esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model
When considering emerging self-limiting infectious diseases, we let the sum of the time from the onset of the disease to self-healing and the time from the onset of the disease to death for mildly and severely infected individuals be \(T_{I_{1}}\) and \(T_{I_{2}}\), respectively. The sum of the time from isolation to self-healing and the time from isolation to death for mildly and severely hospitalized isolated were \(T_{H_{1}}\) and \(T_{H_{2}}\), respectively. According to the characteristics of emerging self-limiting infectious diseases, it could be assumed that \(r_{I_{1}}+d_{I_{1}}=\frac{1}{T_{I_{1}}},r_{I_{2}}+d_{I_{2}}=\frac{1}{T_{I_{2}}},r_{H_{1}}+d_{H_{1}}=\frac{1}{T_{H_{1}}}\) and \(r_{H_{2}}+d_{H_{2}}=\frac{1}{T_{H_{2}}}\). Based on the above transformation diagram, a differential equation model could be established as follows:  
\[\left\{ \begin{array}{l}\dot{S}=-\beta S\frac{\eta E+I_{1}+I_{2}}{N},\\ \dot{E}=\beta S\frac{\eta E+I_{1}+I_{2}}{N}-\alpha E,\\ \dot{I_{1}}=\alpha qE-v_{1}I_{1}-r_{I_{1}}I_{1}-d_{I_{1}}I_{1}=\alpha qE-v_{1} I_{1}-\frac{1}{T_{I_{1}}}I_{1},\\ \dot{I_{2}}=\alpha(1-q)E-v_{2}I_{2}-r_{I_{2}}I_{2}-d_{I_{2}}I_{2}=\alpha(1-q)E- v_{2}I_{2}-\frac{1}{T_{I_{2}}}I_{2},\\ H_{1}=v_{1}I_{1}-r_{H_{1}}H_{1}-d_{H_{1}}H_{1}=v_{1}I_{1}-\frac{1}{T_{H_{1}}}H _{1},\\ \dot{H_{2}}=v_{2}I_{2}-r_{H_{2}}H_{2}-d_{H_{2}}H_{2}=v_{2}I_{2}-\frac{1}{T_{H_{ 2}}}H_{2},\\ \dot{R}=r_{I_{1}}I_{1}+r_{I_{2}}I_{2}+r_{H_{1}}H_{1}+r_{H_{2}}H_{2},\\ \dot{D}=d_{I_{1}}I_{1}+d_{I_{2}}I_{2}+d_{H_{1}}H_{1}+d_{H_{2}}H_{2}.\end{array} \right. \tag{1}\]  
\(S(0)=S_{0},E(0)=1,I_{1}(0)=I_{2}(0)=H_{1}(0)=H_{2}(0)=R_{1}(0)=R_{2}(0)=0,N=S_{ 0}+E(0)\).
###### Abstract  
The bonding properties between rubber granule-basalt fiber composite modified concrete (RBFC) and rebar greatly impact the load-carrying capacity, stiffness, and crack development of RBFC structures. In this paper, the effects of rebar diameter, bonding length, and concrete type on the bonding properties between RBFC and rebar were investigated using center pull-out tests. The bond stress-slip curve as well as the bond strength and its influencing factors were discussed in detail, and a semi-theoretical and semi-empirical model of RBFC with rebar was established. According to the findings, when rubber granules were added to concrete, its bond strength with rebar decreased. At a dosage of 5%, the bond strength was reduced by approximately 4% compared to ordinary concrete (OC) under the same conditions. It was shown that the addition of small amounts of rubber granules did not significantly reduce the bond strength. On the other hand, the incorporation of an appropriate amount of basalt fibers had a positive effect on the bond strength. An admixture of 4.56 Kg/m\({}^{3}\) of fibers increased the bond strength by 3% compared to OC under the same conditions. The bond strength of RBFC with these two additions was improved by approximately 2% compared to OC under the same conditions. When the bonding length was 60 to 100 mm, the ultimate bond strength decreased with increasing bonding lengths. The bond strength decreased by 13.91-16.72% for every 20 mm increase in bonding length. When the rebar diameter was 12 to 16 mm, the ultimate bond stress decreased as the rebar diameter increased. The bond strength decreased by 3.96-5.94% for every 2 mm increase in rebar diameter. The segmental bond-slip constitutive model between RBFC and rebar, established using the results of the center pull-out test, can provide a reference basis for engineering applications of RBFC.  
