esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparative Analysis of the Ability of Composite Strains to Improve Oil Recovery  
The ultimate goal of MEOR is to enhance the oil recovery or the oil production rate. As shown in Table 2, WJ-1, FY-07, and the mixed bacteria of WJ-1 and FY-07 greatly improved the oil recovery compared to the control sample. After the control sample was stationary for 10 days, the subsequent water flooding enhanced the oil recovery by 3.9%, which was due to the redistribution of the oil and water after a period of stationary storage, and the re-entry of the crude oil from low permeability layers into the high permeability layers, and further recovery in subsequent water flooding. FY-07 enhanced oil recovery by 10.4%. This is because the biopolymer produced by the metabolism of the strain formed a plugging of the high-permeability layer, which further improved the recovery of crude oil in the low-permeability layer. WJ-1 enhanced the oil recovery by 7.9%, since the strain degraded the degraded high-carbon chain crude oil into low-carbon chain crude oil, improved the fluidity of crude oil, and the metabolized biological surfactant emulsified the crude oil. The composite flooding of FY-07 and WJ-1 increased oil recovery by 17.4%, which was significantly higher than the individual effects of FY-07 and WJ-1. This is because, after water flooding, most of the crude oil existed in the low permeability layer. First, FY-07 was injected to block the high permeability layer 12. Then, the injected WJ-1 entered the low permeability layer in large quantities and acted on most of the crude oil. Therefore, it exhibited a synergistic effect on the oil displacement and has a certain application prospect for the efficient development of heterogeneous reservoirs.
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 2 Materials and Methods
### Chemometric Analysis
#### 2.3.2 Competitive Adaptive Reweighted Squares  
Competitive adaptive reweighted sampling (CARS) is a feature wavelength selection algorithm which imitates the principle of \"Survival of the fittest\" in [PERSON]'s evolution theory. This algorithm adopts the idea of Monte Carlo sampling or the random sampling method, which selects part of the samples to conduct PLS modeling and repeatedly performs hundreds of modeling iterations [25]. In the process of selecting wavelength variables, the same variable set was repeatedly tested, and CARS remains the wavelength variable with the large absolute value of PLS regression coefficient and removes the wavelength variable with the lowest absolute value of PLS regression coefficient; thus, a series of subsets of wavelength variables is obtained [26]. Additionally, CARS calculates the RMSECV value of the model and selects the minimum value corresponding to the subset of variables for the optimal subset of variables.
###### Abstract
This calculation allows us to determine the fractional DD indices. For the OTFS symbol detection part, a novel hybrid MP detection algorithm has been introduced. When fractional Doppler effects are present, the channel responses are dispersed across the entire Doppler spectrum. This dispersion leads to more interfering symbols and a high computational complexity. However, only a subset of path gains corresponding to a specific Doppler frequency shifts rather than the whole dispersion, which will significantly affect the transmission of the OTFS signal [42; 43; 44; 46]; therefore, a selective approach is employed. Specifically, data detection is implemented based on the paths with the highest gains, which are named main paths, associated with the dispersed Doppler indices. We employed a non-approximated MP algorithm for the primary propagation paths, whereas a GA-based MP algorithm was used for the secondary propagation paths. Through our simulation results, we illustrate its effectiveness regarding accuracy.
## Appendix A Applied Methodologies and Software Tools  
Figure 14 shows several processing stages of a data pipeline based on a study [69] that aims to create KGs for the automation industry. In addition, an end-to-end digital twin pipeline [70] has been taken into account.  
Figure 11: Knowledge graph pipeline based on [69].  
It is beyond the scope of this paper to discuss the data acquisition and import selection parts of the pipeline. Only the KG, the ontology creation, the data queries, the mapping, and the data enrichment and visualization will be discussed. The phases, the methods used, and the different software stages of the industrial case study presented are shown in Figure 12.  
Figure 12: The steps of the applied method.  
