esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### SiOC Optical Waveguides
The difference in coupling efficiency among SiOC waveguides with different core widths (between 2 \(\upmu\)m and 4.4 \(\upmu\)m) is 5% for both polarizations as evaluated from electromagnetic simulations. A difference of 5% means an additional loss of 0.2 dB that is within measurement error of our setup and has no appreciable effect on the assessment of waveguides losses. Furthermore, the coupling efficiency for TE and TM polarizations differing by 1% for respective waveguide width is negligible. The similar coupling efficiency is due to the anisotropy of the material that brings the n\({}_{\text{eff}}\) of TE and TM modes closer to each other. Figure 9b shows propagation loss as a function of core width. The average losses for both TE and TM polarizations are similar while there is a small increase in the propagation losses from wider (4.4 \(\upmu\)m) to narrower (2 \(\upmu\)m) waveguides. The confinement factor is calculated to decrease by about 8% from wider to narrower core waveguides for both TE and TM polarizations is a small number and has no impact on losses. The increase in propagation loss in narrower core waveguides can hence be attributed to the sidewall roughness that contribute to scattering of light. Moreover, from ellipsometric measurements the extinction coefficient k is undetectable which implies that SiOC material losses are low. Therefore, the origin of the waveguide losses is expected from the surface roughness and columnar structure of the sputtered SiOC film. To reduce the waveguide losses, the sputter process may further be optimized to obtain more compact SiOC films, annealing may be considered to smoothen the film structure or CVD process [22]. Figure 8: (**a**) Fundamental TE and TM modes of the SiOC waveguide (W = 4400 \(\times\) H = 400 nm\({}^{2}\)) and their respective effective indices n\({}_{\text{eff}}\) computed with FEM based software, (**b**) Plot of effective refractive index n\({}_{\text{eff}}\) of SiOC waveguide modes as a function of increasing width (2–4.4 \(\upmu\)m), only fundamental TE and TM modes exist at \(\lambda\) = 1550 nm. The results on SiOC waveguides indicate the significance of this material for guided wave applications. SiOC optical waveguides with higher refractive index (n > 1.578) are under development and will be demonstrated in a future study.
# Design Criteria for Planning the Agricultural Rainwater Harvesting Systems: A Review
## 3 Discussion
Multicriteria analysis is also used, and in some cases, location restrictions and maps made with GIS are used as well to superimpose them and establish the appropriate collection sites [87; 102; 105; 106]. The process of analytical hierarchy is supported by multicriteria analysis based on maps developed using GIS and remote sensing to locate the different suitable sites for the construction of structures for rainwater collection, being the methodology applicable to any area due to the flexibility of scores [101]. This group also analyzes the productivity of soil and amount of water retained in it, which are accompanied by supplementary irrigation, where the success or failure of these rainwater harvesting systems is marked by the socioeconomic conditions of the population to the implementation of the technique and the perception of risk and profitability of farmers [46; 100]. It also facilitates its use for reforestation and the planting of fruit trees, as well as increases the productivity of arable and grazing lands, increasing yields and reducing crop losses [100]. Similarly, some authors take into consideration geometric characteristics to determine the volume of water to be captured according to a specific return period [103] and the generation of runoff [106] and even the consultation of stakeholders to obtain the most appropriate area, considering the knowledge of the population in relation to the construction of these systems [71]. The fifth group (reserve tillage systems) makes use of machinery to build footprints in the earth. This method is similar conservation tillage but generates water storage obtained by the formation of small surface deposits [109]. Essentially, the advantage of this method is that it improves soil moisture and increases agricultural productivity, which is associated with soil texture and consequently its productivity. In addition, this system is used to avoid erosion problems [51]. The group was considered especially, since in the majority field, experiments are carried out to determine productivity, contemplating the characteristics of the crop, the soil, and the precipitation in the study area. Additionally, a reduction in soil erosion is obtained with this type of RWHS. Experimental studies have focused on determining the effect of reservoir tillage on the amount of water contained in the soil, with treatments carried out with low and high soil compaction [108; 109]. Other aspects through which it is analyzed with are the slope, density of the soil, and intensities of precipitation [112]. To calculate the efficiency of reservoir tillage, this was compared with no reservoir or conventional tillage [108; 110; 111], zero tillage, minimum tillage, rainwater collection in the field, and Daling plowing [113].
