esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relative Importance of Environmental Data  
The relative importance of each predictor variable calculated using Model E in the RF and BRT modeling techniques is shown in Figure 2. In this study, the relative importance of predictor variables was normalized to 100% to facilitate comparisons among predictor variables. The relative importance of the same environmental variables in the RF and BRT models was slightly different. The five most important predictor variables for the RF model were LULC, BC_3, NDVI, Band_6, and Band_4 while in the BRT model, they were LULC, BC_3, BC_2, NDVI, and elevation. In both models, LULC and BC_3 had the same relative importance rankings, namely, first and second, respectively. These findings indicate that these environmental variables are the dominant predictor variables of STN mapping in this study area. In the RF model, Landsat-8-derived variables had the highest relative importance (with a relative importance of 31%), followed by Sentinel-1-derived variables (28%), LULC (15%), topographic (14%), and climate (12%) variables. The relative importance of Sentinel-1-derived variables, LULC, Landsat-8-derived variables, and topographic and climate variables in the BRT model were 31%, 26%, 19%, 19%, and 5%, respectively. Moreover, the remote sensing variables in the RF and BRT models had a relative importance of 59% and 50%, respectively.
## 1 Introduction
Factors, such as the number of retransmissions and retries when the MAC layer is listening to the channel, delays and general overhead also have an indirect influence over the total energy consumption, and they give an indicator of the network performance. In our work, we discuss these factors to show the efficiency of each of the protocols presented here. The type of transmission, reception, CSMA/CA (carrier sense multiple access with collision avoidance) algorithm, switching and listening in the MAC layer affects the energy problem, and they are well depicted and described in [11]. The problem of the local energy consumed by each node is treated in [12]. This model takes into account sensing, receiving and transmitting energies, and the network is partitioned into rings to analyze the behavior of the nodes according to the proximity to the sink or collector node. In this paper, the authors propose an energy model and use it to compare the performance of AODV, DSR and ZTR and the proposed multi-parent hierarchical (MPH) protocol [13] in the context of a single-sink scenario consisting of static nodes that send and receive information to and from a coordinator node. We assume that all nodes, except for the coordinator, have limited energy, memory and processing resources. A hierarchical logic topology fits well in this scenario: packets are transmitted from source nodes to the coordinator node by establishing routes where each next hop belongs to a nearest neighbor node (or nodes). The main objective is to analyze the principal MAC and network layer metrics that directly affect energy consumption for AODV, DSR, ZTR and MPH. To this end, the proposed energy model provides a way to compute the total consumed energy in the network, globally and locally. We will see that our MPH protocol, purposed to be built for these conditions, is more efficient and has a lower cost than the other protocols under evaluation. We now describe the organization of the rest of the paper. In Section 2, we describe related work. Section 3 reviews the efficient multi-parent hierarchical (MPH) routing protocol and shows a physical network implementation under the MPH protocol. Some parameters are considered in order to compare the results of the real implementation with those of our simulator. Section 4 discusses the results of the proposed metrics classified into: the MAC layer (retransmissions and CSMA/CA retries), network layer (overhead, discovered and valid routes) and global energy metric; the evaluated protocols are AODV, DSR, ZTR and MPH. This section also details the proposed energy model used to calculate the global and local energy consumption. In Section 5 the protocols are evaluated under different topology arrangements. Finally, conclusions are given in Section 6.
## 2 Syntheses of \(\alpha\),\(\omega\)-Dialkoxyfluoropolyethers
### 2.3. Preparation of DA-FPEs by DAF-PFPE Alkylation
#### 2.3.1 Alkylation of PFPE Dialkoxides  
In the past years, there have been many disclosures reported on finding effective alkylating agents suitable for perfluoroalkoxides. Dialkylsulfate was one of the first specific reagents used [21], because of its strong alkylating ability. In fact, it has been used to alkylate almost every nucleophile [22, 23], but the alkylating temperature of dialkylsulfate needs to be lower than 20 \({}^{\circ}\)C and undesired viscose gels can be formed at this temperature and remarkably reduce the alkylation rate and yield. Moreover, dialkyl sulfates are highly toxic and their carcinogenic activity makes them inapplicable for large-scale industrial production.  
