esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 5 Discussion
### Feature Importance of the Combined Sentinel-1/2 Feature Space
Additionally, April is the sowing period of summer crops and the flowering phase of winter crops and, thus, this month is vital for their accurate discrimination. This order is different for the second scenario of using artificially generated cloud masks, where May and June are the following most important months. The significant decrease in April's relative importance might be explained, considering that valuable information content is missed due to the addition of artificial cloud noise. Despite these differences, for the majority of months, feature importance values do not present significant changes for the two scenarios examined. We also observe an important contribution of features acquired during January, which is almost doubled for the case of artificial clouds and on the other hand a very low contribution of February. These two months are useful for identifying the all-year crop types and potentially some winter crops. Normally, we would expect both these months to contribute equally, however from Figure 2 we observe that there are no available cloud-free Sentinel-2 images during February. Moreover, if we look at Tables S8 and S9 in the Supplementary Material we notice that Sentinel-2 features during January are among the most important ones and contribute more often that the Sentinel-1 from either January or February. Based on that, we assume that the information about the crops during this 2-month period is mostly captured from the Sentinel-2 features derived in late January and the rest of the Sentinel-1 features in February are redundant and do not boost the classification performance. Figure 11: Feature importance of each feature without clouds. Blue indicates Sentinel-2 and orange Sentinel-1 features. Figure 12: Feature importance of each feature with clouds. Blue indicates Sentinel-2 and orange Sentinel-1 features.
## 2 Related Work
### Material Classification  
Generally, material classification relies on characteristics like color or texture to differentiate various materials. However, distinguishing materials with conventional visible-light cameras can be challenging due to lighting variations or the presence of materials with different colors. Consequently, material classification often utilizes the visible spectrum of electromagnetic radiation and radiation from various wavelengths and different types of sensors.  
[PERSON] et al. [21] employed the wavelengths of the visible spectrum (R, G, B) and the NIR region to leverage the correlation between them for material classification. They extracted individual features from RGB and NIR domains using two separate CNN networks and then fused them in a fully connected layer for classification. [PERSON] et al. [22] used the LWIR wavelength to perform material classification. They created a dataset incorporating LWIR range imaging, and a classification method utilizing these data was proposed, showing improved performance compared to methods using only visible light data. [PERSON] et al. [23] suggests a material classification method based solely on visible spectrum data. This approach proposes an imaging framework that enhances appearance and recognition performance through slight angular variations when capturing images of materials. Based on this method, a public dataset of 40 types of outdoor ground surfaces was acquired.  
Beyond these visible and infrared-based methods, alternative techniques for material classification have been explored. [PERSON] and [PERSON] [24] investigated ultrasonic techniques to classify materials based on structural and microstructural properties. [PERSON] et al. [25] used deep learning methods for microstructural classification in low-carbon steel, providing insights into material properties through advanced imaging and computational techniques. Similarly, [PERSON] et al. [26] utilized acoustic signals and subspace-based classifiers for material classification. [PERSON] and [PERSON] [27] proposed a Siamese network architecture for material classification using millimeter-wave radar systems. In another study, [PERSON] and [PERSON] [28] applied classifier combination methods to noisy multispectral images for soil science applications to evaluate the efficacy of multispectral approaches in natural material analysis. [PERSON] et al. [29] combined spatial and multispectral features to classify natural materials in 3D point clouds. [PERSON] et al. [30] focused on NIR imaging to effectively classify surface materials of objects, while [PERSON] et al. [31] integrated neural networks and principal component analysis (PCA) to distinguish materials based on their reflectance properties. These advancements underscore the significance of multispectral and computational techniques in material classification to enable performance improvement across diverse applications.
