esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory of Parameter Inversion
### Principles and Optimization Steps of the Improved Modular Vector Method
\tag{9}\]In the equation, \(\Delta_{11}\) signifies the current exploration step size for \(B_{11}\). (5) \(B_{11}\) is employed as the interim modulus vector point, with the remaining parameters of \(B_{1}\) being sequentially adjusted. After each adjustment, the resulting new modulus vector point is treated as the interim modulus vector point for the subsequent adjustment. The calculation of this new point, denoted as \(B_{1i}\), is determined in accordance with Equation (10). \[B_{1i}=\left\{ \begin{array}{ccc}B_{1(i-1)}+\Delta_{1i}&,&\epsilon(B_{1(i-1)}+ \Delta_{1i})<\epsilon(B_{1(i-1)})\\ B_{1(i-1)}-\Delta_{1i}&,&\epsilon(B_{1(i-1)}-\Delta_{1i})<\epsilon(B_{1(i-1)}) <\epsilon(B_{1(i-1)}+\Delta_{1i})\\ B_{1(i-1)}&,&\epsilon(B_{1(i-1)})<\min\Bigl{\{}\epsilon(B_{1(i-1)}-\Delta_{1i} ),\epsilon(B_{1(i-1)}+\Delta_{1i})\Bigr{\}}\end{array} \right. \tag{10}\]  
In the formulae, \(\Delta_{1i}\) represents the current exploration step size of \(B_{1i}\), and \(i\) ranges from 2 to 7. (6) Once all parameters have been adjusted using the modulus vector movement, the temporary modulus vector point \(B_{17}\) is achieved. By setting \(B_{2}\) equal to \(B_{17}\), the first modulus vector \(B_{20}\) is constructed from the new base point \(B_{2}\) and the initial base point \(B_{1}\), with the calculation of \(B_{20}\) following the procedure outlined in Equation (11). \[B_{20}=B_{1}+2(B_{2}-B_{1}) \tag{11}\]  
(7) An analogous exploration within the vicinity of \(B_{20}\) results in the acquisition of temporary modulus vector points \(B_{21}\), \(B_{22}\),... \(B_{27}\). In this context, \(B_{3}\) is set equal to \(B_{27}\), and the second modulus vector, denoted as \(B_{30}\), is determined based on \(B_{3}\) and \(B_{2}\). The calculation of \(B_{30}\) is elaborated in Equation (12). \[B_{30}=B_{2}+2(B_{3}-B_{2}) \tag{12}\]  
(8) Iteratively repeat the aforementioned process until the \(j\)th modulus vector satisfies \(\epsilon(B_{j0})<\epsilon(B_{j})\). If necessary, reduce the step size and continue exploration until the termination step size condition is met. Upon satisfying the termination criteria, conclude the iteration process and output the optimal solution.
## 2 Materials and Methods
### Data Analysis
#### 2.2.1 Univariate Analysis  
The distribution of continuous variables was visualized using the kernel density plot to display the observed values' dispersion. In contrast, the bar chart was used for categorical variables to represent the group's frequency in each variable, as shown in Figure 3. The kernel density plot was used instead of a typical histogram because it produces a smooth curve without having a normality assumption by considering each observation of the variable to contribute to different classes representing the distribution [37]. It also gives clear information on whether the distribution has a normal, bimodal, or skewed distribution. A bar chart was used to visualize PLUG_SIGN and SCREEN variables because the number of groups in both variables is not huge. The groups in both variables are easily identified and interpreted when looking at the bar chart.  
Figure 3: Distribution plot of sand retention test (SRT) variables. (**a**) Slurry test variables; (**b**) sand pack test variables.  
As for the slurry data, the PLUG_SIGN and the SCREEN variables fall under the categorical type, while the SCREEN variable is the only categorical type in the sand pack data. It shows that the occurrence of the screen to plug is lower than the screen not to plug. The premium screen frequency has the highest number for both the slurry and sand pack data, followed by the WWS screen.  
