esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### TGA and DSC Experiments  
For the TGA and DSC experiments, the polymeric materials were prepared as films by using a mixture composed of the monomer of interest, divinylbenzene (DVB) as crosslink (25% in weight relative to monomer), and 2-hydroxy-2-methylpropiophene as photoinitiator (PI) (5% in weight relative to monomer). Each mixture was transferred into a silicon mold and placed under the UV light (Dymax ECE Flood Lamp 5000 equipped with a metal halide bulb--UVA, Dymax, Torrington, CT, USA) to induce polymerization. The thermal stabilities and decomposition temperatures of the prepared materials were measured using a TGA Q50, while the glass transition temperatures were determined using a DSC Q200 differential scanning calorimeter, both from TA Instruments (TA Instruments, New Castle, DE, USA).  
The TGA experiments were performed in aluminum pans and under N\({}_{2}\) atmosphere using the following temperature program: (1) heating from room temperature up to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\); (2) cooling to room temperature; (3) heating from room temperature to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\); (4) isothermal at 250 \({}^{\circ}\)C during 30 min; and (5) heating up to 500 \({}^{\circ}\)C at 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\).  
Prior to DSC experiments, the prepared films were placed in an oven at a temperature of 200 \({}^{\circ}\)C during 30 min. Then, the films were transferred to aluminum pans and under N\({}_{2}\) atmosphere, the following DSC temperature program was used: (1) cooling from room temperature to \(-\)70 \({}^{\circ}\)C; (2) heating from \(-\)70 \({}^{\circ}\)C to 210 \({}^{\circ}\)C, and then; (3) cooling from 210 \({}^{\circ}\)C to \(-\)70 \({}^{\circ}\)C, at a heating/cooling rate of 1 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\). Three cycles were performed for each sample.
# On the Integration of VLBI Observations to GENESIS into Global VGOS Operations
## 3 Materials and Methods
### Analysis  
In the analysis, the target parameters are estimated from the simulated observations with a least-squares adjustment. As presented in Section 2, we follow two processing strategies with individual analysis. In the VGOS processing, the troposphere is estimated as zenith wet delays every 10 min as piece-wise linear offsets (PWLO) with a relative constraints of 1.5 cm. North and east gradients are estimated as PWLO every 20 min with relative constraints of 0.05 cm. The clock behavior is parameterized with a quadratic term and PWLO every 60 min with relative constraint of 1.3 cm. All five EOPs, namely two polar motion parameters, two nutation parameters, and dUT1, as well as the station positions are estimated with a single value per session. To resolve the rank deficiency, the datum definition is realized using NNT and NNR constraints over all stations. The source positions are estimated for sources observed in at least three scans and with at least 25 observations [25]. The GENESIS processing considers observations of both geodetic sources and GENESIS. In contrast to VGOS processing, EOPs and geodetic source positions are not estimated but fixed to a priori values. Furthermore, station position estimates are only derived from observations of GENESIS. That way, the coordinates are defined through the satellite frame. No datum constraints are necessary. The estimates of the 1000 simulation runs are used to calculate repeatability values for each parameter. These repeatability values are calculated as standard deviation and represent the expected accuracy of a parameter.
