esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Methods and Business Intelligent Tools for Budget Planning--Case Study
## 3 Analysis of the Budgeting Process and Its Support with ERP Systems Tools  
The process of defining an organization's budget, usually for the following year, is a multi-stage process that includes analyzing data from various areas of the company, planning expenses and revenues, and approving the budget. This process is commonly divided into several steps:  
1. Defining the company's goals and strategies, such as revenue growth, profitability, debt reduction, etc.
2. Analyzing past financial performance to understand which areas performed well and where issues arose. This includes a detailed review of operational and material costs as well as a revenue analysis.
3. Revenue forecasting: Estimating revenue based on market expectations, trends, and customer order forecasts.
4. Cost forecasting: Estimating costs, including operational costs, production costs, wages, marketing expenses, and other expenditures.
5. Developing a preliminary budget based on departmental budgets: Each department (e.g., marketing, production, sales) prepares its own budget aligned with the company's overall objectives.
6. Data aggregation: Compiling budgets from individual departments to create a comprehensive company budget.
7. Risk and opportunity assessment: Identifying potential financial risks and opportunities that could impact the budget.
8. Goal alignment verification: Ensuring that the preliminary budget aligns with the company's strategic goals and is realistic.
9. Negotiations and revisions: If needed, the budget may be adjusted following negotiations among various departments.
10. Budget approval: The preliminary budget is presented to the company's management for approval. Once accepted by the board, it becomes the official financial plan for the upcoming period.
11. Budget monitoring and control: Continuous monitoring of financial performance compared to the budget, such as through monthly or quarterly reports, allows for the identification and analysis of budget deviations and enables corrective actions when necessary.  
Thus, the budgeting process in organizations is a complex process that takes into account many different aspects. An essential element for the implementation of the budget is data on various areas of activity and sourced in an appropriate manner and a representative period of time. The budget process requires gathering data across the organization, most often recorded in an ERP system, followed by cross-sectional analysis and forecasts, including the following:  
* Quantitative sales data;Direct costs such as materials, storage, logistics, manufacturing;
* Indirect costs like administrative, general management, energy, heating, and water;
* Historical revenues and profitability.
# THz-TDS Techniques of Thickness Measurements in Thin Shim Stock Films and Composite Materials
## 5 Conclusions  
In this approach, the refractive index measurement technique was established to calculate the material properties regarding the utilization of the T-ray for the non-destructive examination of Shim stock films and GFRP composites. In addition, the T-ray limitation in the energy penetrating power was discussed with respect to the conduction characteristics of the GFRP composites and the fiber lamination angle of CFRP plates. Possible THz-TDS techniques are summarized for measuring the thickness of the thin Shim stock films and GFRP composites as follows:  
(1) It was possible to solve the refractive index of the thin Shim stock films and GFRP composites utilizing T-ray techniques under the reflection mode.  
(2) The T-ray showed a constant level of penetrating power in the glass fiber class composites, which led to a very high penetration ratio and enabled the optimization of a strong reception signal. Therefore, it was found that it had potential reproducibility.  
(3) The values of the measured thicknesses for both Shim stock films and GFRP composites were in agreement with those of the nominal thicknesses. The values were successfully measured through the correlation between the TOF cap and the resonance frequency under the reflection mode.  
(4) We expect that the manufactured thickness measurement device using T-ray techniques may be very useful for non-destructive examinations in future applications in the advanced aerospace field.  
[PERSON] suggested and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON] helped in the accomplishment of ideas and the administration of the experiments. The data were discussed and analyzed, and the manuscript was written and revised by all members. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) (No. 2021R11A3042195) and also experimentally helped by the CNDE at Iowa State University, Ames, IA, USA.  
Not applicable.  
Figure 10: The relation between the nominal thickness and the thickness measured from the T-ray techniques in the GFRP composites.
