esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Frequency-Dependence in SIP Measurements Conducted In-Situ and in the Lab
Direct measurements of pore-space geometry are possible [49]; however, it is associated with extra costs and efforts, especially when investigating a large number of samples. Hence, we propose a different approach to understanding the textural properties of the materials in our study area. We decided to destroy some rocks that were used to extract the plugs 4l and 4s to create samples with a controlled grain size. Ground samples then allow us to investigate the frequency dependence of the SIP measurements by changing the grain size of the ground samples. The spectra of the measurements in the ground samples with a well-defined grain size are presented in Figure 4e,f, while Figure 4g,h presents the spectra measured by the mixtures of samples with different grain sizes. In Figure 4e,f, we also include the spectra measured in plug 4s, which were drilled from the same rock where the grains were extracted. Although the weight of the plug 4s is 102 g, we observe similar polarization values (\(\sigma^{\prime\prime}\)) for the measurements in the plug and those in the samples with grain sizes d1, d2 and d4, associated with a 3.75% volumetric content of the sample corresponding to 50 g. In contrast, the \(\sigma^{\prime\prime}\) values are three times lower (in the range of 3-4 mS/m) than those observed for measurements with the biggest grain size, d4 (\(\sigma^{\prime\prime}\) -12 mS/m), which are associated with larger sample volumetric content (7.5% corresponding to 100 g). Regarding the frequency-dependence, plots in Figure 4e-j show a shift in the quadrature conductivity peak to higher frequencies with decreasing grain size, as observed in other measurements performed in electronic semiconductors [20; 29; 50]. Moreover, the amplitude of the quadrature conductivity increases with the increasing graphite content, in agreement with mechanistic models [25; 26; 27; 28; 31]. Accordingly, we also observe increased characteristic relaxation time with increasing graphite grain size (Table 4) from the fitted Cole-Cole model for the different measurements. Figure 4i,j presents the spectra measured on the commercial graphite powder with the increasing saline pore fluids. Due to the small grain size, the frequency peak occurs at high frequencies (\(-\)200 Hz) at low salinity (0.023 mg/L) and shifts to higher frequencies as the salinity increases (\(-\)5000 Hz for 1.15 mg/L), which is consistent with the observations reported by [PERSON] and [PERSON] [15]. The frequency peak and the in-phase and quadrature conductivity values increase with fluid salinity. Similar to [PERSON] and [PERSON] [15], we found that increasing salinity shifts the characteristic frequency to higher frequencies (Table 5) and similar to their 150 \(\upmu\)m graphite data set, we found a decrease in the magnitude of the phase response (Figure 4i,j).
## Materials and Methods
### Experimental Set-Up
#### 2.1.2 Technical Realization of Laser Projection Unit
As shown in Figure 5, the grid (apart from the center point) displays a uniformity error of only \(\pm 10\%\) in the laser point size and intensity distribution which has been allowed for in the determination of \(P_{\mathrm{max,total}}\). For a clear grid distinction in the detection process, all higher diffraction orders outside the \(31\times 31\) array must be kept to a minimum. Figure 4: Gaussian intensity distribution of laser beams in the grid at a working distance of 65 mm: cross-sectional measurement of individual laser spots and determination of \(1/\mathrm{e}^{2}\) radius at 400. Figure 3: Schematic of the optical set-up within the laser projection unit (LPU): The diffractive optical element is a customized, binary phase mask generating a laser grid of \(31\times 31\) points. All optical elements have an anti-reflective coating for 532 nm. Meeting the variable larynx dimensions in male and female subjects, the diameter and energy distribution of the individual laser beams in the grid should be as constant as possible over a given working distance of 50-80 mm from the tip of the endoscope. Compared to earlier prototypes in the development process of the LPU (Figure 6, red curve), the more gentle slope in the beam caustic of the present version (Figure 6, blue curve) avoids the high variability in the diameter and consequently in the tissue exposure. The divergence of the complete beam array is chosen in order to expand the regular grid from the small exit window (\(3.2\,\mathrm{mm}\times 3.2\,\mathrm{mm}\)) at the tip of the LPU to \(25\,\mathrm{mm}\times 25\,\mathrm{mm}\) at the working distance of \(65\,\mathrm{mm}\), resulting in a spot-to-spot distance of about \(0.8\,\mathrm{mm}\) in this plane. In this way, the grid completely and densely covers the vocal folds while the individual spots remain well distinguishable for detection.