esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Arabinoxylan on the Properties of Sourdough Wheat Bread
## 2 Materials and Methods
### Isolation and Modification of Water-Soluble Rye Arabinoxylans
#### 2.2.1 Isolation of Arabinoxylans  
A total of 100 g of wholemeal rye flour was treated with 500 mL of 80% \(v/v\) EtOH at 90 \({}^{\circ}\)C for 2 h to inactivate cereal enzymes. The ethanol solution was removed, and the sediment was dried at 40 \({}^{\circ}\)C for 20 h, followed by extraction with 2 l of water at 25 \({}^{\circ}\)C for 6 h. The suspension was centrifuged, and the clear supernatant was boiled to coagulate soluble proteins, then the sample was cooled and incubated with \(\alpha\)-amylase at 37 \({}^{\circ}\)C for 2 hr. The solution was boiled, then 20 g/L celite was added and filtered via a Buchner flask. The clear filtrate was transferred to a 4-fold solution of ethanol and acetone (1:1). The sediment was centrifuged, then frozen at \(-\)18 \({}^{\circ}\)C and stored for further modifications or washed twice with ethanol/acetone and twice with acetone alone. After the last centrifugation, the AX sediment was dried at 50 \({}^{\circ}\)C for 2 h. The yield of the unmodified AX preparation (designated AX_NM) was 2.7 g/100 g of whole wheat flour.
## 4 Simulation Results and Discussion  
In this research, a standalone microgrid is implemented under MATLAB/Simulink platform as shown in Figure 12. The equipment such as power supply, battery, PV simulator, etc., are modeled based on the real equipment which is available in the laboratory for later experimental purpose. The important parameters are provided in Table 2. It is worth mentioning that the objective of this simulation is to demonstrate the ability of VSG to mitigate the frequency deviation which results from the strong variation of PV output power. Hence, the reactive power control will not be focused in this study. Several case studies are carried out and discussed below.  
\begin{table}
\begin{tabular}{c c} \hline \multicolumn{2}{c}{**DGs**} \\ \hline Model & Kikusui PCR2000M \\ Power Rating & 220 V, 50 Hz, 2 kVA \\ P-Angle Droop Coefficient (m) & 2\(\pi\) (0.000185) \\ Q-V Droop Coefficient (n) & 1/0.002592725 \\ \hline \multicolumn{2}{c}{**Loads**} \\ \hline Variable Load & Load profile from MEA \\  & (\(P_{max}\) = 1.5 kW) \\ \hline \multicolumn{2}{c}{**PV Simulator**} \\ \hline Model & Kikusui PCR2000M \\ Simulate & Based PV profile (\(P_{max}\)= 2.1 kW) \\ \hline \multicolumn{2}{c}{**Battery storage system**} \\ \hline SOLAR 12-65 & 8 * (12 V 65 Ah) \\ \hline \multicolumn{2}{c}{**Supercapacitor storage system**} \\ \hline Maxwell BMOD0165 & 3 * (165 F, 48 V, 53 Wh) \\ \hline \multicolumn{2}{c}{**VSG-Constant**} \\ \hline J & 0.4863 \\ D & 382 \\ \hline \multicolumn{2}{c}{**VSG-BSA**} \\ \hline Population & 25 \\ Epoch & 5 \\ J & 0.51 \\ D & 597 \\ \hline \end{tabular}
\end{table}
Table 2: Microgrid’s parameters.  
Figure 12: Single-phase diagram of standalone microgrid.
# The Role of Verbal Feedback in the Motor Learning of Gymnastic Skills: A Systematic Review
## 2 Materials and Methods  
### Search Strategy  
The preferred reporting items for systematic reviews and meta-analysis statement (PRISMA) guidelines for reporting a systematic review were adopted [33]. Three investigators (M.S., M.N., and M.B.) searched the following electronic databases without time restriction. A literature search was conducted on 14 January 2022. The four databases: MEDLINE (EBSCO), Scopus, SPORTDiscus (EBSCO), and Web of Science were utilized for the search process. The search was limited to peer-reviewed articles that were published in the English language. The following keywords with Boolean operators were applied: (\"verbal feedback\" or \"verbal instruction\" or \"verbal information\" or \"verbal augment\" feedback\" OR \"verbal guidance\") and (trampoline\" or acrobat\" or gymnast\").