## 1 Introduction
[19] proposed a shearer positioning error analysis model of a SINS using the extended Kalman filter. However, in a coal mine environment filled with dust, noise and humidity, the SINS will suffer from the mechanical vibration of the shearer and lead to large measuring errors. There are many factors that can cause the mechanical vibration of a shearer, such as the cutting of discontinuous coal, inaccurate installation of the shearer drums and distributing the picks non-uniformly around the drum [20]. Moreover, the shearer breaks the coal with a rotary impact motion, which can produce vibration of the shearer fuselage and drums. Scholars have researched the dynamic model of shearers and analyzed their mechanical vibration. [PERSON] et al. [21] described the dynamic model of a shearer  
Figure 1: Equipment layout of a typical fully mechanized mining face; (**a**) front view; (**b**) top view. cutting system and calculated the differential equations of the dynamic model using the Runge-Kutta iterative method. Currently, the widely used SINS utilizes low-cost micro electro mechanical systems (MEMS) inertial sensors, which directly measure acceleration and angular rate [22]. Unfortunately, the gear eccentricity and manufacturing error and clearances at joints can cause angular vibration and linear vibration of the shearer [23]. The mechanical vibration of the shearer fuselage, including angular vibration and linear vibration, can reduce the accuracy of the measured acceleration and angular velocity, resulting in the positioning error of the SINS [24]. The angular vibration can lead to a coning error, which can affect the calculation of the attitude update [25]. Meanwhile, the angular vibration and linear vibration can lead to a sculling error, which will affect the calculation of velocity update and position update [26]. The study of these two errors is common. Since [PERSON] [27] proposed the theory of the rotation vector in 1971, the noncommutativity error of SINS has been effectively solved [28]. [PERSON] et al. [29] proposed a direct coning mitigation algorithm based on the sinusoidal component of gyro measurements. This algorithm can be applied to the mitigation of sculling errors. [PERSON] et al. [30] analyzed the SINS coning error for the vibration of unmanned aerial vehicles and proposed an integrated vibration model between sinusoidal angular vibration and random angular vibration. Meanwhile, the coning error is compensated by the multi-sample algorithm. In this paper, we established a shearer positioning system for a fully mechanized working face based on the SINS [31], and we conducted an independent positioning experiment on the shearer. The experimental results show that the shearer positioning system based on SINS can accurately reflect the position information of the shearer.
## 2 Methods
### Design and Implementation Approach
For advanced applications, dual-parameter FBG systems employing two gratings with distinct sensitivities enable matrix-based decoupling of temperature and strain signals. Moreover, software-based post-processing using pre-characterized calibration curves can offer additional correction when direct hardware compensation is not feasible. These techniques, when integrated appropriately, ensure that FBG-based systems maintain high accuracy even under varying thermal environments, enhancing their applicability in real-world biomechanical monitoring scenarios. Figure 6: Photographs of the knee band apparatus: Panel (**a**) shows the elastic knee band, with green ellipses marking the position of the pressure buttons. Panel (**b**) shows the sensing component attached to the standard elastic knee band. Panel (**c**) is a close-up of the PVC foil containing the embedded FBG, indicated with a red circle, and the pressure buttons are marked with green ellipses [34].
## 2 Materials and Methods
### Theoretical Backgrou
#### 2.1.6 Latent Semantic Analysis (LSA)  
LSA is one of the topic modeling techniques used in natural language processing. It is an information retrieval technique for extracting the semantic content of a large text document and calculating the similarity between documents. LSA creates a word-document matrix and reducing it to a low-dimensional semantic space. It represents a given document set as a matrix and uses Singular Value Factorization (SVD) to extract the latent semantic content of the document. Through SVD, the word-document matrix is factorized into three matrices, with the middle matrix representing the semantic space. In this semantic space, both words and documents are represented as vectors, and the similarity between vectors can be calculated to facilitate tasks such as document classification, search, and summarization. An advantage of LSA is the reduced dimensionality of the data, improving computational efficiency. It also allows various tasks such as document classification,  
Figure 4: Topic LDA—The LDA model graphically represented with plate notation.  
document summarization, and topic modeling through vector operations in the semantic space. One disadvantage of LSA, however, is that it considers all words included in the word-document matrix, and thus computational demands and memory requirements can also increase as the size of the data increases. In addition, LSA only considers word frequency information when creating the word-document matrix and does not consider the order or words and the context, potentially leading to the loss of meaning. Furthermore, as LSA models the relationship between words and documents linearly, it may not accurately represent nonlinear semantic relationships.