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 4 Problem Formulation and Data Preparation  
### Problem Formulation  
Modern wireless communication systems increasingly rely on MIMO technologies to meet the demands for higher data rates, spectral efficiency, and robustness. However, signal detection in MIMO systems remain a critical challenge due to their high-dimensional nature, especially under varying channel conditions and interference. Traditional model-based detection algorithms, such as ZF, MMSE, or MLD, often suffer from scalability issues and increased computational complexity, particularly in large-scale MIMO configurations [33]. To overcome these limitations, AI and ML techniques have emerged as powerful alternatives. ML algorithms offer the capability to learn nonlinear relationships, adapt to diverse channel conditions, and generalize well from training data. These methods can enhance signal detection by dynamically adapting to channel variations and noise characteristics, thus supporting the development of ultra-reliable low-latency communications in next-generation networks. Furthermore, AI-driven solutions can optimize channel utilization and improve spectral efficiency through intelligent resource allocation based on real-time traffic patterns. In this work, we aim to design and evaluate a DL-driven signal detection model for MIMO systems by employing an FFNN trained on synthetic data generated for various modulation schemes. The objective is to develop a robust and generalizable detection framework capable of outperforming conventional algorithms in terms of both accuracy and computational efficiency.
## 2 Experimental Methodology  
### Specim Preparation  
Test specimens were all collected from the Longmaxi shale in Lujiao Town, Pengshui County, Chongqing, China and from the same depth. This black carbonaceous shale is characterized by well-defined bedding planes and has a density of approximately 2300 kg/m\({}^{3}\). The results of the X-ray diffraction and scanning electron microscopy analyses indicated a high quartz content (52.68%) and the presence of clay minerals (29.53%) [6].  
For the DTT specimens, 100 mm cube samples were drilled at angles of 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\) relative to the bedding orientation, as shown in Figure 1a. Two coaxial annular slots were then carefully drilled at the top and bottom ends of each specimen. The diameter and depth of the inner annular slot at the upper end were designated as \(d_{1}\) and \(h_{1}\), respectively, while the diameter and depth of the outer annular slot at the lower end were designated as \(d_{2}\) and \(h_{2}\), respectively. The distance between the two coaxial annular slots was denoted as \(b_{1}\) and \(b_{2}\), as shown in Figure 1b,c. The final prepared specimen is illustrated in Figure 1e. The above geometric parameters were as follows: \(H=L\)= 100 mm, \(d_{1}=25\) mm, \(d_{2}=50\) mm, \(b_{1}=b_{2}=5\) mm, and \(h_{1}=h_{2}=70\) mm. For the BST specimen, Brazilian disks with a diameter (\(B\)) of 50 mm and a thickness (\(t\)) of 25 mm were drilled parallel to the bedding orientation, adhering strictly to the recommendations of the International Society of Rock Mechanics, as shown in Figure 2a. A typical BST specimen was illustrated in Figure 2b. For DTT and BST, to mitigate hydration-induced damage in shale, the dry-air suction coring technique was implemented for core specimen retrieval. The loading angles for both tests were set at \(0^{\circ}\), \(15^{\circ}\), \(30^{\circ}\), \(45^{\circ}\), \(60^{\circ}\), \(75^{\circ}\), and \(90^{\circ}\), respectively. The DTT specimens were prepared in one group (total 7 specimens), while the BST specimens were prepared in two groups (total 14 specimens), with each group consisting of seven specimens.  