Before processing the TTL file in a Python 3.12.3 environment using Pyvis (a Python library for visualizing networks) [71] and KGlab [72; 73], the sub-ontologies and the whole KG were developed using Protege [74]. Either Protege or KGlab can be used to import data into the ontology skeleton and to create axioms and properties in Python. For each data query, SPARQL language has been used [66]; moreover, Pyvis offers a graphical representation. Having mapped the semantic data, it was further aggregated in Python to obtain data-enriched graphs for analysis. Normal, directed, or hypergraphs can also be used for graph-based visualization of KG data. Finally, as a concept (denoted by a dashed line in Figure 12), the key information, generated charts, statements, or messages can be displayed on dashboards and DAS devices, or fill any other elements of the application block with data, as previously presented in Figure 4.
# Advanced Tensor Solution to the Problem of Inter-Domain Routing with Normalized Quality of Service
## 7 Discussion
The routing solutions obtained with its help are aimed at increasing network scalability. The novelty of the method is the provision of domain-normalized average end-to-end packet delay when the normalized Quality of Service requirements are met in each domain separately (18) and routing solutions are coordinated under inter-domain interaction (5). Numerical studies have shown that the convergence rate of the coordination procedure, namely the number of coordination iterations, was influenced by the following main factors:  
* Number of domains in the network;
* Structure and size of domains;
* Number of border routers;
* Network congestion;
* Implementation of multipath routing strategy. As the number of border routers increased, the number of iterations in the general case also increased, expanding the time to solve the inter-domain routing problem. Figure 11 shows the dependence of the number of iterations of the coordination procedure on the number of network domains, with the number of border routers equal to two.
## 5 Analysis of Slope Stability
### Calculation of the Safety Factor for the Slope  
The numerical simulation results of slope stability are shown in Figures 6 and 7, and the safety factors obtained for the slope using different calculation methods are shown in Figure 8. Regardless of which calculation method is adopted, the safety factor of the two profiles is greater than 1.2, indicating that the PCO-goaf has good stability after graded treatment. It is worth noting that under the same conditions, the slope safety factors are Load 1 > Load 2 > Load 3. For example, when Section 1 is calculated using Simplified Janbu, the safety factors are 1.314 > 1.266 > 1.248. This shows that the effect of blasting on slope stability cannot be ignored.  
The stability of the OP-slope is influenced by the coupling of the goaf and blasting disturbance [42]. Blasting produces powerful stress waves, resulting in cumulative damage and stress concentration in the rock mass, and the separation and fracture of particles in the rock mass result in a stress drop, changing the stress state of the original rock [43; 44]. The macroscopic manifestation involves the appearance of voids and cracks in the overlying rock mass. In addition, under the action of the roof's gravity, the stable structure of the surrounding rock in the goaf is further changed because of the cumulative damage [45]. When the damage evolution exceeds the bearing strength of the weakened roof, local curing occurs, which generates a rock collapse zone, fracture zone, and continuous subsidence zone. The filling treatment of 2# and 3# plays a major role in slope stability. The backfill provides a reaction force that resists the goaf roof, effectively controls the underground mine, and limits the deformation of the rock mass. This paper demonstrates that the filling approach is an efficacious measure for addressing concealed goafs in open-pit mines, and its efficacy in treating underground goals has been affirmed for a long time.  
Although certain research efforts have been made in open-pit and underground collaborative mining, no systematic outcomes have emerged. Moreover, the disaster prevention measures for slopes with concealed cavities are still in their infancy [46]. Currently, the principal treatment methods of concealed goats include filling, caving, and plugging. It is requisite to propose the corresponding management plan in accordance with the risk characteristics of the goat. This paper offers a dependable scheme for the treatment of concealed goats.  
Figure 8: Safety factor of the slope under different conditions.  
Figure 7: Lowe–Karafiath method used for calculating slope safety factor; Section 1: (**a–c**); Section 2: (**d–f**); Section 3: (**g–i**).