# Partial Oscillation Flow Control on Airfoil at Low Reynolds Numbers
## 4 Results and Discussion
### Influence of the Oscillation Frequency
Figure 12 illustrates the position of transition and the extent of the LSB along the airfoil under varying oscillation frequencies, with the oscillation position fixed at \(0.6c\). Figure 12a depicts that for the rigid airfoil, the transition position is located at \(0.656c\); however, when subjected to oscillation, this transition point shifts forward. At a frequency of \(f=4\) Hz, the transition position shifts to \(0.602c\). In Figure 13, the friction coefficient \(C_{f}\) along the upper surface of the airfoil is depicted. Evidently from the illustration, partial oscillation induces an earlier transition effect, thereby causing a shift in the frictional characteristics on the airfoil's surface. As gleaned from Figure 13, the \(C_{f}\) value upstream of the oscillation zone remains unaffected by the partial oscillation. Conversely, within the downstream area of the oscillation, \(C_{f}\) values escalate due to the oscillation and rise above those of the rigid airfoil. This phenomenon constitutes the primary driver behind the fluctuation in the viscous drag coefficient. Figure 11: Pressure and viscous drag coefficients of the airfoil under different oscillation frequencies (\(P=0.6c\)). Figure 12: Position of the transition and length of the laminar separation bubble on the airfoil under different oscillation frequencies (\(P=0.6c\)). (**a**) Position of the transition; (**b**) length of the laminar separation bubble. Figure 13: Friction coefficient of the airfoil (\(P=0.6c\), \(f=4\) Hz). Figure 12b illustrates that the length of the LSB on a rigid airfoil amounts to \(0.215c\), and the implementation of partial oscillation significantly reduces this LSB dimension. At a frequency of \(f=4\) Hz, the LSB length reaches its minimum at \(0.185c\), demonstrating a \(14.0\%\) reduction in comparison to the length of the LSB on the rigid airfoil. From Figure 13, one can discern the changes in the LSB, where the regions marked by \(C_{f}\) values lower than zero denote the LSB's location. Figure 14 presents the precise outline of the LSB on the airfoil. Combining insights from Figures 13 and 14, it becomes evident that the oscillation does not have a significant influence on the onset of laminar separation; however, it visibly causes the reattachment point to advance. Consequently, the LSB shortens, which in turn results in a reduction in the pressure drag coefficient.
## 2 Quasi-Resonance Circuit  
### Principles of Induction Heating  
In accordance with [PERSON]'s and [PERSON]'s laws, the introduction of a changing magnetic field in a conductor results in the induction of electrical currents that oppose the original changes in the magnetic field. These circular currents, termed eddy currents [18; 19], prompt the material to rapidly heat up upon encountering internal resistance. Illustrated in Figure 1, an alternating current flowing through the red coil produces a variable magnetic field of matching frequency. Guided by a ferrite core and influenced by its shape and penetration depth, this field induces circulating eddy currents within the conductor. In materials exhibiting relatively high resistance, such currents generate significant heat (RI\({}^{2}\)), leading to a swift rise in the material's temperature. Additionally, depending on the material type, hysteresis losses [20] stemming from the constant reversal of magnetic fields can further contribute to temperature elevation.  
Figure 1: Eddy currents with alternating magnetic fields.  