Keywords:basalt fiber; modified concrete; bond-slip constitutive model; rubber granule +
Footnote †: journal: Applied Sciences
## 5 Rotor Temperature Modeling and Prediction
### Rotor Temperature Modeling and Prediction Under Small Samples
Finally, EO-SVR tends to underpredict in high-temperature regions and overpredict in low-temperature regions, indicating significant prediction biases across different temperature ranges. In contrast, RIME-XGBoost demonstrates more stable and reliable predictions across all temperature ranges. Figure 7 presents a dual-Y-axis boxplot using the R-squared and RMSE of the test set. It visually illustrates the prediction errors of each model. The R-squared distribution of SMA-RF is too wide, ranging approximately from 0.78 to 0.96. Its reliability is low, as its predictions are unstable in practical applications. RIME-XGBoost has the highest mean and median R-squared values. Its box length is the shortest, indicating excellent prediction consistency. The RMSE shows similar results: the mean RMSE of RIME-XGBoost is around 0.6, while that of SMA-RF is around 0.9. EO-SVR demonstrates good prediction stability, but its mean and median RMSE are approximately 0.7. SO-BiGRU is a moderate hybrid model, with relatively small error values but significant variability. Figure 6: Scatter plot comparing true and predicted values across training and test sets. In the small-sample experiments, RIME-XGBoost and EO-SVR are the two standout models. The former performs exceptionally well on the training set and demonstrates strong generalization on the test set. The latter has the highest computational efficiency but slightly lower overall accuracy. SMA-RF and SO-BiGRU fail to deliver satisfactory test results. The stark contrast between the model's near-perfect training performance (R\({}^{2}\) = 0.9999) and its lower test set accuracy (R\({}^{2}\) = 0.9491) signals a classic case of overfitting. This gap likely stems from excessive model complexity or inadequate regularization, where the algorithm memorizes training data patterns rather than learning generalizable trends. To bridge this performance divide in RIME-XGBoost, targeted adjustments can enhance generalization without sacrificing predictive power. While RIME already optimizes core hyperparameters like tree quantity, depth, and learning rate, introducing additional regularization constraints may curb overfitting. For instance, setting a minimum loss reduction threshold prevents trees from splitting on insignificant or noisy patterns. Increasing weight penalty terms discourages the model from relying excessively on individual features. Furthermore, not all features contribute equally to generalization. By systematically retaining high-impact predictors while discarding redundant or irrelevant variables, the pruning process reduces the risk of spurious correlations. When implemented in concert, these strategies contribute to simplifying the model architecture and improving generalization.
## 4 Experimental Section and Results Analysis
### Dataset  
The production of photovoltaic-grade germanium single crystals using the Czochralski process [31] primarily includes seeding, necking, shoulder formation, shoulder rotation, radial growth and tailing. A seed crystal is positioned above the molten germanium ingot and under controlled temperature conditions, and it is immersed into the molten germanium to induce non-uniform nucleation. By designing an appropriate thermal field, both longitudinal and radial temperature distributions are effectively managed. During the radial growth stage, the seed crystal is rotated at a specific speed and pulled upward, allowing the newly solidified crystal to gradually grow into a single crystal. Of particular importance in crystal growth is the radial growth stage. We collected data from industrial equipment. To effectively build the model, we selected four datasets that represent relatively complete and valid data from the radial growth stage. Four datasets are denoted as K1 (1123 data points), K2 (400 data points), K3 (2015 data points) and K4 (1501 data points). Given that the primary parameters affecting germanium single-crystal growth are the crystal pulling rate (mm/h), crystal length (mm) and average liquid surface temperature (\({}^{\circ}\)C), these three parameters are chosen as the three-dimensional features for the model input. The objective of the experiment is to provide recommendations for the crystal growth pulling rate to maintain the uniform radial growth of the single crystal.