The continuous slurry variables with bimodal distributions were D10_B, D50_B, D90_B, UC, FINE_CO_B, and RETAINED_PRM. Thus, the mode was used as central values for these variables. On the other hand, the SLOT_SIZE variable distribution looked symmetrical, while the SAND_CONC_IN_TC, SAND_PRODUCED, and SAND_PROD_PER_AREA gave positive skewed distributions. Hence, the mean value was used to represent the central value of the SLOT_SIZE, whereas the median was used for the positive skewed distributions.  
Furthermore, all continuous variables of sand pack data showed positive skewed distributions where the average value of each variable is greater than the median, and all variables have outliers. However, SIZE_CR1, SIZE_CR2, SIZE_CR3, and SIZE_CR4 variables also had two peaks in the distribution in which the data had more than one center, which means only SAND_PROD_PER_AREA showed a positive skewed distribution with a single peak. Therefore, the useful measure that can accurately capture the central tendency for SAND_PROD_PER_AREA is the median. Simultaneously, the mode is used for SIZE_CR1, SIZE_CR2, SIZE_CR3 and SIZE_CR4 because both the mean and median have no meaningful interpretations for a bimodal distribution.
# An Algorithm for Arranging Operators to Balance Assembly Lines and Reduce Operator Training Time
## 2 Problem Statement  
Traditionally, when arranging a limited number of operators on an assembly line, a manager begins with one operator per workstation, before adding an operator to the workstation with the lowest output. Step by step, all of the operators are arranged and the output rate can be found.  
When a workstation has a lower output rate, the manager typically adds another operator. However, the lower output might be due to the \"human\" factor; an operator who is trained on many machines might be assigned to a workstation outside of their skill set. For example, if an operator knows how to work an auto-optical inspection machine but is assigned to a workstation with an in-circuit test machine, the station's output rate will drop because the operator does not have the required skills. Output can also fall when a worker is absent or resigns unexpectedly and the manager fills the vacancy with a random worker who has not been trained to operate that workstation.  
Thus, the problem statement of this study is:  
Ideal: The maximum output rate is found, all of the workstations have the same output rate, and the number of operators at each workstation can be found, with all operators assigned to a workstation that matches their skill set.  
Reality: The arrangement algorithm is step by step, and the maximum output rate cannot be determined until all of the operators have been arranged. Operators can be assigned to unsuitable workstations.  
Consequences: The maximum output rate is an unknown that will affect a boss's decisions, especially before setting up a new plant. An operator who works at an unsuitable workstation might complain, be absent, or quit unexpectedly, causing a lower output rate.  
Proposal: An arrangement algorithm with a constant number of operators must consider the maximum output rate and balance the assembly line without any increase in cost. A sorting algorithm must consider the skills required at each workstation and the skill set of each operator.
## 3 Materials and Methods
### Matrix Effect, Validation and Recovery Tests  
The analytical performances, such as linearity, of the Pyrr-PIL fibers were evaluated using the following conditions: \(T\) = 45 \({}^{\circ}\)C, \(t\) = 15 min, 2.5 wt % of NaCl and standard solution concentrations varying from 0 to 0.2 g\(\cdot\)L\({}^{-1}\). Limits of detection were determined by decreasing the concentration of each one of the standard solutions until the signal to noise ratio of 3 (S/N = 3) was achieved. The recovery tests were accessed by using a real human urine sample. The urine sample was collected from a healthy 31 years old man, filtered using a 0.45 \(\upmu\)m pore size PTFE filter, and stored at 4 \({}^{\circ}\)C. Relative recoveries were obtained by spiking known amounts of analytes into the urine sample.