# Early Infection for Mass Production of Artificial Single-Stranded DNA with _Escherichia coli_
## 2 Materials and Methods
### Phagemid Particle Production  
Phagemid particles derived from M13 phages were pre-produced as infection material. Chemically competent _E. coli_ JM109 was double-transformed with helper plasmid and phagemid and plated on complex media (2 xYT, Carl Roth GmbH + Co. KG, Karlsruhe, Germany) agar (20 g L\({}^{-1}\) Agar-Agar, Carl Roth GmbH + Co. KG, Karlsruhe, Germany) with antibiotics (33.3 mg L\({}^{-1}\) kanamycin and 20 mg L\({}^{-1}\) chloramphenicol) for selection and 1.2 g L\({}^{-1}\) MgSO\({}_{4}\bullet\) 7 H\({}_{2}\)O. Colonies were picked to inoculate pre-culture tubes (13 mL tube, Sarstedt AG & Co. KG, Numbrecht, Germany) with 5 mL complex medium (2 xYT), both antibiotics, and 1.2 g L\({}^{-1}\) MgSO\({}_{4}\bullet\) 7 H\({}_{2}\)O. After 7 h of incubation without pH control at 37 \({}^{\circ}\)C and 250 rpm in a shaking incubator (WIS-20 shaking incubator, Witeg Labortechnik GmbH, Wertheim, Germany), the cultures were passaged to 1 L baffled shaking flasks (DURAN\({}^{\circledR}\) Baffled Flask, DWK Life Sciences GmbH, Wertheim, Germany) with 100 mL complex medium (2 xYT), antibiotics, and 1.2 g L\({}^{-1}\) MgSO\({}_{4}\bullet\) 7 H\({}_{2}\)O as before. After over-night incubation without pH control at 37 \({}^{\circ}\)C and 250 rpm in the shaking incubator, the _E. coli_ cells were removed by centrifugation for 10 min at 4500 rcf (Rotixa 50 RS, Hettich Zentrifugen, Tuttlingen, Germany). The phagemid particles in the supernatant were precipitated in the presence of 40 g L\({}^{-1}\) PEG8000 and 30 g L\({}^{-1}\) NaCl within 30 min after mixing. Phagemid particles were separated by centrifugation for 10 min at 4500 rcf (Rotixa 50 RS, Hettich Zentrifugen, Tuttlingen, Germany). The pellet was solved in 1x Tris EDTA buffer (10 mM Tris base, 1 mM EDTA, pH 8.5).
## 4 Performance Analysis
### Simulation Assumptions and Conditions
* All simulation conditions are summarized as shown in Table 3.  
Figure 4: \(D_{BA}\) according to time and BS motion. (**a**) BS1 and BS2 move in the same direction as the AUV. (**b**) BS1 moves in the same direction as the AUV, while BS2 moves in the opposite direction. (**c**) BS1 moves in the opposite direction to the AUV, while BS2 moves in the same direction. (**d**) BS1 and BS2 move in the opposite direction to the AUV. The simulations were conducted across two scenarios. In the first scenario, the network radius was kept constant to assess the impact of BS motion, with variations in its direction and magnitude. In the second scenario, with a fixed direction and magnitude of BS motion, the focus shifted to evaluating the effects of network radii. In addition, the simulation was based on BS motion response data derived according to the WMO sea state code, as described in Section 3. This simulation incorporated various practical environments, including buoy mooring modeling, WMO sea state codes, and JONSWAP wave spectrum models, to obtain the BS motion response data. Therefore, unlike traditional network simulations that require numerous trials to obtain an average due to random conditions, this approach has the advantage of not necessitating such an extensive number of simulations.
## 3 GC\(\times\)GC Separation
### Column Setup  
Chromatographic columns are the key components of chromatographic systems. GC\(\times\)GC increases the peak capacity and associated separation capability by introducing two different dimensional separation mechanisms on the two columns [113; 123]. According to this concept, the orthogonality of separation is proposed. Separation orthogonality is maximized by using independent retention mechanisms in both dimensions. Previously, this concept was presented by combining polar-apolar (or apolar-polar) columns. However, later studies have reported that enhanced separation resolution can be obtained by using polar or apolar columns in the first dimension and semipolar columns in the second dimension under nonorthogonal conditions [9; 124]. The reason for the inefficient performance of combining apolar and polar may be that analytes that are retained excessively in one dimension are not retained at all in the other dimension because the chemistry of the two stationary phases is so different [125]. The choice of stationary phase polarity and column order must consider the analytes contained in the sample. In beer and wine samples, there are more polar compounds than apolar compounds. The most suitable column combination is polar-semipolar for global profiling [24; 126].  
Other important factors for column selection, especially the second dimension, are column length and diameter. To maintain the separation resolution achieved from the \({}^{1}\)D column (short modulation time), the \({}^{2}\)D separation time has to reach a sufficiently small value [126]. A short column between 80 and 200 cm long is used to completely elute the analytes within 10 s. Because the columns are shorter in the second dimension, applying a small column diameter to improve the separation efficiency was the first intention [127]. However, using different column diameters induces a flow-mismatch problem, and the two columns cannot both work at the optimal flow rate [128]. Another problem caused by the reduced column diameter in the second dimension is overloading. For a beverage sample, the contents of major and minor compounds may vary by nine orders of magnitude. To cover the trace compounds during VOC profiling, major compounds are over-concentrated. Overloading may lead to distortion of analyte peaks, resulting in data processing errors. Nowadays, both columns usually have matching column diameters. Due to the similar column diameters, the front pressure of both columns can remain similar. Thus, there is no need to sacrifice the performance of the other column in order to maintain the optimal gas flow rate in one column. In this way, the quality of the chromatographic data will be improved, and thus the semi-quantitative results will be enhanced.