Dialkyl sulfites and the alkyl ester of polysulphonic PFPE (Figure 5) were also proposed and applied as alkylating agents in reactions with acylfluorides to synthesize hydrofluoroethers (HFE) [6, 24]. Compared with dialkyl sulfate, dialkyl sulphate and the alkyl ester of polysulphonic PFPE are  
Figure 4: General two step reaction for synthesis DA-FPEs.  
Figure 5: Alkyl ester of the polysulphonic PFPE.  
considered non-carcinogenic, and their alkylating temperature over 100 \({}^{\circ}\)C avoids the formation of undesired gels. However, the molar yield of the product resulting from the use of these two alkylating agents is low (below 20%), in addition, the preparation of alkyl ester polysulphonic PFPE is particularly difficult.  
Fluorovinyl alkyl ethers, (CF\({}_{3}\))\({}_{2}\)C=CFO-R\({}_{\text{H}}\), were also considered as one of the most effent but expensive class of alkylating agent for metal perfluoroalkoxides [25]. A high molar yield of HFEs was achieved by using heptafluoro-_iso_-butenyl methyl ether [26] in the reaction with metal perfluoroalkoxides, shown in Figure 6(a). A nonvolatile conjugated base (CF\({}_{3}\))C=CO\({}^{-}\)FK\({}^{+}\) was formed as by-product after the reaction that can be hydrolyzed into its carboxylic acid (CF\({}_{3}\))\({}_{2}\)CHC(O)OH. Lastly, the carboxylic acid was decarboxylated with an aqueous base to form hexafluoropropane as another valuable product; this reaction is shown in Figure 6(b). Fluorovinyl alkyl ethers are commercially available products and they can also be prepared following the synthetic route described in Figure 6(c): The reaction between perfluoro-_iso_-butene (1) and a proper alcohol produces an octafluoro-_iso_-butyl alkyl ether (2) and by its subsequent dehydrofluorination the alkylating agent (3) and HF are synthesized.  
Newly developed alkylating agents which have strong a alkylating ability and less toxicity have been emerging in the past years, but these kinds of alkylating methods have an unavoidable common drawback: A stoichiometric amount of metal fluoride is usually needed to provide metal perfluoroalkoxides, and large amounts of partially reacted solid metal fluoride is left over after the reaction.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 4 CFRP Conductivity Measurements and Techniques for Non-Destructive Testing Applications  
CFRP with continuous carbon fibers are also widely applied in primary structures of aircrafts. However, CFRPs are typically characterized by low functional and structural properties in the thickness direction of laminates, so CFRP is seldom considered in electromagnetics [115]. In case of overheating, lightning strikes, and external impact, composite laminates may suffer from wide structural damage [116]. The increasing demand for improved CFRP performance arouses new engineering solutions to enhance the performances of CFRP and other fiber-reinforced composites [77].  
In most cases, electrical conductivity is an important parameter providing useful information for CFRP quality assessment, non-destructive testing, and performance improvement. Several approaches are used for the CFRP conductivity measurement. Some approaches have focused on the fiber's orientation or through-thickness conductivity. The Four Points Bridge technique has been used to determine the electrical conductivity of CFRPs, but this technique is unreliable and difficult in many cases due to low dimensionality and high anisotropy of CFRPs [42; 43]. Contactless methods have been proposed for measuring CFRPs' bulk electrical conductivity. However, the anisotropy has not been explored [43; 117]. The 0\({}^{\circ}\)/90\({}^{\circ}\) electrical conductivity ratio range is 50 to 500. The resistance of CFRP increased due to internal damage, such as delamination and fiber breakage [118; 119; 120]. In the following sections, different techniques for CFRP conductivity measurements are described and classified according to different applications.
## 2 Materials and Methods  
### MAGDA Demonstrator Sites  
The MAGDA project is being tested across three demonstrator sites strategically selected in different agricultural regions of Europe. These sites, located in Italy, France, and Romania, encompass diverse climatic conditions, soil types, and crop types, allowing for the evaluation of MAGDA's integrated system in a variety of agricultural contexts (Figure 1). Each site is characterized by distinct crop water requirements, irrigation methods, and vulnerability to climate variability, providing a comprehensive assessment of MAGDA's flexibility and performance.
## 3 Results  
### Differences in Perceived Risk Level  
The Kendall coefficient of concordance (Kendall's W) was computed to evaluate the consistency among survey participants in ranking the perceived risk levels of street images [41]. The analysis yielded a Kendall's W value of 0.72 (\(p\)! 0.001), which falls within the range of 0.61 to 0.80, indicating a substantial level of agreement among raters [42]. This significant concordance suggests that participants share a common perceived risk for each street image, rather than making entirely individual subjective judgments.  