## 5 Information Processing Module (IPM) Design
### Cross Validation  
This section aims to demonstrate that the chosen LDB, although simple, is well suited to the problem, and thereby eliminates the need for additional feature engineering. Therefore, it is essential to assess not only the model's performance but also its ability to generalize, which justifies the use of cross-validation.  
Therefore, from the final dataset (Figure 12), 50% of the samples were randomly selected to create a new dataset, the training dataset (TrainDS), as shown in Figure 13.  
Sequentially, the TrainDS was divided into 10 equally sized folds. The model was trained and evaluated 10 times with a different fold being used for testing each time and the remaining nine for training. This approach ensures that every sample is used once for testing and nine times for training. Four commonly used metrics for classification  
Figure 13: Training dataset (TrainDS).  
problems were employed to evaluate the model's performance: accuracy, precision, recall, and F1 score.  
To gain a clearer understanding of the concepts of accuracy, precision, recall, and F1 score, it is essential to first define the following terms:  
* True positive (TP)--represents the number of correct predictions that identify the \"Fault\" condition;
* False positive (FP)--represents the number of incorrect predictions that identify the \"Fault\" condition;
* True negative (TN)--represents the number of correct predictions that identify the \"No Fault\" condition;
* False negative (FN)--represents the number of incorrect predictions that identify the \"No Fault\" condition.  
In the cases of a TP or FP, the ML model returns a '1', and in the case of a TN or FN, the model will return a '0'.  
Thus, it is possible to define accuracy as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{13}\]  
Accuracy is a suitable metric for balanced datasets, those in which the number of positive and negative samples is approximately equal. In the case of TrainDS, the dataset contains 1828 negative-class samples and 1772 positive-class samples, which reflects a balanced distribution.  
Precision represents how accurate the model is when it predicts a '1', and can be represented by the following equation:  
\[Precision=\frac{TP}{TP+FP} \tag{14}\]  
Recall represents how accurate the model is when the true class is predicted as a '1', and can be represented by the following equation:  
\[recall=\frac{TP}{TP+FN} \tag{15}\]  
Finally, we present the F1 score, a metric that represents a trade-off between precision and recall, and for this purpose calculates its harmonic mean. The F1 score offers a single metric that highlights cases where either the precision or recall is significantly low.  
\[F1\ score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{16}\]  
Figure 14 presents the various metrics across each cross-validation split.  
Figure 14: Assessment of model performance during cross-validation.  
Figure 14 clearly indicates that the model does not exhibit overfitting on the training dataset and demonstrates strong overall performance.
## 3 The Time Source Techniques
### Summary of Time Sources  
Among the four time sources discussed, all except pulsars utilize radio broadcast signals; pulsars emit natural electromagnetic radiation. While these sources share certain timing characteristics, their timing mechanisms and processes differ significantly.  
GPS and LEO are satellite-based sources, while pulsars are neutron stars. Signals from GPS, LEO, and pulsars travel through space to reach Earth, covering varying distances. GPS, LEO, and WWVB provide controlled, man-made signals, while pulsars emit naturally consistent signals with long-term stability.  
GPS, LEO, and WWVB broadcast time signals directly in formats such as timestamps or PPS (Pulse-Per-Second). In contrast, pulsar signals are natural pulses mixed with significant noise, requiring advanced signal processing and an initial timestamp from another source to generate usable time signals.  
GPS and LEO offer global coverage, and while pulsar signals can also cover the globe, this requires observing multiple pulsars from different directions and switching between them as Earth rotates. WWVB primarily serves the U.S., though coverage may vary with distance from the broadcast station. Similar time signal services exist globally, such as JJY in Japan and BPC in China.  
All four sources require receivers, but pulsar timing relies on large telescopes, making it less practical. This limitation highlights the need for robust time distribution systems in pulsar timing. GPS and pulsar receivers require outdoor installation, while LEO and WWVB receivers can function indoors, though WWVB may face reception challenges based on signal strength and location.  