## 2 Observer-Based Estimation  
As far as the VSA estimation is concerned, three state observers are found in the literature: the Luenberger observer (LO), the sliding-mode observer (SMO), the Kalman filter (KF), and their variants. All of them can be used both for linear and non-linear systems. As discussed, observers are based on a vehicle model; therefore, Section 2.1 analyses the most used vehicle models. Owing to the KF being the most used observer (71 studies out of 120 observer-based in total), a general description of the LO and SMO is provided hereafter, whilst a full section, i.e., Section 2.2, is dedicated to the KF method. Finally, Section 2.3 discusses the potential use of GPS to enhance observer-based estimation.  
LO and SMO are deterministic observers. A few works propose the use of a LO for VSA estimation, but most of them are only based on simulations [59; 111]. The SMO is slightly more complicated than the LO, as it introduces a sign function that is typical of sliding mode theory, and is considered a robust and efficient method to estimate vehicle variables and states [15; 20; 21; 22; 23; 60]. It has been applied in many forms such as triangular, first order, second order, and so forth. As discussed, LO and SMO can be used for both linear and non-linear applications, according to the type of vehicle model adopted (see Section 2.1). The main advantage of LO and SMO is their simplicity; however, they are designed for deterministic systems. That means they assume a complete knowledge of the system and its inputs, not considering potential modelling errors or measurement noises. While some deterministic observers may be robust and tolerant against modelling errors and measurement noise, KF-based observers assume to deal with stochastic systems; hence, they are inherently designed to deal with model approximations and measurement noise [112]. Nonetheless, the KF observers are still relatively simple to implement.  
In summary, the most used observer for VSA estimation is the KF, due to its ability to use input and measurement noise information directly, and because it is robust, stable, and relatively simple to implement [15; 18; 19].
# From General Language Understanding to Noisy Text Comprehension
## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Name**} & \multicolumn{6}{c}{**Logistic Regression**} & \multicolumn{6}{c}{**MLP**} \\ \cline{3-10}  & & **SL** & **WC** & **BS** & **TD** & **SM** & **SL** & **WC** & **BS** & **TD** & **SM** \\ \hline S-BERT & None & 39.53 & 29.57 & 66.53 & 49.27 & 68.36 & 44.79 & 29.27 & 67.36 & 49.27 & 65.47 \\ \hline \multirow{4}{*}{BERT} & CLS & 33.83 & 25 & 65.95 & 40.29 & 65.06 & 38.39 & 25.61 & 66.28 & 48.22 & 65.2 \\  & Last layer & 45.75 & 44.51 & 70.27 & 53.24 & 71.66 & 51.8 & 46.65 & 71.52 & 56.01 & 71.39 \\  & Last four & 50.57 & 52.44 & 72.77 & 51.92 & 73.59 & 56.79 & 55.79 & 72.94 & 56.54 & 72.9 \\  & All & 55.57 & 67.68 & **73.61** & **54.69** & **74.83** & 66.87 & 71.04 & 73.77 & 59.18 & **73.18** \\  & First layer & **58.63** & **82.01** & 67.94 & 52.44 & 70.01 & 66.96 & **85.67** & 67.78 & 58.39 & 70.56 \\  & 1-5-9 & 56.35 & 70.12 & 72.52 & 54.43 & 72.9 & **68.8** & 74.09 & **73.94** & **59.84** & 71.53 \\ \hline \multirow{2}{*}{Glove} & glove\_6b & 22.7 & 64.02 & 59.87 & 38.57 & 58.18 & 24.45 & 64.02 & 59.2 & 38.44 & 57.08 \\  & glove\_twitter & 26.38 & 66.77 & 61.53 & 40.29 & 64.65 & 25.42 & 69.82 & 61.37 & 42.01 & 63.96 \\ \hline Word2 Vec & word2 vec\_neg & 25.24 & 62.2 & 61.62 & 36.2 & 67.4 & 26.47 & 70.43 & 62.7 & 40.16 & 66.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification accuracies for different sentence vectors (SL—SentLen, WC—WC, BS—BShift, TD—TreeDepth, SM—SOMO). further research in NLU by providing additional datasets that cover the domain of noisy data. It is also significant that future research should focus on the understanding of pre-processing Tweets to reduce the noise level of the linguistic knowledge distribution and the derived sentence representations. Moreover, the same probing dataset could be used to examine the relationship between the BERT's attention layers and the meaning-rich sentence embeddings. This could help to derive more meaning-rich sentence vectors.