Following this confirmation of the inter-rater consistency, a Kruskal-Wallis test was performed to examine whether the four sidewalk environments differed significantly in terms of perceived risk. The test revealed a significant overall effect, H(3) = 1298.74, \(p\)! 0.001, \(\epsilon^{2}\) = 0.51, indicating that participants' risk ratings were influenced by the presence or absence of construction obstructions and barriers.  
Specifically, as shown in Figure 5, the group with no obstructions and no barriers (O0_B0) exhibited the lowest perceived risk with a median of 2 and mean of 1.68. This group was the only one that did not involve a construction setting, serving as the control condition consisting of well-maintained sidewalks without adjacent construction activity, and was thus perceived as the safest scenario. In contrast, the remaining three groups (O1_B0, O1_B1, and O0_B1) all depicted sidewalks adjacent to construction sites, but with differing physical conditions. Sidewalks obstructed by construction materials or equipment but lacking any protective barriers (O1_B0) were rated as the most hazardous, receiving a median risk rating of 3 and a mean of 3.81. When obstructions were present but barriers were also placed (O1_B1), the median remained 3, and the mean perceived risk slightly decreased to 3.59, suggesting that barriers may offer pedestrians a sense of protection. Lastly, in the barrier-only group (O0_B1), which represents well-maintained sidewalks adjacent to construction sites, the median perceived risk was 2 and the mean was 2.21, indicating a level of risk lower than moderate. The descriptive statistics for each group are also presented in Table 2.  
[PERSON]'s post hoc tests were conducted to identify which specific pairs of groups differed significantly, and the results are summarized in Table 3. All pairwise comparisons revealed statistically significant differences in the mean perceived risk levels. Five of the six comparisons were highly significant (\(p\) < 0.001), while the comparison between O1_B0 (obstructions without barriers) and O1_B1 (obstructions with barriers) showed a smaller yet still meaningful difference (\(p\) < 0.05). Among all pairs, the largest difference in the mean perceived risk was observed between O0_B0 (no obstructions, no barriers) and O1_B0, indicating the substantial impact of construction material encroachment into pedestrian space on perceived risk.
# Monte Carlo Study of Imaging Plate Response to Laser-Driven Aluminum Ion Beams
###### Abstract  
We measured the response of BAS-TR imaging plate (IP) to energetic aluminum ions up to 222 MeV, and compared it with predictions from a Monte Carlo simulation code using two different IP response models. Energetic aluminum ions were produced with an intense laser pulse, and the response was evaluated from cross-calibration between CR-39 track detector and IP energy spectrometer. For the first time, we obtained the response function of the BAS-TR IP for aluminum ions with a kinetic energy as high as 222 MeV. On close examination of the two IP response models, we confirm that the exponential model fits our experimental data better. Moreover, we find that the IP sensitivity in the exponential model is nearly constant in this energy range, suggesting that the response function can be determined even with little experimental data.  
Monte Carlo simulation; laser-driven ion acceleration; imaging plate +
Footnote †: journal: Applied sciences
# Raman Spectroscopic Investigation of Osteoclastic Activity under the Influence of Bisphosphonate
## 2 Materials and Methods
### Sample Characterization  
Raman imaging was performed using a confocal laser Raman microscope (RAMAN-touch, Nanophoton Co., Ltd., Osaka, Japan). The excitation source was 532 nm, at a nominal power of 200 mW. In order to prevent samples from burning, the power output was controlled by adjusting a dedicated ND filter. The microprobe used lenses ranging from 5\(\times\) to 100\(\times\) magnifications with numerical apertures from to 0.5 to 0.15. At the lowest magnification, the Raman spot size was 1.8 \(\upmu\)m, according to the \"half intensity\" definition. For Raman imaging, a linear detector was used to simultaneously acquire 400 spectra on a 415.47 \(\upmu\)m line in the \(x\) direction (lateral resolution in \(x\), 1.03\(\upmu\)m). In the \(y\) direction, the linear scans were repeated 233 times on a 241.41 \(\upmu\)m interval to have a comparable lateral resolution in \(y\) (1.03 \(\upmu\)m). Two maps have been acquired on 5 different specimens for each type of samples.  