Also, we can determine the penetration depth of the magnetic fields inside the metals by the following [21]:  
\[\delta=\sqrt{\frac{\rho}{\mu\pi f}} \tag{1}\]  
where \(\rho\), \(\mu\), and \(f\) denote resistivity [\(\Omega\).m], magnetic permeability [H/m], and frequency [Hz], respectively. Equation (1) indicates a reduction in penetration depth as the frequency of the applied magnetic field increases, and therefore, IH provides a distinctive and important feature: the desired depths inside metals can be heated remotely by changing the frequency of the alternating magnetic field [22]. Choosing the right frequency for IH depends on the type of the material, the gap between the piece and the current-carrying coil, the working dimensions, and the desired penetration depth. For example, in induction furnaces for melting different metals, the frequency can range from 50 Hz to 400 kHz (usually, the smaller the object, the shallower the depth of penetration required, so a higher frequency should be used). In our intended application (induction stoves), a working frequency ranging from 25 kHz to 50 kHz can be used [23].
# Two-Step Enzymatic Synthesis of Structured Triacylglycerglycerols from ARASCO+
## 3 Results and Discussion
### Ethanolysis of Coconut Oil
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**No Added Water**} & \multicolumn{4}{c}{**Added Water**} \\ \hline  & **5 h** & **8 h** & **12 h** & **24 h** & **5 h** & **8 h** & **12 h** & **24 h** \\ \hline MAG & 4.6 \(\pm\) 0.7 \({}^{\circ}\) & 5.2 \(\pm\) 0.7 \({}^{\circ}\) & 2.9 \(\pm\) 0.4 \({}^{\mathrm{b}}\) & 2.0 \(\pm\) 0.5 \({}^{\mathrm{ab}}\) & 3.1 \(\pm\) 0.4 \({}^{\mathrm{b}}\) & 1.8 \(\pm\) 0.5 \({}^{\mathrm{ab}}\) & 1.1 \(\pm\) 0.3 \({}^{\mathrm{a}}\) & 0.9 \(\pm\) 0.4 \({}^{\mathrm{a}}\) \\ \hline DAG & 6.9 \(\pm\) 0.9 \({}^{\mathrm{cd}}\) & 6.5 \(\pm\) 1.0 \({}^{\mathrm{cd}}\) & 6.5 \(\pm\) 1.0 \({}^{\mathrm{cd}}\) & 3.4 \(\pm\) 0.7 \({}^{\mathrm{ab}}\) & 8.4 \(\pm\) 0.8 \({}^{\mathrm{d}}\) & 5.4 \(\pm\) 0.5 \({}^{\mathrm{bc}}\) & 4.3 \(\pm\) 0.4 \({}^{\mathrm{ab}}\) & 2.4 \(\pm\) 0.3 \({}^{\mathrm{a}}\) \\ \hline FFA & ND & ND & ND & ND & 1.2 \(\pm\) 0.1 \({}^{\mathrm{a}}\) & 1.4 \(\pm\) 0.1 \({}^{\mathrm{ab}}\) & 1.8 \(\pm\) 0.2 \({}^{\mathrm{b}}\) & 3.6 \(\pm\) 0.4 \({}^{\mathrm{c}}\) \\ \hline FAEE + TAG & 88.5 \(\pm\) 1.0 \({}^{\mathrm{abc}}\) & 88.3 \(\pm\) 1.1 \({}^{\mathrm{ab}}\) & 90.5 \(\pm\) 1.5 \({}^{\mathrm{bcd}}\) & 94.6 \(\pm\) 1.0 \({}^{\mathrm{e}}\) & 87.3 \(\pm\) 0.8 \({}^{\mathrm{a}}\) & 91.5 \(\pm\) 1.4 \({}^{\mathrm{cd}}\) & 92.8 \(\pm\) 0.9 \({}^{\mathrm{de}}\) & 93.2 \(\pm\) 0.9 \({}^{\mathrm{de}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The distribution of reaction products (mol%) after the ethanolysis of coconut oil using the Thermomyces lanuginosus lipase as a biocatalyst in a water-free medium and a water-added medium (4% of the oil weight). The results are reported as mean values \(\pm\) standard deviation of three replicates. For each lipid class, the values at different reaction times and with or without added water are compared in each row, and those with different lowercase letters are significantly different (\(p\) < 0.05) according to ANOVA and [PERSON] ’s post hoc test. ND: not detected.