## 3 Methodologies
### Evaluation Parameters  
On a scale of precision, recall, sensitivity, and level of accuracy, classifiers were judged. False negatives are possible. The system can create accurate predictions that are measured by the accuracy of its performance.  
\[Accuracy=\frac{Correct\;Prediction}{Total\;Prediction}*100 \tag{2}\]  
System sensitivity is a performance statistic that assesses a system's capacity to accurately forecast favorable outcomes.  
\[Sensitivity=\frac{True\;Positive}{True\;Positive+False\;Negative}*100 \tag{3}\]  
Performance parameter Specificity assesses the system's ability to correctly forecast negative outcomes.  
\[Specificity=\frac{True\;Positive}{True\;Negative+False\;Positive}*100 \tag{4}\]  
Precision refers to a system's ability to produce just the most relevant data.  
\[Precision=\frac{True\;Positive}{True\;Positive+False\;Positive}*100 \tag{5}\]  
F-Measure utilizes the harmonic mean to combine accuracy and sensitivity measurements.  
\[F-Measure=2*\frac{Sensitivity+Precision}{Sensitivity*Precision}*100 \tag{6}\]
# Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data
## 2 Data and Methods
### Interband Regression Coefficient Lookup Table
In addition, it is noted that in the interband regression procedure, a significant discontinuity point exists between the higher and lower reflectance (Figure 2a), and this discontinuity point changes with variation in the solar zenith angle. At the hotspot, the discontinuity point peaks, i.e., 0.35 (Figure 2b), whereas the value is relatively small (0.27) at other angles (Figure 2b). Compared to a global linear \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Band**} & \multicolumn{5}{c}{**POLDER BRDF**} & \multirow{2}{*}{**Offset**} \\ \cline{2-3} \cline{5-9}  & **C\({}_{1}\)** & **C\({}_{2}\)** & **C\({}_{3}\)** & **C\({}_{4}\)** & **C\({}_{5}\)** & **C\({}_{6}\)** & \\ \hline HJ-Band1 & 0.777 & 0.212 & \(-\)0.011 & 0.016 & 0.030 & \(-\)0.028 & \(-\)0.006 & 0.003 \\ HJ-Band2 & 0.196 & 0.532 & 0.243 & 0.066 & \(-\)0.010 & \(-\)0.032 & 0.001 & 0.003 \\ HJ-Band3 & 0.004 & 0.269 & 0.740 & \(-\)0.050 & \(-\)0.009 & 0.052 & 0.004 & 0.003 \\ HJ-Band4 & 0.023 & \(-\)0.008 & \(-\)0.025 & 0.426 & 0.537 & 0.052 & 0.001 & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Band conversion coefficients from Polarization and Directionality of the Earth’s Reflectances (POLDER) to HJ-1 A/B CCD. BRDF: Bidirectional Reflectance Distribution Function; RMSE: Root mean square error. regression, the fitting accuracy can be much improved by splitting the datasets into two parts at the discontinuity point. In this study, the discontinuity points for different solar/view geometries were detected, and two regression procedures were performed for higher and lower reflectance datasets.
# A Study on the Amplitude Comparison Monopulse Algorithm
## 1 Introduction
In Section 2, the conventional amplitude comparison monopulse algorithm is explained. In Section 3, two least-squares-based monopulse algorithms are proposed. Section 4 discusses the relationships between the proposed and the conventional algorithms. In Section 5, a novel covariance equation is proposed as a performance prediction measure. In Section 6, numerical simulations are performed to evaluate the proposed algorithm and covariance equation. Conclusions are given in Section 6.
# Assessment of Imaging Protocol and Patients Radiation Exposure in Computed Tomography Colonography
A study shows that an optimized screening protocol can reduce the effective dose significantly compared to a general daily practice protocol, with a life-time cancer risk of 0.14% being estimated for a 50-year-old individual exposed to CTC radiation dose [20]. Today, manufacturers provided many options to reduce the radiation dose to the patient, automatic tube current modulation (ATCM) being one such example, justifying the number of photons (mA) according to patient size [18]. Iterative reconstruction technique (IRT) is yet another technique to reduce the tube current, providing less image noise and a more extended threshold without degrading the image quality [21]. Good professional practice is also a leading factor in reducing radiation dose without constraining optimum coverage of the CTC procedure [22]. Previous studies of patient-effective dose during CTC have shown patient doses ranging from 2.3 to 9.8 mSv per procedure [18; 19; 23], the wide variation pointing to the likelihood that patients might be subject to unnecessary radiation risk. Thus it is crucial that investigation be made of patient exposure to ensure the procedure is optimized. This study evaluates the current imaging protocol at King Fahad Medical City (KFMC) and radiation dose, following guidance of the American College of Radiology (ACR).