# Circular Orbit Flip Trajectories Generated by E-Sail
## 2 Problem Description and Mathematical Approach
### Trajectory Design and Optimization
The optimal control law is derived from the Pontryagin maximum principle [34] by maximizing, at each instant of time, the Hamiltonian function (20). The result is in agreement with the model proposed in Ref. [28], so that the pitch angle \(\alpha_{n}\) and the switching parameter \(\tau\) are expressed as a function of the two dimensionless adjoint variables \(\{\lambda_{\widetilde{u}},\lambda_{\widetilde{v}}\}\) as  
\[\alpha_{n}=\frac{\alpha_{p}}{2},\quad\tau=\frac{1+\text{sign}\big{(}1+3\cos \alpha_{p}\big{)}}{2} \tag{25}\]  
where \(\text{sign}(\square)\) denotes the signum function, while the auxiliary angle \(\alpha_{p}\in[-\pi/2,\pi/2]\) rad is defined as  
\[\alpha_{p}\triangleq\arctan\biggl{(}\frac{\lambda_{\widetilde{v}}}{\lambda_{ \widetilde{u}}}\biggr{)}. \tag{26}\]The transversality condition [33] provides two additional constraints at the final time  
\[\widetilde{r}(\widetilde{t}_{f})=1,\quad\widetilde{u}(\widetilde{t}_{f})=0, \quad\widetilde{v}(\widetilde{t}_{f})=-1. \tag{18}\]0  
which complete the two-point boundary problem (TPBVP) formed by the four equations of motion (12)-(15), the four Euler-Lagrange differential Equations (21)-(24), and the seven boundary constraints given by Equations (16) and (18). In fact, since in this problem \(\widetilde{t}_{f}\) is an output of the optimization process [33], solving the TPBVP requires nine scalar constraints. For a given value of the dimensionless propulsive acceleration \(\beta\), the TPBVP associated to the optimization process has been solved with an absolute error less than \(10^{-8}\). Note that, according to Equation (22), the adjoint variable \(\lambda_{\theta}\) is a constant of motion and, taking advantage of Equation (27), we get \(\lambda_{\theta}=0\) throughout the flight. The adjoint variable \(\lambda_{\theta}\) can therefore be removed from the mathematical model, with a slight simplification of the numerical solution.
# Ballasted Track Status Evaluation Based on Apparent Track Stiffness Index
## 2 Main Concepts
### Apparent Track Stiffness
#### 2.1.2 Classification of Supporting and Hanging Sleepers Using LFWD  
[PERSON] et al. (2018) measured and compared the apparent stiffness under the LFWD load (10 kN) and under train load (80 kN) to look into the effect of loading level on track stiffness. The apparent stiffness under train load was calculated using the ratio of the maximum train wheel load measured between sleepers to the maximum sleeper displacement. The comparison between the stiffness values (\(\mathbf{k_{Train}}\)/\(\mathbf{k_{LWD}}\)) resulted in a significant correlation. Figure 2 shows the result of calculating the ratio of the two sets of apparent stiffness. In the graph, the \(x\)-axis is the apparent stiffness measured by the LFWD and the \(y\)-axis is the ratio between the stiffness values (\(\mathbf{k_{Train}}\)/\(\mathbf{k_{LWD}}\)). As can be seen from the graph, the sleepers could be divided into two groups based on the stiffness measurement: supporting sleepers which have relatively great stiffness and hanging sleepers which have relatively smaller stiffness. The ratio appears to be constant at about 4 for the relatively great stiffness, whereas the ratio increases as the apparent stiffness decreases for relatively smaller apparent stiffness. That is, the ratio is distributed like a bi-linear function. This is because as the degree of sleeper hanging increases, the difference in the stiffness based on the load size also increases ([PERSON] and [PERSON], 2018). Figure 3 shows the load-deflection characteristics of ballasted track. In the case of a supporting sleeper, which means  
Figure 1: Light falling weight deflectometer (LFWD) testing: (**a**) LFWD equipment; (**b**) Field test; (**c**) Measured data.  
there is no void under the sleeper, the difference according to load level (\(k_{Train,Sup}\)/\(k_{LWD,sup}\)) is small. In the case of a hanging sleeper, which means there is a void under the sleeper, the bigger the void, the bigger the difference according to load level (\(k_{Train,Hang}\)/\(k_{LWD,Hang}\)).  