In power grid applications, GPS is widely used for time synchronization but is susceptible to signal loss and interference. LEO, offering similar mechanisms, flexibility, and accuracy, is emerging as a strong candidate for time synchronization in these applications. Pulsar signals, while naturally emitted and stable over the long term, are less flexible and less accurate than GPS, though sufficient for certain applications. WWVB is easy to deploy but limited in both accuracy and geographic reach.  
A brief summary of the four time sources is shown in Table 2.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 2 Case Study: Applications to Pilbara Iron Ore Mine  
### Overview of the Pilbara Iron Ore Mine  
The Pilbara iron ore mine, located in the Pilbara region of Western Australia, is one of the world's largest producers and exporters of iron ore. The mine, owned by a subsidiary of multinational Rio Tinto [22; 23], is known for its extensive use of automation and digitalization. Pilbara's operations employ autonomous transport systems, drills, and trains,  
Figure 1: Mining technology evolution roadmap [21] (used with permission from Swedish Mining Innovation).  
Figure 2: Overall logic diagram.  
significantly reducing the need for on-site labor [24]. This high level of automation not only improves mine safety and operational efficiency, but also allows operators to remotely control vehicles and equipment from a central hub in Perth [25]. This approach sets a new standard for the mining industry and demonstrates the impressive potential for advanced technology to transform traditional mining practices. Despite Pilbara iron ore mine's advanced automation and digitalization, opportunities and needs for further improvement still remain. The mine continues to address issues related to geological modeling, intelligent systems for material tracking and probabilistic reasoning, mine planning and production scheduling, and robotic sensing and automation [26]. The main challenge is to obtain reliable estimates of uncertainty and average grade from sparse data, which is critical for confidently selecting high-grade ore. As high-grade hematite ore becomes increasingly depleted, the need for accurate and frequently updated geological models becomes extremely important [27; 28]. These models must reflect the current state of the mine, requiring the use of advanced stochastic models to effectively track material movement and mine status. Material tracking and planning also present significant challenges, especially in keeping grade control and planning tools up to date. These require intelligent systems that can dynamically track material movement and update the mine's digital twin. This task is further complicated by the challenges in reporting changes in ore grade and quality, especially as the industry increasingly relies on beneficiation processes to upgrade the lower-grade ores [28; 29]. In the field of robotics, achieving continuous autonomy is critical for ensuring resilience in unpredictable situations, such as harsh and hazardous environments. Interoperability and reliability of complex robotic systems in dynamic mining environments are critical for maintaining continuous operations. These systems must adapt to changing conditions in the mines, ensuring operations remain efficient and safe. In addition, improving the integration and coordination of different operational planning layers remains a pressing challenge [26]. These layers must be better coupled to improve overall operational efficiency, which directly impacts the productivity and sustainability of the Pilbara iron ore mining operations.
# Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review
## 2 JPEG and JPEG2000 Compression Effects on Medical Images
### Medical Images and Their Characteristics
#### 2.1.2 Location  
For natural images, the location of an object is not as crucial as in medical images. In most cases, the object is still the same object in general photography; however, in medical images, certain abnormal tissues are more likely to be present in specific parts of the human body, which can be a crucial piece of information for diagnosis and in ML.
## 4 Trajectory Sensitivity Analysis
### The Capacity Limit of the Converter  
In addition to considering the above constraint, the capacity limit of the converter is also considered. The higher the wind speed, the closer the output active power of the WTG is to the capacity limit. In order to ensure the safe operation of the WTG, the output electromagnetic power should be less than the capacity limit of the converter during the frequency regulation process. The upper limit of the virtual damping coefficient can be obtained from the expression of the output electromagnetic power and the constraint of the converter capacity limit, as shown below:  
\[D_{\text{vsg.2}}\leq\frac{\left(k\omega_{\text{r0}}-b-J_{\text{vsg}}\frac{d \Delta f}{dt}-K_{\text{vsg}}d-P_{\text{max}}\right)}{\Delta f_{\text{max}}}-K_{ \text{vsg}} \tag{27}\]  
Similarly, the upper limit of the virtual damping coefficient obtained from Equation (27) is the optimal value after considering the capacity limit of the converter.  