Optical and topographical maps of the surface of the samples were taken using a 3D laser scanning microscope (VK200K series, Keyence, Osaka, Japan) with lenses magnifications ranging from 10\(\times\) to 150\(\times\) and a numerical aperture between 0.30 and 0.95. Map acquisition was automated by using a motorized xy stage and a \(z\)-axis autofocus.  
X-ray diffraction (XRD) analyses of the samples were performed on a Rigaku Miniflex 600 system (Rigaku Corporation, Tokyo, Japan), using CuK\(\alpha\) radiation at 40 kV and 15 mA. The diffraction pattern was collected in the region between 22\({}^{\circ}\) and 57\({}^{\circ}\).
## 2 Materials and Methods
### MIC (Minimal Inhibitory Concentration) and MBC (Minimum Bactericidal Concentration) Determination  
The minimal inhibitory concentrations (MICs) of CHG and mouthwash formulations were determined by using the microbroth dilution technique [28]. An overnight culture of _S. mutans_ Clarke ATCC 25,175 was diluted up to 2 \(\times\) 10\({}^{6}\) CFU/mL in a fresh BHI medium. CHG stock (200 mg/mL) and CHG formulations (2 mg/mL) were diluted up to 20 mg/mL in sterile BHI broth. Then, two-fold dilutions, 100 \(\upmu\)L each, were prepared in a 96-well-microtiter plate in duplicate. One hundred \(\upmu\)L of sterile BHI broth was used as a growth medium for positive controls. Negative controls were performed using 100 \(\upmu\)g/mL ampicillin in the same broth. Thereafter, 100 \(\upmu\)L of the bacterial inoculum prepared at 2 \(\times\) 10\({}^{6}\) CFU/mL was added to each well. The final CHG concentrations tested were 10, 5, 2.5, 1.25, 0.6, 0.3 and 0.15 \(\upmu\)g/mL in the presence of 10\({}^{6}\) CFU/mL of _S. mutans_. Plates were incubated at 37 \({}^{\circ}\)C for 24 h, and growth was monitored by measuring optical density at 600 nm. The MIC was defined as the lowest concentration showing clear growth inhibition (OD/mL comparable to sterility controls). Experiments were performed in five independent replicates.  
For the determination of the minimum bactericidal concentration (MBC), the culture broths of the wells showing no visible growth were plated on BHI agar plates and incubated for 48 h at 37 \({}^{\circ}\)C. The concentration at which 99.9% of bacteria were killed was identified as the MBC.
## 4 POD with Ambiguity Resolution
### Results
#### 4.2.2 Single Satellite Orbit Validation
Compared with the DD AR solution, the SD AR result has better performance, which is also validated by JPL's orbit. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Satellite**} & \multirow{2}{*}{**Orbit**} & \multicolumn{3}{c}{**Solutions/mm**} & \multicolumn{3}{c}{**Improvement**} \\ \cline{3-8}  & & **FA** & **DD AR** & **SD AR** & **DD AR** & **SD AR** \\ \hline \multirow{3}{*}{GRCC} & Along-track & 8.8 & 8.3 & 6.0 & 5.7\% & 31.8\% \\ \cline{2-8}  & Cross-track & 6.7 & 6.4 & 6.2 & 4.5\% & 7.5\% \\ \cline{2-8}  & Radial & 6.0 & 5.6 & 5.1 & 6.7\% & 15.0\% \\ \hline \multirow{3}{*}{GRCD} & Along-track & 8.8 & 8.1 & 5.9 & 8.0\% & 33.0\% \\ \cline{2-8}  & Cross-track & 6.7 & 6.3 & 6.1 & 6.0\% & 9.0\% \\ \cline{2-8}  & Radial & 5.9 & 5.5 & 5.1 & 6.8\% & 13.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSs of differences between the FA, DD AR and SD AR orbits and JPL’s orbit. Figure 8: Daily RMSs of along-track orbit differences of FA, DD AR, and SD AR solutions with JPL’s orbit. The upper panel is for the GRCC satellite and the lower panel is for the GRCD satellite.
## 3 Preliminary and Model
### Traffic Degradation Image Restoration Model
#### 3.4.3 Triple Control Embedding Attention Module  
As shown in Figure 7, to efficiently utilize all control information, the triple control embedding attention module (TCE) is designed to replace the original attention modules in the middle blocks and decoder blocks of the denoising U-Net module. In the TCE attention module, parameters of original self-attention and cross-attention modules in U-Net are frozen. Degraded image attention and segmentation map attention are added before them, which are trainable.  