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
###### Abstract  
Global Navigation Satellite Systems (GNSS) are the main source of position, navigation, and timing (PNT) information and will be a key player in the next-generation intelligent transportation systems and safety-critical applications, but several limitations need to be overcome to meet the stringent performance requirements. One of the open issues is how to provide precise PNT solutions in harsh propagation environments. Under nominal conditions, the former is typically achieved by exploiting carrier phase information through precise positioning techniques, but these methods are very sensitive to the quality of phase observables. Another option that is gaining interest in the scientific community is the use of large bandwidth signals, which allow obtaining a better baseband resolution, and therefore more precise code-based observables. Two options may be considered: (i) high-order binary offset carrier (HO-BOC) modulations or (ii) the concept of GNSS meta-signals. In this contribution, we assess the time-delay and phase maximum likelihood (ML) estimation performance limits of such signals, together with the performance translation into the position domain, considering single point positioning (SPP) and RTK solutions, being an important missing point in the literature. A comprehensive discussion is provided on the estimators' behavior, the corresponding ML threshold regions, the impact of good and bad satellite constellation geometries, and final conclusions on the best candidates, which may lead to precise solutions under harsh conditions. It is found that if the receiver is constrained by the receiver bandwidth, the best choices are the L1-M or E6-Public Regulated Service (PRS) signals. If the receiver is able to operate at 60 MHz, it is recommended to exploit the full-bandwidth Galileo E5 signal. In terms of robustness and performance, if the receiver can operate at 135 MHz, the best choice is to use the GNSS meta-signals E5 + E6 or B2 + B3, which provide the best overall performances regardless of the positioning method used, the satellite constellation geometry, or the propagation conditions.  
GNSS; Cramer-Rao bound; time-delay and phase ML estimation; SPP and RTK positioning; precise positioning; GNSS meta-signals; high-order BOC signals +
Footnote †: journal: Sensors
## 5 Immobilized Lipase
### Types of Supports and Immobilization Procedures
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Lipase Source** & **Feedstock** & **Immobilization** & **Yield \({}^{\text{a}}\) (\%)** & **No. Cycles** & **References** \\ \hline _A. niger_ & Sardine oil & Activated carbon & 94.5 \({}^{\text{a}}\) & 5 & [62] \\ _T. Inagimous_ & Land & Silica gel & 97.6 \({}^{\text{a}}\) & 20 & [90] \\ _C. antarctica_ & Waste cooking oil & Silica nanoflower & 98.5 \({}^{\text{a}}\) & 15 & [70] \\ _B. capsaicin_ & Castor oil & Polyvinylalcohol alginate & 75 \({}^{\text{b}}\) & 6 & [85] \\ _R. mizule and_ & Waste cooking oil & Epoxy functionalized silica & 91.5 \({}^{\text{a}}\) & 14 & [100] \\ _Streptomyces_ sp & Waste cooking oil & XAD 1180 resin & 95.45 \({}^{\text{a}}\) & 4 & [79] \\ \multicolumn{5}{c}{Polysiloxane-polvinyl} \\ _B. capsaicin_ & Beef tallow &
\begin{tabular}{c} Beef tallow \\ alcohol (5032-PVA) hybrid \\ composals \\ \end{tabular} & 89 \({}^{\text{a}}\) & - & [101] \\ _C. antarctica_ & Waste fish oil & Acrylic resin & 95 \({}^{\text{a}}\) & 4 & [102] \\ _C. antarctica_ & Waste fish oil & Acrylic resin & 75 \({}^{\text{a}}\) & 10 & [103] \\ \hline \end{tabular} \({}^{\text{a}}\) Biodiesel yield (wt/wt.%) was determined as the methyl esters amount produced by the lipases in the reaction process divided by the initial amount of esters, or \({}^{\text{b}}\) by the amount of oil.  