In conclusion, the speed limit of the rotor and the power limit of the converter should be considered in the evaluation of the frequency regulation ability of the WTG. The rotor kinetic energy is large at high wind speeds, but it is easily affected by the capacity limit of the converter during frequency regulation processes. When the wind speed is low, the converter has a large capacity margin, but the low rotor speed makes it easy for it to fall below the minimum allowable value, which may cause a secondary frequency drop. Therefore, the optimal value for the virtual damping coefficient under different operating conditions should satisfy both the rotor speed constraint and the capacity constraint of the converter. The optimal value of the virtual damping parameter \(D_{\text{vsg.opt}}\) is the smaller value in Equations (27) and (28):  
\[D_{\text{vsg.opt}}=\min\bigl{(}D_{\text{vsg.1}}\,,D_{\text{vsg.2}}\bigr{)} \tag{28}\]
## 3 Results and Discussion
### Thermal Stress Distribution Caused by the Subsurface Impurity Defects
#### 3.2.2 Effect of Impurity Defect Parameters on Its Induced Thermal Stress
[17; 25] that the laser damage density of silica optics is most closely correlated to the cerium content, followed by Al and Fe defects. The correlation coefficients of damage density to the Ce, Al and Fe impurity defects are 0.99, 0.16 and 0.001, respectively. Hence, the simulation results of temperature and stress in this work are qualitatively supported by the previously experimental results and the present results would offer potential theoretical explanations on the experimental observations of the effect of impurity defects on the laser damage resistance of fused silica optics.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 4 Simulation Experiments and Validation
### Simulation Analysis  
The position changes of the target group in the image plane are caused by several factors including the observation distance, direction of the optical axis, and relative motion between targets. We analyzed several satellites to describe the characteristics of the target  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target Type** & **Main Target** & **Target Type1** & **Target Type2** & **Target Type3** \\ \hline Shape & Cone & Sphere & Cylinder & Cone \\ Height/m & 1 & / & 1 & 1 \\ Radius/m & 0.25 & 0.5 & 0.25 & 0.25 \\ Material & Graybody & Graybody & Graybody & Graybody \\ Reflectance & 0.9 & 0.95 & 0.9 & 0.85 \\ Spin rate(revs/s) & 3 & 0 & 0 & 3 \\ Precession rate(revs/s) & 1 & 0 & 0 & 5 \\ Rolling rate(revs/s) & 0 & 1 & 1 & 0 \\ Precession angle(deg) & 3 & 0 & 0 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Targets infrared and attitude parameters.  
Figure 7: Simulation scenario from multiple perspectives: (**a**) Full view; (**b**) Satellite view; (**c**) Group view; (**d**) Target view.  
group under different conditions. Sat11, located in the first orbital plane, has observed the trajectory throughout the entire journey, and the distance between the targets did not change significantly. Both Sat27 in the second orbital plane and Sat47 in the fourth orbital plane face the direction of target dispersion and provide different views of the target's motion; this helps illustrate the motion more intuitively than the other satellites. Sat41 closely observed the target group from the side. Figure 8 shows the variation in distance from these satellites to the main target over time.
## 4 Conclusions
### Dimensionless Formulation  
We can rewrite the above equation in a non-dimensional formulation, using characteristic values of the single entities. This enables a better quantitative and qualitativecomparison between different geometrical variants. The radial velocity is then given as \(v_{r}=v_{c}\cdot v_{r}^{*}\), the radius as \(r=r_{0}\cdot r^{*}\), the pressure as \(p=p_{0}\cdot p^{*}\) and the \(z\)-coordinate becomes \(z=h\cdot z^{*}\), where \(v_{c}\) denotes a characteristic value for the velocity, \(r_{0}\) and \(h\) denote the geometry parameters and \(p_{0}\) is the input pressure.  
The dimensionless equation is then given by:  
\[\frac{\varrho v_{c}h}{\mu}\cdot v_{r}^{*}\frac{\partial v_{r}^{*}}{\partial r^ {*}}=-\frac{p_{0}h}{v_{c}\mu}\cdot\frac{\partial p^{*}}{\partial r^{*}}+\frac {r_{0}}{h}\cdot\frac{\partial^{2}v_{r}^{*}}{\partial z^{*2}}. \tag{11}\]  
We can identify the term \(\frac{\varrho v_{c}h}{\mu}\) as Reynold's number of the flow. Additionally defining \(\alpha\):=\(\frac{p_{0}h}{v_{c}\mu}\) and \(\beta\):=\(\frac{r_{0}}{h}\) dropping the asterisk, we finally arrive at:  
\[\text{Re}\cdot v_{r}\frac{\partial v_{r}}{\partial r}=-\alpha\frac{\partial p }{\partial r}+\beta\frac{\partial^{2}v_{r}}{\partial z^{2}}. \tag{12}\]  
Doing the same for the continuity equation and dropping the asterisks results in the same equation again, but then for dimensionless entities.