# Calibration of Magnetometers with GNSS Receivers and Magnetometer-Aided GNSS Ambiguity Fixing
## 6 Conclusions  
In this paper, a method for calibration of magnetic field sensors with three Global Navigation Satellite System (GNSS) receivers was provided. A GNSS-based attitude information was obtained from the relative positions between the GNSS receivers. The relative positions were jointly estimated with the carrier phase integer ambiguities and pseudorange multipath errors. The latter ones were considered as state parameters to exploit the spatial and temporal correlations of multipath. The reliability of carrier phase integer ambiguity fixing was improved by integration of prior information on the baseline coordinates provided in a local body-frame.  
The attitude information of the calibrated magnetometer was also integrated into the sequential tree search of ambiguity fixing, i.e., integer candidates with inconsistent attitude information were disregarded. It was shown that a heading and pitch angle accuracy of 10 degrees are sufficient to reduce the probability of incorrect instantaneous ambiguity fixing by several orders of magnitude.  
Finally, the proposed method was tested with three low-cost Multi-GNSS receivers and a magnetic field sensor in a test drive. A tightly coupled Multi-GNSS/INS system served as a reference.  
Figure 6: Heading determination with calibrated magnetometer in comparison to tightly-coupled GNSS/INS heading: the latter one has an accuracy of 0.25 degrees and serves as a reference. As the magnetometer-based heading is noisy, a filtered version with a time constant of 0.1 s is also shown.  
Figure 7: Detailed analysis of heading performance for two sections with moderate to high rotational dynamics: the filtered magnetometer-based heading deviates by less than 10 degrees from the GNSS/INS tightly coupled heading.  
The measurement results showed that the calibrated and filtered heading differed by less than 10\({}^{\circ}\) from the reference heading.  
This work was supported by the German Research Foundation (DFG) and the Technische Universitat Munchen within the funding programme Open Access Publishing.  
The author declares no conflict of interest.
## 3 Experiments
### Diurnal Variation Observation  
In order to evaluate the performance of the proposed method, a field experiment is conducted subsequently. The diurnal variation of 24 h was observed with a sampling frequency of 1 Hz in the experiment. Meanwhile, the diurnal variation obtained by the Changchun Geomagnetic station was used as the reference for evaluating the observation accuracy of the proposed method. Figure 8 illustrates the diurnal variation observations. It can be intuitively seen that the trend of observations of the proposed method is highly consistent with the reference. Besides, two indexes of Pearson correlation coefficient \(p\)[16] and average error \(\overline{e}\) (Equation (28)) are introduced in the paper to evaluate the performance of the proposed method more objectively, as it is shown in Table 2. The larger \(p\) is, the higher the correlation is and the smaller \(\overline{e}\) is, the higher the accuracy of observation is. Commonly, \(0.8\leq p<1\) denotes high correlation. The fact that \(p\) of each component is greater than 0.8 and \(\overline{e}\) is less than 1.2 nT indicates high observation accuracy of the proposed method.  
\[p=\frac{\sum_{i=1}^{n}(o(i)-\overline{o})(r(i)-\overline{r})}{\sqrt{\sum_{i=1} ^{n}(o(i)-\overline{o})^{2}}\sqrt{\sum_{i=1}^{n}(r(i)-\overline{r})^{2}}}\]  
\[\overline{e}=\frac{1}{n}\sum_{i=1}^{n}\lvert o(i)-r(i)\rvert \tag{31}\]  
where \(o\) denotes observed value and \(\overline{o}\) denotes the average value of \(o\), \(r\) denotes reference value and \(\overline{r}\) denotes the average value of \(r\).  
Figure 8: Diurnal variation observations. (**a**) The observed value of \(x\)-component; (**b**) The observed value of \(y\) -component; (**c**) The observed value of \(z\) -component.