In Figure 2, when the sleepers are divided into hanging and supporting sleeper groups, a trend line for each group can be drawn. R\({}^{2}\) is the coefficient of determination. The R\({}^{2}\) of the hanging sleeper group was 0.68. This means that the ratio of \(k_{train}\) to \(k_{LWD}\) could be calculated with a probability of 68% when using the apparent stiffness \(k_{LWD}\) and trend line. On the other hand, the R\({}^{2}\) of the supporting sleepers was 0.0014. This very low R\({}^{2}\) value indicates that \(k_{train}\) is almost independent of \(k_{LWD}\) for supporting sleepers. The ratio of \(k_{train}\) to \(k_{LWD}\) is about 4 regardless of the apparent stiffness \(k_{LWD}\). The two trend lines (i.e., hanging sleepers and supporting sleepers) intersect at apparent stiffness of 70 to 80 kN/mm.  
Figure 3: Load–deflection characteristics of ballasted track [15].  
Figure 2: Change in the ratio of \(k_{Train}\) to \(k_{LWD}\) according to level of apparent stiffness.
# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 2 System and Signal Characterization
The mapping between the time domain signal and the frequency domain signal, for the \(k\)th subcarrier (assumed invariant during the transmission of a given block), of the transmitted block is defined as \(DFT\Big{\{}s_{n}^{(t)};n=0,1,\ldots,N-1\Big{\}}=\Big{\{}S_{k}^{(t)};k=0,1, \ldots,N-1\Big{\}}\), i.e., by performing the Discrete Fourier Transform (DFT) of the time-domain block. Similar mapping is assumed for the received block as \(DFT\Big{\{}y_{n}^{(r)};k=0,1,\ldots,N-1\Big{\}}=\Big{\{}Y_{k}^{(r)};k=0,1, \ldots,N-1\Big{\}}\). After removing the cyclic prefix, and assuming a cyclic prefix longer than the overall channel impulse response of each channel, using the matrix-vector representation, we arrive at the received frequency-domain signal [20]:  
\[Y_{k}=\Big{[}Y_{k}^{(1)},\ldots,\ Y_{k}^{(R)}\Big{]}=H_{k}S_{k}+N_{k} \tag{1}\]  
Figure 1: Block diagram of an LIS System. where \(S_{k}=\left[S_{k}^{(1)},\ldots,\ S_{k}^{(T)}\right]^{T}\) stands for the frequency domain transmitted data symbols; where \(H_{k}\) denotes the \(T\ \times\ R\) channel matrix for the \(k\)th subcarrier, with \((r,t)\)th element \(H_{k}^{(t,r)}\), and where \(H_{k}\) denotes the channel frequency response for the \(k\)th subcarrier (assumed invariant during the transmission of a given block). Note that the mapping between the time and frequency domains is defined by \(\{H_{k};\ k=0,1,\ldots,\ N-1\}\ =\ DFT\{h_{n};n=0,1,\ldots,N-1\}\). Moreover, \(N_{k}\) is the frequency-domain block channel noise for that subcarrier [21].
