the conical emission induced by two kinds of typical vortex beams (i.e., Laguerre-Gaussian (LG) and Bessel-Gaussian (BG) beams) in water is experimentally studied. By recording the light spots of different spectra components from the supercontinuum induced by the vortex beams, the characteristics of the conical emission induced by femtosecond vortex beams are studied. It is found that the spots of the supercontinuum induced by the two kinds of vortex beams differ greatly from each other. The spots of the supercontinuum induced by the BG beams are a set of concentric rings like a rainbow with a white center, while the white light spots in the case of the LG beams are circular white disks, which are different from the commonly observed white light spots. By measuring the maximum divergence angle, it is observed that the divergence angle increases with a decrease in the wavelength, while it is merely affected by the topological charge, which is explained by the formation mechanism of conical emission in terms of self-phase modulation. Based on the observed results, we discuss the transfer of optical angular momentum during the supercontinuum induced by the filamentation of femtosecond vortex beams. This work may help to better understand the transfer of optical angular momentum in non-optical parametric processes as well as the interaction of high-intensity pulses with matter.  
Keywords:conical emission; vortex beams; topological charge; orbital angular momentum; femtosecond filament +
Footnote †: journal:  
Article