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 2 Synthesis Methods for Copper- and Iron-Based Nanoflowers
### Biomolecule Immobilization  
In recent years, extensive research has concentrated on the development of biomolecule-integrated nanostructures for a range of potential applications in biomedical sciences. Many of these approaches utilize enzymes (Figure 7), which are known for their exceptional activity and specificity. However, like most biomolecules, they are highly susceptible to various environmental factors. To enable their application in large-scale reactions across different industries, enzymes have been immobilized. This immobilization enhances their stability, reusability, and enantioselectivity while simplifying product purification, making them a more economically viable option [93]. As a result, the immobilization of enzymes has garnered significant interest from researchers and the scientific community due to their extensive range of applications [94].  
The concept of biomolecule-embedded nanomaterials, where biomolecules are immobilized within nanostructured materials, is a promising area of research that improves the stability, activity, and selectivity of the biomolecules. Traditionally, biomolecules have been covalently bonded, trapped, or physically adsorbed onto various supports like bulk materials, beads, membranes, and fibers. However, improper immobilization can reduce biomolecule activity due to issues like blocked active sites, mass transfer limitations, and  
Figure 7: Hybrid employed techniques for synthesizing inorganic–organic nanoflowers.  
conformational changes. Recently, organic-inorganic hybrid nanoflowers and biomolecule-encapsulated metal-organic frameworks (biomolecule@MOF (CD BioSciences, Shirley, NY, USA)), formed through self-assembly, have attracted significant attention in this field.  
These composites provide a straightforward synthesis, high efficiency, and considerable potential to enhance the stability, activity, and selectivity of biomolecules, outperforming traditional immobilization methods. Biomolecules, such as proteins, strongly bind to metal ions, mainly through the coordination of amide groups in the protein backbone [95].  
[PERSON] et al., synthesized triple enzyme-inorganic hybrid nanoflowers (TrpE@ihNFs) by combining \(\alpha\)-amylase, lipase, and protease with Cu\({}^{2+}\) ions. They compared the enzyme activity and stability of TrpE@ihNFs with those of the free enzymes under varying pH levels and temperatures using spectrophotometric methods. The results showed that the enzyme activities and stability of TrpE@ihNFs were significantly superior to those of the free enzymes [96].
# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 4 Discussion
### Experimental Setup or Environment  
To evaluate our method, we compared its domain adaptation capabilities in object detection with state-of-the-art methods, including 2 PCNet, TDD, and UMT [47]. Additionally, we demonstrated the robustness of our nighttime-to-daytime image translation module.
## 4 Discussion of Limitations
### Prospective Test Scenarios  
Accident databases provide only a retrospective view on traffic accidents. [PERSON] et al. present a method for predicting collision configurations in vehicles with crash avoidance systems and identifying the changes in new collision configurations [34]. A lot of prospective studies utilize retrospective accident scenarios to create a new prospective test scenario catalog by using virtual simulations. As addressed by SOTIF, even after the data acquisition and test sequences extraction, there is still a possibility of missing relevant scenarios, especially unknown unsafe scenarios. Therefore, continuous traffic data collection and analysis is still necessary, besides prospective simulations studies.