Figure 1: Specim preparation of DTT with different loading angles. (**a**) Schematic diagram of shale coring, (**b**) Top view [23], (**c**) Section-cutting plane [23], (**d**) 3D perspective, and (**e**) Typical shale specimen for DTT.
# One-Dimensional Nanostructure Engineering of Conducting Polymers for Thermoelectric Applications
## 3 Strategies to form 1D Conducting Polymer Nanocomposite Thermoelectric Materials
### In Situ Polymerization of Conducting Polymers on 1D Nanofilers
After vacuum filtration, these composite fibers can self-assemble to free-standing films of different size and thickness (20-150 mm). With a CNT: Py ratio of 60 wt% and aqueous ethanol as solvent, the nanocomposite films exhibit a remarkably high thermoelectric performance. The electrical conductivity, Seebeck coefficient and power factor can reach up to 399 \(\pm\) 14 S cm\({}^{-1}\), 22.2 \(\pm\) 0.1 \(\upmu\)V K\({}^{-1}\) and 19.7 \(\pm\) 0.8 \(\upmu\)W m\({}^{-1}\) K\({}^{-2}\), respectively. Furthermore, the reported composite films exhibit excellent mechanical stability upon various deformations. As shown in Figure 11c-e, no obvious reduction is observed with thermoelectric properties after bending for as many as 1000 times and the performance maintained after being stretched to a strain of 2.6%, demonstrating great promise of this thermoelectric material to be applied in wearable devices for energy harvesting. Figure 10: Schematic representations of the formation mechanism of DM-CNT/PANI (**a**) and CNT/PANI (**b**) nanofibers. The enlarged part shows the highly ordered arrangement of the PANI backbone chains on the surface of the CNTs. SEM images of the fiber mats of DM-CNT/PANI (40 wt%) (**c**) and CNT/PANI (40 wt%) (**d**). The inserts are the TEM images of a single fiber. Arrows denote the alignment direction of fibers. Readapted with permission from The Royal Society of Chemistry [87].
## 3 Results
### Correlation between MCV, RDW, MPV, PDW, and Other Laboratory Parameters  
Table 2 presents the potential correlations between MCV, RDW, MPV, PDW, and the laboratory parameters that we intended to analyze.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dependent Variable** & **r Squared** & \
\begin{tabular}{c} **Independent** \\ **Variable** \\ \end{tabular} & **Coefficient** &
\begin{tabular}{c} \(p\) \\ **Value** \\ \end{tabular} \\ \hline NT-proBNP & 0.015 & MCV & \(-\)85.5258 & 0.31 \\ \hline NT-proBNP & 0.001 & MPV & 118.8817 & 0.799 \\ \hline NT-proBNP & 0.013 & PDW & \(-\)1.5405 & 0.485 \\ \hline NT-proBNP & 0.056 & RDW & 7.096 & 0.051 \\ \hline Creatinine & 0.050 & RDW & 0.0658 & 0.027 \\ \hline Creatinine & 0.047 & MPV & 0.0692 & 0.033 \\ \hline Creatinine & 0.001 & MCV & \(-\)0.002 & 0.751 \\ \hline Creatinine & 0.029 & PDW & 0.1006 & 0.199 \\ \hline Admission & 0.002 & MCV & 62.1712 & 0.655 \\ high-sensitivity troponin & 0.008 & MPV & \(-\)507.6378 & 0.396 \\ \hline Admission & 0.017 & PDW & 1526.2125 & 0.336 \\ \hline Admission & 0.000 & RDW & \(-\)39.6987 & 0.954 \\ high-sensitivity troponin & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation between MCV, RDW, MPV, PDW, and other laboratory parameters.  
The NT-proBNP level was measured for each patient upon admission. We observed a statistically reliable interdependence between NT-proBNP and RDW (Figure 3). However, there was no relevant relationship between MCV, MPV, PDW, and the values of NT-proBNP (Table 2).  