###### Abstract  
Optical and microwave images have been combined for land cover monitoring in different agriculture scenarios, providing useful information on qualitative and quantitative land cover changes. This study aims to assess the complementarity and interoperability of optical (SPOT-5 Take-5) and synthetic aperture radar (SAR) (Sentinel-1A) data for crop parameter (basal crop coefficient (\(K_{cb}\)) values and the length of the crop's development stages) retrieval and crop type classification, with a focus on crop water requirements, for an irrigation perimeter in Angola. SPOT-5 Take-5 images are used as a proxy of Sentinel-2 data to evaluate the potential of their enhanced temporal resolution for agricultural applications. In situ data are also used to complement the Earth Observation (EO) data. The Normalized Difference Vegetation Index (NDVI) and dual (VV + VH) polarization backscattering time series are used to compute the \(K_{cb}\) curve for four crop types (maize, soybean, bean and pasture) and to estimate the length of each phenological growth stage. The \(K_{cb}\) values are then used to compute the crop's evapotranspiration and to subsequently estimate the crop irrigation requirements based on a soil water balance model. A significant \(R^{2}\) correlation between NDVI and backscatter time series was observed for all crops, demonstrating that optical data can be replaced by microwave data in the presence of cloud cover. However, it was not possible to properly identify each stage of the crop cycle due to the lack of EO data for the complete growing season.  
agriculture; land cover change; SPOT-5 Take-5; Sentinel-1A; evapotranspiration; TIGER initiative +
Footnote †: journal: Remote Sensing, 2016, 8, 525; doi:10.3390/rs8060525
## 6 Discussion
### RQ3: What Are Students' Perceptions of ITS-CAL?  
The questionnaire and qualitative feedback suggest that most students view ITS-CAL positively, with approximately 60% agreeing that the system improves their programming skills. Students also praised its user-friendliness and convenience. However, several shortcomings were identified and are as follows:  
* Hint Function: students found the hints overly simplistic, particularly lacking guidance for handling hidden test data.
* Debug Function: the debug responses were deemed too general, providing limited assistance for specific issues.
* User-defined Question Function: this feature was perceived as costly to use, with room for improvement in meeting students' needs effectively.  
In addition, students expressed concerns about the deduction mechanism, with some suggesting that it discourages system usage. They recommended reducing the penalty's weight to encourage greater freedom in exploring ITS-CAL's features for learning. Other suggestions included adding a usage count reminder to prevent unintended overuse and optimizing the code editing interface for a better user experience.
## 2 Method
### Markov Chain Co-Simulation
#### 2.2.4 MCRF Co-Simulation (Co-MCSS)  
Similar to the cokriging model in classical geostatistics, Co-MCSS can be built by extending Markov chain random fields. According to Bayesian reasoning theory, Co-MCSS can be regarded as the Bayesian updating of the Markov chain random field model based on new evidence on auxiliary data.  
If \(X\) is the target classification variable to be estimated, and \(E\) is the auxiliary data set, then the Bayesian inference formula can be written as follows:  
\[P(X|E)=\frac{P(E|X)P(X)}{P(E)}=\frac{P(E|X)P(X)}{\sum_{X}P(E|X)P(X)}=C^{-1}P(E| X)P(X) \tag{3}\]  
where \(C=\sum_{X}P(E|X)P(X)\) is a constant. Based on Bayes principle, the simplified Equation (3) is extended to the Co-MCSS model, and the auxiliary data are combined via co-simulation. The contribution of auxiliary variables can be realized in different ways. In this study, auxiliary variable data are considered the nearest neighbor of an unknown location in other variable spaces. The Co-MCSS model with \(k\) auxiliary variables can be expressed as follows:  
\[p\Big{[}i_{0}(u_{0})|i_{1}(u_{1}),\dots,i_{m}(u_{m});r_{0}^{(1)}(u_{0}^{(1)}), \dots,r_{0}^{(k)}(u_{0}^{(k)})\Big{]} = \frac{p_{i_{1}i_{0}}(h_{10})\prod_{g=2}^{m}p_{i_{0}i_{g}}\big{(} h_{0g}\big{)}\prod_{l=1}^{k}q_{i_{0}r_{0}^{(l)}}}{\sum_{f=0}^{n}\left[p_{i_{1}f_{0}} (h_{10})\prod_{g=2}^{m}p_{f_{0}i_{g}}\big{(}h_{0g}\big{)}\prod_{l=1}^{k}q_{f_{ 0}r_{0}^{(l)}}\right]} \tag{4}\]  
where \(r_{0}^{(k)}\) is the class of the \(k\)th auxiliary variable at \(u_{0}^{(k)}\), and \(q_{i0}r_{0}^{(k)}\) is the transition field probability matrix between \(i_{0}\) in the space of the primary variable at position \(u_{0}\) and \(r_{0}\) in the space of the auxiliary variable. In this study, two auxiliary variables are selected: (1) Pre-classified image and (2) DEM. Therefore, \(k=2\) in Equation (4).  