Q, K, and V represent query, key, and value features in the attention mechanism, respectively. For Seg attention, Q comes from degraded image condition control, and K  
Figure 6: Network structure of image semantics encoder module (ISE).  
and V come from segmentation map condition control. This allows full utilization of latent information in degraded image DI. For DI attention, Q comes from latent feature Z, which is used to denoise iteratively in U-Net, and K and V come from degraded image condition control. For the original cross-attention module, K and V come from semantics embedding SE of the degraded image. This is used to guide the reverse sampling process of U-Net.  
In summary, through the TCE attention module, the U-Net denoising process can more fully utilize all control conditions, improving fidelity and generation quality of the restored image.
## 1 Introduction
This research used Landsat-8, ASTER, and Sentinel-2 data to devise a strategy for developing a surface outcrop map at regional and local scales in Marble, granite-bearing vegetated regions in Shewa Shahbazghari Mardan District of Khyber Pakhtunkhwa, Pakistan. Such low-cost, fast and reliable mapping strategies can be used in any granite field and maybe combined further with geophysical data for three-dimensional (3D) resource quantification and modelling. The main objectives of this study are as follows: (i) to apply the feature-oriented principal components selection (FPCS) technique to decorrelation-stretched and raw Sentinel-2, ASTER, and Landsat-8 datasets for generating granite and marble outcrop maps; (ii) to select the three best PCs by the Crosta technique, supported by the following: (1) field validation; (2) known granite and marble mining leases; and (3) petrographic investigations of collected samples; (iii) to devise a weighting criterion for the fusion of the selected components to generate a false-colorcomposite (FCC) image, differentiating between marble and granite in the region; (iv) to further extend the technique for mapping extents of granite in the southern (Thar District in the Province of Sindh) and northern (Malakand District of Khyber Pakhtunkhwa Province) Pakistan to establish further applicability.
# Research on the Vehicle Steering and Braking Stability Region
## 4 Solution of Stability Region
### Fitting of Three-Dimensional Stable Region  
Figure 9 shows the two-dimensional bifurcation parameter set composed of the front wheel steering angle and braking torque at the given velocity, which is the boundary of the steering and braking stability region. In other words, within this boundary the system is stable.  
It can be observed in Figure 9a,b that the boundary of the stability region is a hyperbola. With the increase in velocity, the stability region gradually decreases. Especially, when the  
Figure 9: Steering and braking stability region: (**a**) Bifurcation parameter set in \(\delta_{f}\)-\(T_{b}\)-\(v_{x}\) space; (**b**) Bifurcation parameter set in the \(\delta_{f}\)-\(T_{b}\) plane; (**c**) Steering and braking stability region and surface fitting.  
longitudinal velocity \(v_{x}=10\) m/s increases to \(v_{x}=20\) m/s, the stability region is significantly reduced. When the longitudinal velocity \(v_{x}=50\) m/s increases to \(v_{x}=60\) m/s, the stability region does not change much and only shrinks slightly. The blue circle line is a set of two-dimensional bifurcation parameters solved at different velocities. It can be seen that it passes through the boundary of the stability region at a given velocity in three-dimensional space, thereby mutually proving the correctness of the solved data.  
Figure 9b shows the relationship between the bifurcation parameters at a given initial velocity and different initial velocities: when the initial velocity is the same, as the braking torque increases, the front wheel steering angle at the bifurcation point gradually increases; when the initial velocity continues to increase, as the braking torque increases, the front wheel angle at the bifurcation point gradually decreases, but then gradually increases after decreasing to a certain extent.  
When there is more and more data on the two-dimensional bifurcation parameter set, theoretically, the steering and braking stability region should be composed of two symmetrical two-dimensional surfaces and a three-dimensional coordinate system. Figure 9c is a hyperboloid (three-dimensional parameter space) fitted according to six sets of boundary data of the stable region from \(v_{x}=10\) m/s to \(v_{x}=60\) m/s, that is, the boundary surface of the stable region. The surface interpolation method adopts the default \"linear\" method of MATLAB 2022a. The blue circle line in Figure 9 crosses the curved surface obliquely, which also proves the correctness of the fitted hyperboloid.