\end{table} 
Table 3: Biodiesel production with various immobilized lipases. Figure 2: Major types of biological catalysis for biodiesel generation. and coprecipitation [114]. Lipase is strongly adsorbed by metal-chelating affinity immobilization that is a simple technique with the advantage that support may be reused [54]. Desorption agents may cause the desorption of the enzyme that can be also achieved by changing the pH value [115]. The compound n-hexane could reduce the deactivation of AGMNP-CO\({}_{2}\)+-PFL from methanol. It was reported that biodiesel production from oil transesterification was higher with n-hexane than using tert-butanol [70; 94]. Encapsulation immobilization entraps lipase by a co-precipitation method and crosslinking agents like glutaraldehyde are used to interconnect the enzymes. However, there is a high mass transfer resistance that reduces its efficiency. The entrapment of cells having lipase activity appears to be a simple and attractive technique. Lipase immobilized in silicon granules or calcium alginate beads, with glucose supplementation for cells maintenance, achieved an increased number of cycles, 28 instead of 23, while keeping 90% activity [33]. Whole cell, recombinant methods and protein and metabolic engineering are promising options to increase lipase applications [82].
# Green Sweet Pepper Fruit and Peduncle Detection Using Mask R-CNN in Greenhouses
## 2 Materials and Methods
### Dataset Construction and Annotation  
In this experiment, the images were scaled to 1024 \(\times\) 1024 pixels. To avoid singularities within the set of images obtained (own and those of the base dataset), it is ensured that the set of images includes images of sweet peppers in various natural conditions. A total of 1127 images were randomly selected and used for parameter optimization and training of the Mask R-CNN model, following the data set methodology of 80% as the training set and 20% as the verification set (80/20). Once the training was completed, 100 random images from the initial own set obtained (from the about 30 k initial images) were used to evaluate the performance of the trained model.  
Given the need to obtain reliable and relevant results, the 100 images used for evaluation were acquired from our initial set of images, which considers daylight illumination variations (full harvest period), many varied examples of shadows, overlapping fruits and peduncles, leaves, and stems of the main crop, and some examples with distortion from moving images, variation in size, visibility, brightness, and quantity per image of sweet pepper peduncles and fruits (different ripeness and color). In addition, because our focus is on the most complex color variety of sweet pepper, our training and evaluation sets contain mostly examples of green sweet peppers (due to their resemblance to the environment). Therefore, our implementation aims to provide results in a real production environment  
Figure 3: Image acquisition diagram.  
Figure 2: View from inside the sweet pepper greenhouses of the Experimental Agricultural Field of the Tec de Monterrey campus Quééétaro (CAETEC).  
that is challenging and does not consider ideal examples where lighting is very controlled and other factors are chosen to obtain images that are easier to analyze, based on a realistic and complex dataset that considers most cases of the real production environment. In addition, ref. [30] is the only one that provides a database with the same characteristics; therefore, it is used as a base as described in the previous subsection.  
The image annotation tool VGG Image Annotator (VIA), Visual Geometry Group VGG, Oxford, UK [31], in version 2.0.12, was used to annotate/label the experimental dataset to generate images of sweet pepper masks. The performance of the trained model, for instance segmentation, is evaluated by comparing the annotated mask images against the mask prediction results. The regions of the sweet pepper belonging to its peduncle or stem, and fruit were labeled, and the remaining region passes by default as the background. Annotated sweet pepper images are shown in Figure 4.