# Study on the Influence of a Rigid Wall on Cavitation in Underwater Explosions Near the Free Surface
## 3 Results and Discussions
### Effect of Rigid Wall on Small-Charge Underwater Explosion near Free Surface
Figure 3: Schematic of underwater explosion near a free surface with a rigid wall (**a**) and mesh strategy of the fluid domain (**b**). Every 1 in 10 lines is plotted in the right figure. Figure 4: Numerical results of density (**top**), pressure (**middle**), and cavitation domain (**bottom**) under the condition of an underwater explosion near the free surface without a rigid wall at \(t\) = 0.167, 0.333, 0.5, 0.666, 10, 4.17, 6.17, and 8.17 ms (from left to right). The red area represents the cavitation domain in the bottom contour plots. Figure 5: Numerical results of density (**top**), pressure (**middle**), and cavitation domain (**bottom**) under the condition of an underwater explosion near the free surface with a rigid wall at \(t=0.167,0.333,0.5,0.666,1.0,4.17,6.17,\) and \(8.17\) ms (from left to right). The red area represents the cavitation domain in the bottom contour plots. Figure 6 presents a comparison of the total volume variation in the cavitation domain with and without a rigid wall over time. It is evident that in an underwater explosion environment near the free surface, the growth and collapse processes of the cavitation domain are constrained by the presence of the rigid wall. This is because, while the explosion shock wave propagates to the water surface and reflects as a rarefaction wave, it also reflects as a new compression wave on the rigid wall. This compression wave continues to propagate towards the cavitation domain, causing its expansion and accelerating the collapse of cavitation. Figure 7 displays the pressure-time history curves at underwater measurement points located at depths of 0.13 m and 0.4 m from the axis of symmetry, respectively, under the aforementioned two conditions. In the left picture of Figure 7, it can be observed that cavitation collapses earlier in the presence of a rigid wall, with the collapse pressure significantly higher than that without a rigid wall. The maximum cavitation pressure increases from 0.98 MPa to 3.7 MPa. The right figure in Figure 7 demonstrates that the reflected shock wave reaches the measurement point at \(t=0.4\) ms due to its distance of 0.5 m from the wall. Additionally, the existence of the rigid wall causes cavitation to collapse at an earlier stage. Overall, these findings highlight the influence of a rigid wall on the cavitation phenomenon during underwater explosions near the free surface. The presence of the wall affects the cavitation domain size, collapse timing, and collapse pressure, emphasizing the importance of considering such factors in underwater explosion scenarios. Figure 6: Comparison of the volume evolvement in cavitation domain under two cases. Figure 7: Pressure–time history curve of the measurement points at distances of 0.2 m (**a**) and 0.4 m (**b**) from the symmetry axis, at a depth of 0.13 m from the free surface.
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 3 GNSS Orbit Repeat Period
### Bds  
The BDS space segment consists of satellites deployed in three different types of orbit [25]. The three orbit types are geostationary Earth orbit (GEO), inclined geosynchronous satellite orbit (IGSO), and medium-Earth orbit (MEO). The GEO satellites operate at an altitude of 35,786 km over the equator, and are positioned at 58.75\({}^{\circ}\) E, 80\({}^{\circ}\) E, 110.5\({}^{\circ}\) E, 144\({}^{\circ}\) E, and 160\({}^{\circ}\) E. Thus, three to five BDS satellites can always be observed from many Asian countries, including Korea. The orbit plane of IGSO satellites is inclined at 55\({}^{\circ}\) to the equatorial plane, although it is located at the same altitude as GEO satellites. The ascending nodes of IGSO satellites are located at 95\({}^{\circ}\) E and 118\({}^{\circ}\) E. MEO satellites are deployed in 21,528 km altitude orbits inclined at 55\({}^{\circ}\) to the equator. MEO satellites have an orbit period of approximately 12 h and 53 min, so the satellite ground tracks repeat every seven days. During the period, MEO satellites make 13 revolutions.  
The repeat time of a BDS satellite is a variable quantity depending on altitude and orbit type. Figure 2 shows X components of the satellite position by orbit type during an eight-day period. As can be seen from Figure 2a,b, GEO and IGSO satellites go through exactly the same tracks with a slight time lag. However, MEO satellite PRN #11 in Figure 2c shows a different trajectory from the other two. After seven days, the eighth orbit is similar to that of the first day.  
Repeat times of GEO and IGSO satellites were calculated based on three-day data prior to the desired date. On the other hand, a repeat time for the MEO satellite was obtained by using data from 7 and 14 days previously. The reason for using only two-day data was that the MEO satellite has a longer repeat period than the other types, thereby making atmospheric conditions significantly  
Figure 2: Eight-day time series of the X coordinates of satellite positions according to the BeiDou orbit type: (**a**) PRN-C01 GEO satellite; (**b**) PRN-C06 IGSO satellite; (**c**) PRN-C11 MEO satellite. The number inside each plot refers to the sequential order of dates of observations. Each color line is a different day.  