Furthermore, our results showed no significant statistical correlation between the infarct size (measured by admission creatine-kinase, admission creatine kinase-MB, high-sensitivity troponin, peak creatine-kinase, peak creatine kinase-MB, and peak high-sensitivity troponin) and the parameters we proposed (Table 2).  
Regarding the history of antiplatelet therapy, our findings did not indicate any significant effect of these drugs on MPV or PDW values, as demonstrated in Table 3.  
Figure 3: Linear relationship between RDW and NT-proBNP. The figure is an original contribution by the authors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dependent Variable** & **r Squared** & **Independent Variable** & **Coefficient** &
\begin{tabular}{c} \(p\) \\ Value \\ \end{tabular} \\ \hline MPV & 0.000 & Dual antiplatelet therapy & \(-\)0.0095 & 0.976 \\ \hline MPV & 0.000 & Mono antiplatelet therapy & 0.0153 & 0.952 \\ \hline PDW & 0.028 & Dual antiplatelet therapy & 0.2998 & 0.208 \\ \hline PDW & 0.008 & Mono antiplatelet therapy & \(-\)0.1262 & 0.512 \\ \hline \hline \end{tabular} The table is an original contribution by the authors.  
\end{table}
Table 3: Correlation between MPV, PDW, and antiplatelet therapy.  
Figure 2: Linear relationship between MPV and creatinine. The figure is an original contribution by the authors.
## 3 Methods
### VI Derivation  
The NDVI is defined by [17]:  
\[\text{NDVI}=\frac{(\text{NIR}-\text{RED})}{(\text{NIR}+\text{RED})} \tag{1}\]  
where NIR is the reflectance value in the near infrared band, and RED is reflectance in the red band, respectively. For the MOD09 data, band 2 represents the near infrared reflectance (871-876 nm) and band 1 represents the red reflectance (620-670 nm).  
The SAVI is defined by [20]:  
\[\text{SAVI}=\frac{(\text{NIR}-\text{RED})}{(\text{NIR}+\text{RED}+\text{L})} \times(1+\text{L}) \tag{2}\]  
where L is the soil brightness correction factor. The value of L varies with the amount or cover of green vegetation: in very high vegetation regions, L = 0; and in areas with no green vegetation, L = 1. Generally, a value of L = 0.5 works well in most situations and is the default value used. When L = 0, then SAVI = NDVI. We set L = 0.5 as suggested in our study.  
The EVI is defined by [17]:  
\[\text{EVI}=\text{G}\times\frac{(\text{NIR}-\text{RED})}{(\text{NIR}+\text{C} _{1}\times\text{RED}-\text{C}_{2}\times\text{BLUE}+\text{L})} \tag{3}\]  
where G = 2.5, C\({}_{1}\) = 6, C\({}_{2}\) = 7.5, L = 1. BLUE is the reflectance in the blue band. The coefficients 2.5 and 1 represent the gain and canopy background, respectively [17]. The atmospheric influence on the red band is corrected using the blue band and the coefficients 6 and 7.5, respectively. Band 3 of MOD09 data represents blue band reflectance (459-479 nm).
# New Methods for Assessing External Sulfate Attack on Cement-Based Specimens
## 4 Discussion
### Discussion Regarding the Drying Method
These techniques may include the following: (i) aluminum nuclear magnetic resonance (Al NMR) and X-ray diffraction (XRD) tests to quantify ettringite produced during the ESA chemical process, (ii) thermogravimetric analysis (TGA) tests to quantify portlandite consumed to form secondary ettringite, (iii) scanning electron microscopy (SEM) tests to determine if the sulfate penetration front has occurred, and (iv) mercury intrusion porosimetry (MIP) tests to identify microstructure changes.