# Effects of Velocity Profiles on Measuring Accuracy of Transit-Time Ultrasonic Flowmeter
## 5 Conclusions  
The effect of velocity profiles across the pipe on the propagation time of ultrasonic wave is considered for transit-time ultrasonic flowmeters. Theoretical correction factors were proposed to improve measurement accuracy. For laminar flow, the correction factor is a constant of 0.75. For turbulent flow, the correction factor varies with Reynolds number, show as Equation (27). Flow measurement experiments were performed for laminar and turbulent flow respectively, and experiment results showed that the proposed correction factors agree well with measured correction factors. The average relative error is determined to be 0.976% for laminar flow and 0.25% for turbulent flow.  
Conceptualization, C.G.; Data curation, H.Z.; Formal analysis, H.Z.; Funding acquisition, C.G.; Investigation, H.Z.; Methodology, C.G.; Resources, J.L.; Software, J.L.; Writing--original draft, H.Z.; Writing--review & editing, C.G.  
This research was funded by the Fundamental Research Funds for the Central Universities, grant number 2017 XKZD02.  
The authors are very grateful to Dr. [PERSON] for his help in the experiments.  
The authors declare no conflict of interest.
# Gorilla Glass Cutting Using Femtosecond Laser Pulse Filaments
## 4 Results and Discussion
### Cutting Gorilla Glass Using Femtosecond Laser Pulse Filament-Assisted High Aspect Ratio Micro-Voids
(**a**) Micro-groove formed on the top surface when \(E=210\)\(\upmu\)J, \(S=5\) mm/s, \(F=0\)\(\upmu\)m; (**b**–**d**) bottom surface: (**b**) \(E=210\)\(\upmu\)J, \(S=2\) mm/s, \(F=50\)\(\upmu\)m; (**c**) \(E=210\)\(\upmu\)J, \(S=1\) mm/s, \(F=100\)\(\upmu\)m; (**d**) \(E=210\)\(\upmu\)J, \(S=1\) mm/s, \(F=200\)\(\upmu\)m; (**e**–**g**) Side view of the fully cut 0.5 mm thick Gorilla glass samples using single time laser irradiation: (**e**) \(E=150\)\(\upmu\)J, \(S=3\) mm/s, \(F=100\)\(\upmu\)m; (**f**) \(E=100\)\(\upmu\)J, \(S=2\) mm/s, \(F=100\)\(\upmu\)m; (**g**) \(E=210\)\(\upmu\)J, \(S=1\) mm/s, \(F=100\)\(\upmu\)m.  
\(F=100\) um (Figure 8g). Although the focus position of the laser beam was 100 um, the filament position was \(\sim\)146 um in all cases. Figure 8e depicts the side view of the optical microscope image of the cut Gorilla glass for Case 1. The filament length was greater than 354 um. However, we detected a damaged layer near the focal point of the laser beam and surface roughness was high. For Case 2, the filament position and length were \(\sim\)146 um and \(>354\) um, which is evident from the side view image of the cut glass of Figure 8f. Like Case 1, we detected a damaged layer at the focal point of the laser beam for Case 2. Due to the decrease of scanning speed from 3 mm/s to 2 mm/s, the surface roughness has been improved significantly in Case 2 as compared to Case 1. Like Case 1 and Case 2, the filament position and length were \(\sim\)146 um and \(>354\) um for Case 3, as evident from the side view image of the cut glass of Figure 8g. The decrease in scanning speed in Case 3 was responsible for the smooth surface quality of the cut layer of the Gorilla glass where no damaged layer was evident inside the glass. Experimental results exemplify Case 3 as the best combinations for single cut of a 0.5 mm thick Gorilla glass. After femtosecond laser irradiation, we expect slight melting of materials in some places inside the micro-channel. This melted portion has been re-solidified and loosely bonded immediately after laser beam scanning. This phenomenon is less significant in femtosecond lasers as compared to nanosecond or picosecond lasers, because femtosecond lasers produce small heat affected zone around the micro-machined area. To overcome the limitation caused by re-solidified materials, filament-engraved glass samples went through mechanical cleaving process for cutting Gorilla glass samples into two pieces. Still, the key contributor in Gorilla glass cutting is the femtosecond laser micro-machining.