different. From computation of cross-correlations, we found that GEO and IGSO satellites have repeat times in the range of 217-252 s, whereas MEO satellites have a repeat time of around 25 min (1490-1539 s) after shifting seven days.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Plasma Irradiation  
Plasma has been employed to remove impurities and contaminants from surfaces by the collision energy of gas molecules and the chemical action on impurities and contaminants. Various gases (such as argon, oxygen, hydrogen, and nitrogen, as well as their mixtures) have been employed to carry out the cleaning procedures. The plasma is generated by using high-frequency voltages (typically kHz-MHz) to ionize the working gases. The activated species in plasma, including atoms, molecules, ions, electrons, free radicals, metastables, react with contaminants on surfaces. Short-wave ultraviolet (vacuum UV), whose energy is very effective to break most organic bonds of surface contaminants, is produced in plasma too. The technique has been utilized to modify surfaces of MoS\({}_{2}\) few-layers in recent years.
# Goldmann Tonometry and Corneal Biomechanics
## 2 Materials and Methods
### Tonometry Test  
The intraocular pressure detected by the Goldmann explanation tonometer (GIOP) was compared to the intraocular pressure (TIOP) invasively measured by a pressure transducer (140205D, Honeywell, Freeport, IL, USA) in 49 swine eyes. Each eye was clamped in a custom-made Plexiglass eye holder placed in vertical position on the chin rest of the slit lamp with the corneal apex in front of Goldmann tonometer, simulating the clinical measurement of IOP. A needle was cannulated at the level of limbus directly into the anterior chamber of the eye and connected to the transducer by a connecting tube and a stopcock. The transducer in turn was connected to a power supply (KAT 5 VD, Kert, Caerano di San Marco (TV), Italy). The pressure transducer signal was measured by a multimeter in mV and then converted in mmHg through a mercury sphygmomanometer. A syringe connected to the stopcock was used to gradually increase intraocular pressure by saline infusion, added in steps of 100 \(\mu\)L, up to 500 \(\mu\)L. While infusing the saline solution, through the inspection system at variable magnifications and the optical system of the slit lamp, an explanation area of 7.35 mm\({}^{2}\) was obtained under the Goldmann tonometer. For each saline infusion, TIOP and GIOP were recorded, until the achievement of a TIOP of about 35 mmHg.  
The needle used was chosen with the lowest possible diameter (30 gauge), in order to avoid damage to the corneal tissue, once inserted into the eye anterior chamber. The procedure of inserting the needle was carried out with particular care to avoid corneal injuries that can prevent the measurement of IOP by Goldmann tonometry or cause false assessments. Care was taken to avoid leaks in the connection circuit between the transducer and the eye and to minimize pressure losses in the pipes, by using only one tap and a short connecting pipe. Figure 1 is a schematic representation of the IOP measurement setup.  
The time between the saline infusion and measurement by Goldmann/transducer was as short as possible (less than one minute) to avoid relaxation effects due to the viscoelastic properties of the ocular tissues [52] that would result in IOP values lower than the actual. During the measurement procedure eyes were kept hydrated by dropping hydrating solution (sodium hyalurone 0.2%, distilled water). Fluorescein (1 mg fluorescein sodium) was used to visualize the two GAT semi-circles by blue light.
## 4 Future Perspectives
### Airborne Laser Scanning  
Elevation models of very fine resolution (at 0.1 m level) and high relative precision (\(<\)0.1 m) will be needed for performing GNSS-R analysis over steep or complex terrain, such as in alpine environments. The primary technology that can potentially satisfy such criteria is airborne laser scanning. The performance of commercially available systems utilizing laser scanning technology has increased at an astonishing pace, including a significant reduction of the acquisition time. Surprisingly, rapid development of the accompanying software-tools for automation of data processing, modeling and quality monitoring has not followed. Airborne Laser Scanning (ALS) requires concurrent employment of at least two navigation technologies due to the relatively complicated generation of the laser point-cloud coordinates [121], while the subsequent classification and calculation of digital elevation models (DEM) is even more involved. The algorithmic part of current state-of-the-art research focuses first on the real-time generation of the laser point cloud from moving platforms [122; 123]. The second research area develops and implements the classification algorithms (possibly in real-time), which are necessary for the production of digital terrain models (DTM) and for the assessment of scanning geometry, which influences the quality of ALS-derived products [124]. The third research subject treats the obtained mapping accuracy relative to the scanning-target [125] as well to the derived DTM [126].