# When Maritime Meets Aviation: The Safety of Seaplanes on the Water
## 4 Discussion
### Interpretation of Results in Context of Previous Studies  
The analysis revealed that loss of control and abnormal runway contact are the predominant causes of seaplane occurrences on Canadian waters, accounting for over half of the recorded occurrences. This aligns with findings from [PERSON] [7] and [PERSON] et al. [8], which emphasized operational and environmental challenges unique to seaplanes. Recreational seaplanes often operate in less controlled environments with diverse pilot experience levels. This increases their risk of occurrences compared to commercial operations, which benefit from rigorous regulatory oversight and advanced training protocols.  
Environmental hazards emerged as a significant contributing factor. The data indicate that landing remains the most hazardous phase of on-water seaplane operations, accounting for over half of all accidents and incidents. This corroborates international studies, including those focusing on Croatian seaplane operations, which identified landing as a critical phase due to its reliance on both waterway conditions and pilot proficiency [8]. However, this paper uniquely highlights an increasing trend in landing-related occurrences in recent years, suggesting that additional factors, such as operational changes or evolving reporting practices, may play a role.  
In terms of human factors and training, the persistent challenge of obtaining detailed data, particularly for private recreational seaplanes, is consistent with [PERSON]'s [17] observations regarding the lack of comprehensive records on flight hours, pilot experience, and seaplane operations. This gap underscores the need for enhanced data collection mechanisms to support more accurate risk assessments and safety interventions. Furthermore, variability in pilot skill levels, particularly among recreational operators, points to the critical importance of standardized and comprehensive training. This has been highlighted in the prior literature as essential for reducing accident rates in under-regulated aviation sectors.
# Lp-Norm Inversion of Gravity Data Using Adaptive Differential Evolution
## 5 Field Example  
The Shihe iron ore deposit is located in the northern part of the Hengshan-Wutaishan dome in the Lvliang-Taihang fault block, North China. The main exposed strata are the Paleoproterozoicoiz Jingangku Formation, the Mesoproterozoicoiz Changcheng System,  
Figure 8: The inversion results of the model shown in Figure 2. (**a**) response of rectangular prism, (**b**) response of dipping prism, (**c**) response of parallel prism, (**d**) response of U prism.  
Figure 7: The observed data with noise and the model response for each model shown in Figure 2. (**a**) response of rectangular prism, (**b**) response of dipping prism, (**c**) response of parallel prism, (**d**) response of U prism.  
the Cambrian, and Quaternary. The Jingangku Formation is the ore-bearing unit that comprises metamorphic amphibolite-like rocks. The density of rock and mineral in this area is shown in Table 1. The garnet magnetite quartzite has the highest average density, and the difference between its density and the one of surrounding rock amphibolite has reached 0.38 g/cm\({}^{3}\).  
Since there are negative residual anomalies in the original data, in order to fit this part of the data, the search range of Lp-norm gravity inversion in this area is set to \([-0.5,0.5]\) g/cm\({}^{3}\). The horizontal cell size for subsurface space is set to 20 m during the inversion process, and the vertical cell size changes proportionally. The results with different \(p\) values are shown in Figure 9; it can be concluded that the top buried depth of the iron ore is about 400 m, and the depth of the ore body extends more than 400 m.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mineral** & **Number of Specimens** & \
\begin{tabular}{c} **Arithmetic Mean** \\ **(g/cm\({}^{3}\))** \\ \end{tabular} \\ \hline PlagioclAse amphibolite & 41 & 2.78 \\ Garnet Magnetite Quartzite & 30 & 3.16 \\ Biotite Granulite & 27 & 2.88 \\ Amphibole Plagioclase & 7 & 2.65 \\ Layer & Number of specimens &
\begin{tabular}{c} Arithmetic mean \\ (g/cm\({}^{3}\)) \\ \end{tabular} \\ Quaternary system & 6 & 1.53 \\ Jingangku formation & 106 & 2.78 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Density parameters of minerals and rocks in the Shihe area.  