# Experimental Investigation on a Throttleable Pintle-Centrifugal Injector
## 1 Introduction
Similarly, investigations into LOX/GCH\({}_{4}\) pintle injectors have revealed enhancements in atomization quality and flame stability, critical for efficient combustion [18; 19]. Green propellant alternatives have also been pursued, with ethanol-nitromethane mixtures achieving specific impulses rivaling hydrazine, while nitromethane-based designs offer low-toxicity solutions for air turbo ramjet applications [20]. Optimization efforts with hydroxylammonium nitrate (HAN)-based injectors have yielded improved regression rates and faster response times, underscoring their adaptability to hybrid configurations [21; 22; 23]. Advances in additive manufacturing have enabled the fabrication of complex injector geometries, further enhancing performance [1], while numerical simulations have provided deeper insights into combustion dynamics, facilitating design refinement [24; 25]. Despite these strides, the integration of pintle and swirl features into monopropellant hybrid systems remains a relatively uncharted domain, with unresolved challenges in achieving seamless synergy between throttling precision and combustion enhancement [26; 27; 28]. This gap underscores the need for innovative injector designs that can holistically address the multifaceted demands of hybrid propulsion, leveraging the strengths of both approaches to overcome the limitations of traditional systems. The novelty of this study lies in the introduction of a throttleable pintle-centrifugal injection system, specifically designed for hybrid rocket motors. This new system integrates the benefits of both swirl injection and pintle injection, addressing the critical need for improved combustion efficiency and throttling control. The research investigates the performance of this hybrid injector system, focusing on its ability to control oxidizer flow rate and enhance combustion dynamics in hybrid propulsion systems. This study aims to fill this gap by exploring how the combination of swirl and pintle injectors can improve the performance of hybrid rocket motors and contribute to the development of more efficient and controllable propulsion systems for future aerospace missions.
# Removal of Sodium Dodecylbenzenesulfonate by Macroporous Adsorbent Resins
## 3 Results
### Effect of Adsorption Time  
The variation in the efficiency of SDBS removal by the MARs according to adsorption time is shown in Figure 3. The efficiency of SDBS removal using HP20 and HP2 MGL increased rapidly and became saturated after around 40 min adsorption time. When both adsorbents (20 mg/mL) were used, 82% SDBS removal was achieved before 40 min, followed by 90% after 90 min at an SDBS concentration of 0.1 mg/mL (100 ppm). The initial adsorption occurred in the macropores of the MARs in the early stages of adsorption, and the rate of adsorption decreased to the equilibrium, indicating that the macropores were easily filled with SDBS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Adsorbents** & **Surface Area (m\({}^{2}\)/g)** & **Pore Volume (mL/g)** & **Pore Radius (Å)** \\ \hline HP20 & 614.1642 & 1.0140 & 66.0466 \\ HP2 MGL & 594.0658 & 1.0862 & 73.1393 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Surface Properties of HP20 and HP2 MGL.  
Figure 1: FT-IR spectra of HP20 and HP2 MGL.  
Figure 2: SEM images of **(a,b)** HP20 and **(c,d)** HP2 MGL.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 4 CenTri: Reinforcement Learning Model and Algorithm
### Scenario 2  
In the second scenario, as shown in Figure 7, PUs reappear in routes \(k_{3}\) (\(fc_{s}-fc_{1}-fc_{4}-fc_{7}-fc_{4}\)) and \(k_{4}\) (\(fc_{s}-fc_{2}-fc_{5}-fc_{8}-fc_{d}\)). \(PU_{1}\), \(PU_{2}\), and \(PU_{3}\) interfere with channels \(c_{2}\), \(c_{6}\), and \(c_{3}\), respectively. Specifically, two channels, \(c_{2}\) and \(c_{6}\) of route \(k_{3}\) and channel \(c_{3}\) of route \(k_{4}\), are occupied by PUs. The source node selects the D2D route \(k_{2}\) rather than the backbone route \(k_{1}\).
# Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology
###### Abstract
However, because both the denoising and noise addition processes must be performed on full-resolution images [16; 17], they demand substantial computational resources. To address this issue, [PERSON] et al. [18] proposed latent diffusion models (LDMs), which compress the processing of pixel images into a latent space that is perceptually equivalent to the original image space but has significantly lower computational complexity. This approach enables high-fidelity data reconstruction while reducing the model's computational resource requirements [19]. Consequently, LDMs can produce high-quality images while demanding fewer computational resources [20; 21] and have demonstrated significant application value in the field of mining engineering. However, in real-world mining scenarios, ore blocks generated through blasting and caving exhibit diverse shapes and complex textures. Public datasets used to train LDMs typically contain simpler patterns, which limits the model's ability to effectively handle the complexity of ore blocks. As a result, there is a certain degree of bias in the model's perception of ore block information when applied to mining engineering. To address this issue, this study proposes a novel approach--the intelligent ore block shape prediction model (IOBSPM) based on LDMs, which derives global morphology from local perceptual information. The primary technical contribution of this research is the application of LDMs to mining engineering. The model is specifically designed to handle the complex shapes and textures of ore blocks, which are not well represented in existing public datasets. This approach overcomes the limitations of current view synthesis methods in complex environments, significantly improving the accuracy and computational efficiency of ore block morphology prediction. Additionally, this research lays the foundation for future applications of deep learning techniques in remote-controlled precision shoveling and autonomous shoveling operations.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 5 Design Considerations for High-Gradient Accelerating Structures
### Materials  
Currently, a single-cell cryogenic copper cavity holds the record for the highest accelerating gradient at a low breakdown rate [98], and work continues to be conducted to apply this finding to practical accelerating structures [15; 60]. However, the complexity of running a linac in a cryostat prevents this method from quickly developing into practical applications. I think that this method has great potential, but it will require a sizeable investment before we see its application in science and industry.  
I have found that the next best option to cryo structures are cavities made from CuAg with a silver concentration of 0.8%. There is an ongoing effort to explore CuAg alloys with different concentrations, which have not yet produced experimental results [117]. In my experiments, both high-temperature brazed and hard CuAg cavities performed better than soft copper, but hard CuAg had lowest breakdown rates; see, for example, Figure 7 and [14]. I note that the cost of CuAg with a low concentration of silver is not significantly higher than the cost of the usual linac material, oxygen-free high-conductivity copper, and the technological properties are very close to the copper. Therefore, I conjecture that CuAg will find its use in practical high-gradient linacs.  
I have achieved the next best results with hard copper. Practical applications of the hard, non-brazed, diffusion-bonded copper depend on the development of robust joining techniques, which preserve the vacuum and electrical properties of high-performance linacs. This work is ongoing with numerous experimental results [32; 82; 84; 85; 86].
## 2 Methods and Material  
### Thermodynamic Calculations  
As a first step to determine how to eliminate brownmillerite, thermodynamic calculations in the software FactSage [26] were conducted. The databases FIoxid-SLAGA, FToxid-SPINA, FToxid-MeO_A, FToxid-Mel_A, and FToxid-C2 AF were used. The gas phase was suppressed but all possible pure solids from FactPS and FToxid were included, with the activity of Fe set to 1 to simulate slag and molten steel in equilibrium. The thermodynamic calculations indicated that basicity B2, the CaO/SiO\({}_{2}\) ratio, affects the formation of brownmillerite. The correlation of basicity and chromium leaching has been noted by [PERSON] et al. who stated that chromium leaching increased as the basicity increased [24]. In addition, [PERSON] et al. no longer detected brownmillerite in the slag samples after the basicity of the slag was decreased by quartz addition [27]. Studies by [PERSON] and [PERSON] [28] and [PERSON] et al. [29] also connected the basicity and chromium leaching.  
Figure 1 shows the solidification of the chromium-containing minerals of a slag at basicity 2.5. The first mineral to form is magnesiowustite, followed by spinel, and lastly brownmillerite. All the minerals start to form while there is still molten slag present. According to thermodynamic calculations, decreasing the basicity in the investigated low-alloy EAF slag compositions leads to brownmillerite forming at a lower temperature, when all of the melt has solidified. As a result of kinetics, reaching equilibrium and forming minerals takes longer in a solid than in a liquid. If the cooling rate exceeds the formation rate of brownmillerite, brownmillerite is not present in the slag. Thereby, two methods for avoiding brownmillerite are possible: decreasing the basicity to lower the temperature of formation or increasing the cooling rate. If the basicity is decreased too much, the mineral merwinite is formed in the thermodynamic calculations. Merwinite can contain chromium and dissolves in water and is therefore not a suitable substitute for brownmillerite and should be avoided to prevent chromium leaching [30]. Thermodynamic calculations of slag of different compositions were used to obtain the basicity, which would prevent brownmillerite and merwinite formation in most compositions. According to the calculations, there is a basicity span of around 2.2 to prevent both brownmillerite and merwinite; the size and location of the span differs depending on the slag composition.