In practical applications, considering many nearest known neighbors in different directions is unnecessary and difficult. For the pixel data of a remote sensing image, the four main directions are easily considered. Therefore, Equation (4) of the Co-MCSS considering two auxiliary variables and four main directions is as follows:  
\[p\Big{[}i_{0}(u_{0})|i_{1}(u_{1}),\dots,i_{m}(u_{m});r_{0}^{(1)}(u_{0}^{(1)}), \dots,r_{0}^{(4)}(u_{0}^{(k_{2})})\Big{]} = \frac{p_{i_{1}i_{0}}(h_{10})\prod_{g=2}^{4}p_{i_{0}i_{g}}\big{(} h_{0g}\big{)}\prod_{l=1}^{2}q_{i_{0}r_{0}^{(l)}}}{\sum_{f=0}^{n}\left[p_{i_{1}f_{0}} (h_{10})\prod_{g=2}^{4}p_{f_{0}i_{g}}\big{(}h_{0g}\big{)}\prod_{l=1}^{2}q_{f_{ 0}r_{0}^{(l)}}\right]} \tag{5}\]  
Equation (5) is the Co-MCSS model used in this study.
# Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating the Sun
## 2 Sun Position
### Time \(\to\) Anomaly  
Sun's position can be defined by an angle (named anomaly, from 0 to 360\({}^{\circ}\)) measuring the arc traveled along an orbital trajectory, from some reference point. Anomaly (mean, eccentric, true) is always counted from Perigee, the point where Sun is closer to Earth, while celestial longitude (ecliptic, equatorial) is always counted from the meridian including the First Point of Aries. Perigee changes every year, making it unsuitable as a reference for accurate astronomical calculations.  
These issues are solved, applying two angles accurately measured for some arbitrary date, called standard epoch (such as 12:00 UT, 1 January 2000, designated as J2000 [22] and often used in this work): the angle \(\varpi_{g}\) from the First Point of Aries to Perigee, and the angle \(\varepsilon_{g}\) from the First Point of Aries to the east (see Figure 2).  
Figure 2: Geocentric model showing the key orbital elements needed to calculate the Sun’s mean anomaly, using ecliptic longitudes measured for a standard epoch.  
As the full circumference arc of \(360^{\circ}\) corresponds to \(365.242191\) sidereal days, the Sun's mean anomaly \(M\) (Sun's position in a circular orbit with average speed around Earth) is given by (step 4):  
\[M=\frac{360D_{e}}{365.242191}+\varepsilon_{g}-\varpi_{g} \tag{1}\]  
where \(D_{e}\) represents the (fractional) number of sidereal days counted from the standard epoch (calculated subtracting the Julian days between the required time and the standard epoch), and \(\varepsilon_{g}-\varpi_{g}\) accounts for the arc difference between standard epoch and Perigee. Each year elapsed from Perigee at standard epoch adds \(360^{\circ}\) to \(M\), so the actual value of the mean anomaly (in degrees) is the remainder of the integer division of \(M\) by \(360\).