Figure 9: _Cont._  
Observed from Figure 9, the density distribution results obtained by Lp-norm regularization inversion with different \(p\) values are similar, and are in good agreement with the one obtained by drill-hole information. In Figure 9, the relation between the three inversion results and the geological interpretation of the ore body is also shown. Obviously, the inversion results Lp-norm with \(p\leq 1.5\) are reliable, which shows that the Lp-norm inversion algorithm in this paper can improve the quality of inversion and bring the restored model closer to the real situation. Additionally, by comparing the L2-norm regularization results shown in Figure 9 with L1, L1.1, and L1.5, it is easy to see that the L2-norm inversion technique failed to draw the real shape of the ore deposit.
## 4 Approaches of Optimizing Geolocation Measurements
### Artificial Intelligence (AI)  
Artificial intelligence (AI) is a group of many different technologies working together to achieve the desired goal. It is implemented in control and communication systems,  
Figure 17: Algorithm performance comparison (MSPE and MRPE for three-BS geometry without NLOS). From the figures, it can be noticed that the two-step WLS algorithm has the best performance compared to other methods [91].  
as well as employed in geolocation and emitter tracking estimation. For example, the Genetic Algorithm (GA) approach is used to optimize classical geolocation measurements, and Dempster-Shafer (D-S) theory is used to optimize target tracking estimation [92; 93].  
It is important to mention that there are open public datasets of RF signals in the format that contains I and Q at different sampling rates. The best example one can find is the following datasets and platform: RF datasets for machine learning, see [94]. However, this dataset is mainly dedicated to signals classification and not geolocation and positioning; nevertheless, it is still interesting to investigate if one can estimate and extract navigation data (Doppler shift, Doppler rate) from raw data.
## 3 Integration of Sensors and Geometric Processing
### Georeferencing of 2D Imager Data  
#### 3.3.1 Snapshot 2D Imagers  
The major advantage of snapshot 2D imagers is that the spatial patterns in each image frame can be used in an SfM workflow. Through the selection of an optimal spectral band or the use of the raw 2D hyperspectral mosaic, the SfM process allows for the extraction and matching of image features. The resulting bundle adjustment will then calculate the position and orientation for each image frame without the need for GNSS/IMU sensors (although the image-matching phase can be assisted with GNSS/IMU observations), which reduces the complexity of the setup (Figure 4). Since this approach derives the relative position and orientation of the images, a scene with relative scaling can be generated. For several applications, this is already sufficient, and the approach is appealing, since one can forgo the additional weight and complexity of a GNSS/INS approach. Still, with the aid  
Figure 3: TerraLuma pushbroom system: Image (**top left**) and drawing of the sensor payload (**top right**) and device interaction flow chart (**bottom**; CAD design and flow chart: [PERSON], TerraLuma group).  
of an accurate on-board GNSS receiver or GCPs, geometrically accurate orthomosaics can be created (with a typical absolute accuracy of 1-2 pixels). One of the issues with this approach is that 2D imagers tend to have a lower spatial resolution, which can affect the number of matching features found in the SfM process. This can result in poor performance in image matching in complex terrain/vegetation, which has a direct impact on the quality of the spectral orthomosaic. This can be compensated by merging the low-resolution hyperspectral information with, e.g., a higher resolution panchromatic image [43]. An additional benefit of 2D imagers is that an initial bundle adjustment can be followed by an optional dense matching approach, which then allows the generation of high-resolution 3D hyperspectral point clouds and surface models (c.f. Section 5.4).
## 2 Materials and Methods
### Bulk Density  
The bulk density of the SSP/bio-composite specimens was measured following the ASTM D1895 [26] standard, which specifies procedures for determining the bulk density of granular and powdered materials. Composite specimens were prepared in cube-shaped molds with a volume of 1000 cm\({}^{3}\), consistent with those used for thermal conductivity testing. The specimens contained varying SSP contents, ranging from 0% to 3.5% by weight. Each specimen was weighed using a precision balance with an accuracy of 0.01 g. All density measurements were conducted in triplicate, with average values calculated. The bulk density (\(\rho\)) was calculated using Equation (2):  
\[\rho=\frac{m}{V} \tag{2}\]  
Here, m represents the mass of the specimen in grams, and V denotes the volume of the mold (1000 cm\({}^{3}\)). The results were recorded in units of g/cm\({}^{3}\). The bulk density values were utilized to evaluate the influence of SSP content on the physical characteristics of the composites, offering insight into their structural integrity and potential applications in material engineering.