## Appendix D Correlation Estimation and Interpretation by a Clinical Expert  
In this section, we outline the reference guide, which was followed by a clinical expert in correlation estimation and interpretation. It is important to note that the expert guidanceprovided is a general framework and not a rigid protocol. We fully acknowledge that practitioners may identify opportunities for case-specific improvements.  
Clinical Expert Reference Guide for Correlation Estimation and Interpretation  
1. Purpose of the Correlation Matrix  
We are asking you, as a clinical expert, to assess the relationships between health-related variables based on your clinical knowledge and experience with a specific patient population. These assessments will be used to build a scientific model that reflects real-world clinical patterns. You do not need to perform any calculations, just provide your judgment about whether each pair of variables is related, and if so, how strongly and in which direction.  
2. What Is Correlation?  
Correlation tells us how closely two variables move together in a linear pattern:  
Positive Correlation: When one variable increases, the other tends to increase as well.  
Negative Correlation: When one variable increases, the other tends to decrease.  
No Correlation: No consistent or meaningful pattern exists between the two variables.  
3. Key Concept: Scatter Determines Correlation (Not Slope)  
When visualized on a graph (called a scatter plot), each pair of values (e.g., age and blood pressure) is shown as a dot. The correlation strength depends on how tightly the dots cluster around an imaginary straight line.  
4. How to Fill the Matrix  
For each pair of variables in the matrix:  
Step 1: Think About the Direction  
Do both variables tend to increase or decrease together? \(\rightarrow\) Positive  
Does one tend to increase while the other decreases? \(\rightarrow\) Negative  
Not sure, or no consistent pattern in your experience? \(\rightarrow\) No/Unsure  
Step 2: Think About the Strength  
If you choose Positive or Negative, assess how consistent this relationship is:  
\begin{tabular}{l l} \hline
**Strength** & **Your Clinical Interpretation** \\ \hline
**Weak** & \multicolumn{1}{c}{Slight trend, many exceptions} \\ \hline
**Moderate** & \multicolumn{1}{c}{Clear trend, but not always consistent} \\ \hline
**Strong** & \multicolumn{1}{c}{Very consistent relationship in most or all patients} \\ \hline
5. Translation into Numeric Values & \multicolumn{1}{c}{} \\ \multicolumn{1}{c}{Your input will be translated into numerical values for analysis as follows:} \\ \hline
**Clinical Input** & \multicolumn{1}{c}{**Numeric Value**} \\ \hline
**No/Unsure** & \multicolumn{1}{c}{0.00} \\ \hline
**Weak Positive** & \multicolumn{1}{c}{+0.15} \\ \hline
**Moderate Positive** & \multicolumn{1}{c}{+0.25} \\ \hline
**Strong Positive** & \multicolumn{1}{c}{+0.55} \\ \hline
**Weak Negative** & \multicolumn{1}{c}{\(-0.15\)} \\ \hline
**Moderate Negative** & \multicolumn{1}{c}{\(-0.25\)} \\ \hline
**Strong Negative** & \multicolumn{1}{c}{\(-0.55\)} \\ \hline \end{tabular}
## 1 Introduction
Although the landslide failure time prediction method has been successfully applied in some indoor and field cases, it has certain drawbacks because it relies on the displacement data measured by contact sensors. First of all, the displacement sensor hardware cost is high, the installation is complicated, the maintenance is difficult, and the staff during the construction process has a certain danger; secondly, there are high requirements for the buried position of the sensor, and improper positioning of the sensor will seriously affect the accuracy of prediction; finally, the information obtained by the sensor is very limited, the information collected has a large amount of noise, and the displacement sensor can only reflect a small change, which is difficult to reflect the overall situation of the slope. Considering the above problems in displacement monitoring, noncontact monitoring methods have begun to attract the attention of some scholars because of their advantages of convenient installation and simple maintenance. Some scholars have realized the automatic identification of a landslide deformation area through landslide images collected with noncontact monitoring equipment [20; 21], for example, a method based on a combination of RGB color space and a support vector machine (SVM) to identify landslide areas [22]. The identified landslide area and volume play a very important role in understanding the slope damage degree and evaluating remedial methods [23; 24; 