## 3 Results
### Effect of Commercial Volatile Compounds on Antifungal Activity against F. incarnatum  
Commercial volatile compounds that have been reported as antifungal compounds (2-ethylhexanol, 1-nonanol, 6-PP, and 2-methyl-1-butanol) and PEA were tested against _F. incarnatum_ growth in vitro on PDA plates. The results showed that all commercial volatile compounds inhibited the mycelial growth of _F. incarnatum_ (Table 2). High percentage inhibition of volatile compounds depended on high concentrations. A dilution of 10\({}^{-1}\) of all volatile compounds strongly inhibited _F. incarnatum_, with the percentage inhibition ranging from 21.68% to 74.29%. 2-ethylhexanol was found to be the best volatile compound for inhibiting _F. incarnatum_, with a percentage inhibition of 74.28%. The PEA showed a percentage inhibition of _F. incarnatum_ of 56% at a dilution of 10\({}^{-1}\), equivalent to a concentration of 1.5 mg mL\({}^{-1}\). This confirmed that PEA is a volatile antifungal compound against _F. incarnatum_.  
Fungal mycelia among the VOCs released from _T. asperellum_ T76-14, PEA, and the control was observed by a compound microscope. The fungal mycelia of _F. incarnatum_ exposed to VOCs and PEA showed an abnormal shape, whereas the control showed no change of mycelia and remained a normal shape (Figure 6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Commercial Volatiles**} & \multicolumn{3}{c}{**Percentage Inhibition (\%) a**} \\ \cline{2-4}  & **10\({}^{-1}\)** & **10\({}^{-2}\)** & **10\({}^{-3}\)** \\ \hline
1-Nonanol & 55.42 \(\pm\) 13.69 b ** & 29.71 \(\pm\) 3.32 b  & 19.67 \(\pm\) 5.69 c  \\
2-Ethylehexanol & 74.29 \(\pm\) 11.12 a  & 29.71 \(\pm\) 5.01 b  & 24.89 \(\pm\) 9.04 a  \\
2-Methyl-1-butanol & 39.12 \(\pm\) 8.43 c  & 20.88 \(\pm\) 4.86 d  & 16.86 \(\pm\) 1.20 d  \\
6-Pentyl-2H-pyran-2-one & 21.68 \(\pm\) 5.25 d  & 32.53 \(\pm\) 14.65 a  & 20.08 \(\pm\) 6.18 b  \\ Phenylethyl alcohol & 56.00 \(\pm\) 5.25 b  & 24.00 \(\pm\) 6.92 c  & 12.00 \(\pm\) 4.00 e  \\ \hline \hline \end{tabular}  
* Percentage inhibition of mycelial growth was calculated using 95% ethyl alcohol as a negative control. Means within the same column followed by the same superscript letters are not significantly different at _p_\(<\) 0.05 using Tukey’s test.  
\end{table}
Table 2: Comparison of phenylethyl alcohol (PEA) and other commercial volatiles against _Fusarium incarnatum_.  
Figure 5: Mass spectrum for the compound at 6.52 min (**a**) and phenylethyl alcohol (PEA) with its structure (**b**).
## 3 Methods
### Random Forest and Its Extensions  
RF is an ensemble machine learning model that conducts classification and regression using the bootstrap and bagging methods [39]. The RF generates many decision trees with different input features  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data Source** & **Variable** & **Spatial Resolution** & **Temporal Resolution** \\ \hline MODIS 1  & NDVI 2 & 0.05\({}^{\circ}\) & 1 month \\  & LAI 3 & 500 m & 8 days \\  & FPAR 4 & 500 m & 8 days \\ \hline GPM 5 & SPI3 6 & 0.25\({}^{\circ}\) & 1 day \\ \hline LDAPS 7 & Air temperature & 1.5 km & 3 h \\  & Land surface temperature & 1.5 km & 3 h \\  & Soil temperature & 1.5 km & 3 h \\  & Relative humidity & 1.5 km & 3 h \\  & Wind speed & 1.5 km & 3 h \\ \hline \end{tabular}
\end{table}
Table 1: Spatial and temporal resolutions of the data used as input features for the ML-based ET\({}_{0}\) model.  