# XLNet-Based Prediction Model for CVSS Metric Values
## 4 Experiments and Results
### Comparative Study
#### 4.3.3 Comparison with Other Similar Works
\begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline
**Metric** & **Model** & **AV** & **AC** & **Au** & **C** & **I** & **A** \\ \hline \multirow{4}{*}{Accuracy} & XLNet & **0.9711** & **0.9169** & **0.9598** & **0.9072** & **0.9167** & **0.8946** \\  & Decision Tree & 0.9574 & 0.8767 & 0.9406 & 0.8630 & 0.8725 & 0.8505 \\  & K-Nearest Neighbors & 0.9440 & 0.8588 & 0.9255 & 0.8156 & 0.8270 & 0.8149 \\  & Multilayer Perceptron & 0.9229 & 0.7219 & 0.9252 & 0.6667 & 0.6871 & 0.6667 \\  & Naive Bayes & 0.9080 & 0.8714 & 0.9056 & 0.8161 & 0.8085 & 0.8065 \\  & Logistic Regression & 0.9633 & 0.8973 & 0.9533 & 0.8805 & 0.8899 & 0.8692 \\ \hline \multirow{4}{*}{Precision} & XLNet & 0.9216 & 0.8714 & 0.7571 & **0.8837** & **0.8928** & **0.8736** \\  & Decision Tree & 0.9569 & 0.8755 & 0.9400 & 0.8624 & 0.8720 & 0.8497 \\  & K-Nearest Neighbors & 0.9412 & 0.8564 & 0.9207 & 0.8129 & 0.8238 & 0.8128 \\  & Multilayer Perceptron & 0.8731 & 0.5525 & 0.8811 & 0.4611 & 0.4870 & 0.4533 \\  & Naive Bayes & 0.9095 & 0.8698 & 0.9038 & 0.8226 & 0.8152 & 0.8143 \\  & Logistic Regression & **0.9622** & **0.8974** & **0.9511** & 0.8787 & 0.8880 & 0.8672 \\ \hline \multirow{4}{*}{Recall} & XLNet & 0.8970 & 0.8106 & 0.7550 & 0.8831 & **0.8919** & 0.8750 \\  & Decision Tree & **0.9574** & 0.8767 & 0.9406 & 0.8630 & 0.8725 & 0.8505 \\  & K-Nearest Neighbors & 0.9440 & 0.8588 & **0.9254** & 0.8156 & 0.8270 & 0.8149 \\  & Multilayer Perceptron & 0.9229 & 0.7219 & 0.9252 & 0.6667 & 0.6871 & 0.6667 \\  & Naive Bayes & 0.9080 & 0.8714 & 0.9056 & 0.8160 & 0.8085 & 0.8065 \\  & Logistic Regression & 0.8973 & **0.9533** & 0.8805 & **0.8899** & **0.8692** & **0.8973** \\ \hline \multirow{4}{*}{F1} & XLNet & 0.9086 & 0.8313 & 0.7560 & **0.8834** & **0.8923** & **0.8743** \\  & Decision Tree & 0.9571 & 0.8760 & 0.9403 & 0.8627 & 0.8722 & 0.8500 \\ \cline{1-1}  &
## 3 Analysis of Shield Tunneling Parameters
### Grouting Amount  
The correlation between the grouting amount and the shield tunnel ring number is illustrated in Figure 8. Table 4 presents the statistical characteristics of the grouting amount across three distinct stratum conditions.  
The analysis of Figure 8 and Table 4 reveals that, overall, the grouting amount re-mains relatively consistent across the different strata. It consistently fluctuates around the average value, and the grouting amounts for the three strata are comparable. Specifically, the grouting amount is consistently maintained within the range of 18,641.8 to 19,718.9 L.