25]. However, there are relatively few studies on the change law of landslide area and volume during the landslide instability process. This paper collects the data from indoor rainfall model tests completed by the team in recent years [26; 27; 28]. These experiments used the control variable method to study the instability and failure of rain-type slopes under different experimental conditions (rainfall intensity, slope angle, presence or absence of slope crest, base soil compaction, presence or absence of confining pressure, vegetation cover density, and wind level). The main purpose is to quantitatively identify the failure area and volume in the whole process of slope instability, to study its change pattern under different experimental conditions, to find the commonalities and differences in the slope instability process based on experimental phenomena, and to reveal the evolution mechanism of rainfall-type landslides. Meanwhile, an improved S-growth curve model is proposed to predict landslide volume using the value of landslide volume at two moments with an interval of 5 min and the factors affecting the landslide volume as the input values. The research results can provide a scientific basis and theoretical support for landslide risk assessment and early warning prediction methods.
###### Abstract  
Vegetation transpiration (Tr) is crucial for the water cycle, regional water balance, and plant growth but remains challenging to estimate at large scales. Sun-induced chlorophyll fluorescence (SIF) provides a novel method for estimating Tr, but its effectiveness is limited by species specificity, requiring continuous tower-based observations for comprehensive analysis across diverse ecosystems. In this study, SIF and Tr were simultaneously monitored in Chinese cork oak (ring-porous), poplar (diffuse-porous), and arborvitea (non-porous) plantations in northern China, and the SIF-Tr relationship was further analyzed. The results showed that SIF and Tr shared similar diurnal dynamics, although Tr exhibited midday saturation. SIF and Tr were closely correlated, and the correlation strengthened as the temporal scale aggregated. Environmental factors had nonlinear impacts on SIF and Tr. Therefore, the SIF-Tr relationship deteriorated to some extent at midday, with short-term stress reducing the correlation by 0.1-0.23. Compared to the linear empirical model, the inclusion of environmental factors improved the accuracy of SIF-based Tr estimation, increasing the R\({}^{2}\) value by 0.12 to 0.37. At the same level of accuracy, the number of environmental variables required was higher at the half-hour scale than at the daily scale. This study demonstrated the species-specific influence of environmental factors on SIF and Tr in different plantations, enhanced the understanding of the SIF-Tr relationship, and provided theoretical and data support for future large-scale Tr predictions using satellite-based SIF.  
SIF; transpiration; environmental control; plantation; random forest +
Footnote †: journal: Remote Sensing
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 4 Discussion and Recommendation
### A Recommendation for Improved Participation of External Organizations  
The measurements stored in the Radio Noise Data Bank are certainly not the only radio noise measurements done world-wide. Unfortunately, data of several very interesting and valuable radio noise studies have never been submitted to the ITU Radio Noise Data Bank. Outside the ITU, the existence of this data bank is not widely known, and currently it is not very easy to access from outside the ITU, which will hopefully be improved soon. However, Study Group 3, the group that creates and manages the propagation models and the radio noise models of the Radio Sector of the ITU (ITU-R), welcomes such input. Of course, any submission must fulfill high quality standards, to avoid pollution of the data bank with flawed data, and adopt the required data format. Measurement system requirements and data formatting is described in Recommendation ITU-R SM.1753 [17], but it must be said that this manual needs updating, so that it will focus more on how to establish and report the performance and measurement uncertainty of the system, instead of describing standard system components, so that it will become more future proof and more inclusive of innovative and state-of-the-art solutions. Currently, the Radio Noise Data Bank is managed by Ofcom, UK, who can provide more information on required data quality and data submission. Further information can always be obtained by contacting the chairpersons of Study Group 3 [18].