through random sampling of training data. Resampling is conducted via bootstrapping if necessary, to achieve a suitable distribution of training samples. As an extension of the RF model, the gradient boosting machine (GBM) and the extreme gradient boosting (XGBoost) adopt a boosting method instead of bagging. The boosting considers the prediction performance of each sample during the iteration of training. The XGBoost uses more regularized trees than the GBM for the adjustment of the weighting scheme [40]. We used the three ensemble machine learning methods (RF, GBM, and XGBoost) and compared the test results to select the most suitable model. The number of decision trees (ntree) and the number of variables for splitting the tree branches (mtry) are the critical hyperparameters for the optimization of the three models [41]. We set the ntree parameter to 100 (Figure 3), and the mtry parameter to the number of input features divided by three, based on optimization experiments.
# Function-Oriented Applicability Evaluation of Technical Folding Based on Expert Knowledge
## 2 State of the Art
### Expert Knowledge of Technical Folding  
Specific knowledge of a total of 21 solutions that have successfully applied the solution principle of technical folding are known from previous publications described above. From these publications, the following classifications for the respective technical folding system can be obtained: Overall Purpose, Origami Discipline, Folding Scheme, Type of Folding System (rigid/non-rigid system), Type of Movement, and Direction of Movement. The expression of the classifications is either described textually in previous publications or can partly be taken from the published illustrations of the manufactured product. Expressions of the classifications for technical folding systems are given in non-formalised form.  
The overall purpose of previous technical folding systems is, for example, to reduce the overall installation space for transporting the system [7; 16] or to change the overall installation space for discrete-time shielding of solar radiation [21]. Every known technical folding system from the literature can be assigned to a folding system type (rigid, non-rigid) and an origami discipline, such as _Tessellation Origami_, _Curved Folding Origami_, or _Modular Origami_. In addition, the folding scheme on which the movement of the folding system is based, such as _Miura-Ori_, _Yoshimura_, _Chickenwire_, or _Waterbomb_, can be clearly identified. Further classifications are type of movement (planar, cylindrical, helical, spherical) and direction of movement of the technical folding system. The classification was derived from the cited sources and discussed with experts in the field.  
Figure 1 illustrates the classifications of technical folding using the example of three folding systems from the literature.
## 1 Introduction
The primary objective of this study was to create a continuous, high-resolution cropping intensity dataset for Shaanxi Province from 2019 to 2023. Using a combination of Landsat-8 and Sentinel-2 imagery processed through GEE, we aimed to capture the spatiotemporal dynamics of cropping intensity and identify abandoned cropland in this region. Our findings will provide valuable insights to inform more effective cropland management strategies, and the methodology developed in this research offers the potential for broader applicability, facilitating continuous monitoring of the cropping intensity in other regions by leveraging multi-source remote sensing data.
## 1 Introduction
### State of the Art LIDAR and Photogrammetry  
LIDAR surveying uses a laser beam to collect information. The technology works by emitting lasers and measuring the time it takes for the light to return to the source. This technique came into use in the 1990s and makes it possible to date the coordinates of the analysed points with respect to the emitter. Photogrammetry collects data by analysing and comparing multiple two-dimensional images (photographs) to create a three-dimensional model. It is considered a passive sensor because it does not produce its own energy source to collect information. Both LIDAR surveying and photogrammetry can be used to take measurements and discern data. LIDAR creates a three-dimensional model through a laser target. The scanning frequency and repetition rate will determine a point cloud.  
Photogrammetry produces a fully coloured 3D image by \"stitching together\" multiple 2D photographs, although it may take thousands of photographs to obtain an accurate model. In this work, data acquisition has been done by combining both techniques: laser scanning and photogrammetry.