## 3 Results
### Clustering Oncological Patients
#### 3.2.2 External Validation  
In our data set 110 patients belong to the luminal A subtype, 55 are in the luminal B HER2 negative condition, 35 in luminal B HER2 positive, 22 in HER2 positive and 45 in triple negative. Once a category among the latter is chosen for a patient, a therapy is assigned by clinicians to each case according to characterizing specificities and the presence of possible comorbidities. We resume the therapy statistics characterizing each molecular subtype in Table 2.  
Results presented in Table 3 highlight misclassifications of patients belonging to luminal B HER2 negative and positive subtypes, corresponding numerically to five and six cases included among triple negative and HER2 positive molecular subtypes, respectively. The wrong inclusion is explained by considering the mean value of biomarkers for hormonal receptors, that in the cluster C4 read \(\langle\text{ER}\rangle=65.3\) and \(\langle\text{PgR}\rangle=50.1\), while for misclassified patients in C3 read \(\langle\text{ER}\rangle=0.4\) and \(\langle\text{PgR}\rangle=4.8\) and \(\langle\text{Ki67}\rangle=20.6\), so signaling a much lower value for the hormonal receptors expression. Analogously in C2 we observe \(\langle\text{ER}\rangle=61.5\) and \(\langle\text{PgR}\rangle=34.4\), while misclassifications in C1 are endowed with \(\langle\text{ER}\rangle=5.2\) and \(\langle\text{PgR}\rangle=7.7\).  
To quantify the correspondence between clusters and molecular subtype or therapy labels, we evaluate ARI for both cases. To establish a baseline this calculation is implemented in the variation of the number of clusters \(k\): the behavior of the index is shown in Figure 5, where we highlight the confirmation for the choice of optimal number of clusters, because the maximum value is reached corresponding to \(k=4\) in both cases. Moreover, approximately the same derivative characterizes both plots, thus strengthening the relation between therapies and molecular subtypes in a completely unsupervised way.  
Figure 5: Dependence of ARI on the number of clusters, which is computed with respect to the correspondence between the unsupervised clustering and molecular subtypes in (**a**) and therapy combinations in (**b**). The blue line joins mean values included in the error bars and associated with the interquartile range yielded by bootstrap, whose finite difference derivative is described in the inbox.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cluster** & **Luminal A (\%)** & **Luminal B HER2 Neg (\%)** & **Luminal B HER2 Pos (\%)** & **HER2 Pos (\%)** & **Triple Neg (\%)** \\ \hline
**C1** & 0 & 0 & 21.4 & 78.6 & 0 \\
**C2** & 0 & 0 & 100.0 & 0 & 0 \\
**C3** & 0 & 10.2 & 0 & 0 & 89.8 \\
**C4** & 68.3 & 31.1 & 0 & 0 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correspondence between clusters and molecular subtypes in the data set.  
The clusters yielded by UPGMA contain a prevalent group in terms of both molecular subtypes or therapies, as resumed in Table 3. The maximal number of cluster members with respect to therapy correspond to the one observed in Table 2.
# Experimental Investigation of TR-UHPC Composites and Flexural Behavior of TR-UHPC Composite Slab
## 1 Introduction
[23] investigated the tensile properties of strain hardening cement-based materials reinforced by carbon fiber textiles (CFTs) where the results indicated that the composite reinforced with continuous fiber textiles and short fiber hybrids exhibited the best mechanical properties. [PERSON] et al. [24] investigated the tensile behaviors of textile reinforced mortar (TRM) with different shapes of carbon multifilament yams and types of matrices. The results showed that the flatter shape of the yarn could significantly improve the utilization of the textiles in the matrix. [PERSON] [25] conducted experimental and mechanical studies on the flexural behaviors of FRP fabric reinforced ultra-high-performance concrete (UHPC) panels, where the results showed that such formwork could be potentially used in the accelerated construction of critical infrastructure with enhanced crack resistance and an extended service life. Currently, research on fiber textile reinforced concrete is well-developed. However, the application of fiber textile reinforced UHPC (TR-UHPC) to engineering structures has received little attention. This study aimed to test the tensile properties of UHPC reinforced with various types of fiber textiles (basalt, carbon, and glass fiber textiles--BFT, CFT, and GFT, respectively) to determine the most cost-effective and reinforcing-efficient case. Subsequently, a novel structural type of basalt fiber textile reinforced UHPC (BFT-UHPC) pedestrian walkway thin slab was proposed in this study. The flexural behavior of the BFT-UHPC slab was explored through the stacking test and bending test. The results of the tests can provide a good basis for the application of fiber textiles in the engineering of BFT-UHPC thin slabs.