# A Wideband Non-Stationary 3D GBSM for HAP-MIMO Communication Systems at Millimeter-Wave Bands
## 4 Results and Analysis
Figure 8 shows that the spatial CCF of different receiving and transmitting antenna pairs is significantly different. This is because different antennas have different positions and directions in space. Due to the different positions and directions of different antennas, the propagation paths experienced by the signals they receive will also vary. This results in signals between different antennas having different delays and phase differences, leading to changes in spatial CCF. The theoretical and simulation results are in good agreement. Therefore, the proposed channel model can describe the non-stationary nature of channels in the space domain. Due to the non-uniform fading characteristics of wideband at different frequency points, the GBSM channel model of HAP exhibits non-stationary characteristics in the frequency domain. In Figure 9, it can be seen that the normalized CCF in the frequency domain varies with the frequency interval \(\Delta f\) at three different starting frequencies \(f=38\) GHz, \(f=38.5\) GHz, and \(f=39\) GHz. From the figure, it can be seen that the CCF at different simulation frequencies has a significant difference. Our HAP channel is located in mmWave and has a large bandwidth, wideband HAP channel has different fading characteristics at different frequency. Therefore, it exhibits non-stationary characteristics in the frequency domain. The theoretical results and simulation results are basically consistent. Therefore, the proposed channel model can describe the non-stationary nature of channels in the frequency domain. Figure 7: Temporal ACF results at different degrees of scatterer aggregation (\(t=0\) s). The cumulative distribution functions (CDF) of the stationary bandwidth of our channel model at different frequencies and the model in [31] and the model in [32] are shown in Figure 10. The threshold is chosen as 0.9. The model in [31] and the model in [32] did not consider FCF, and the stationary bandwidth is infinite. The channel whose bandwidth less than the stationary interval can be considered as frequency stationary. Therefore, in the case of wide bandwidth at the mmWave band, it is necessary to consider the non-stationary characteristics of the frequency. Figure 8: Spatial CCF under different antenna spacing. Figure 9: CCF under different frequencies.
## 4 Results
### Implementation Details and Execution Platform  
The MILP formulation for the bus synchronization problem was coded and solved with IBM ILOG CPLEX Interactive Optimizer 12.6.3.0. To facilitate the process, a program in C was used to translate the instances from the.csv format to the LP-format that CPLEX uses. The pre-processing and post-processing of the obtained solutions was performed in Matlab (version R2015a-8.5.0). Additionally, Matlab was also used to develop the discrete event simulation model for estimating bus occupation as described in Section 2.2.  
The experiments were conducted on an HP ProLiant DL380 G9 high-end server equipped with two Intel Xeon Gold 6138 processors, each having 20 cores and a RAM memory of 128 GB. The computing resources were provided via the high-performance computing infrastructure of the National Supercomputing Center, Uruguay (Cluster-UY [61]).
# Practical Use of Starlink Downlink Tones for Positioning
## 3 Tones Frequencies Aggregation
Indeed, if the offset from _Fo_ is the same for all satellites, it can be captured in the user clock drift in Doppler-based positioning as a common frequency error.
# Calculation and Dressing Simulation of the Profile of the Form Grinding Wheel for Modified ZI Worms
## 3 Solving of Grinding Wheel Profile
### Solution of Grinding Wheel Dressing Trajectory  
As shown in Figure 11, it is the formation principle of the trajectory of the diamond roller dressing the grinding wheel. When the outer circle of the diamond roller is used to dress the grinding wheel, the arc on its side contacts the profile of the grinding wheel. Taking the center of the arc as the trajectory control point of the diamond wheel, the motion trajectory of the diamond wheel is an equidistant curve of the profile of the grinding wheel with a distance equal to the arc radius. The interpolation accuracy of the profile of the grinding wheel is controlled by adjusting the coordinate changes of the center of the arc.  
Suppose the radius of the arc is r, and the center of the circle is set as \(Pr(xt,y\prime,0)\). The contact point with the profile AB is assumed to be \(P(x,y,0)\). Then the position vector of the point P on the profile of the grinding wheel is defined as \(\textbf{O}\textbf{P}=(x,y,0)\), and the position vector of the center of the diamond wheel is regarded as \(\textbf{O}\textbf{P}\prime=(x\prime,y\prime,0)\). The tangential vector passing through the point P is expressed as follows:  
\[\textbf{O}\textbf{P}_{e}=(\frac{dy}{\sqrt{dx^{2}+dy^{2}}},\frac{-dx}{\sqrt{dx^ {2}+dy^{2}}},0) \tag{32}\]  
According to the vector relationship, the normal vector at point \(P\) is obtained as \(N_{p}=\pm(\textbf{O}\textbf{P}\prime-\textbf{O}\textbf{P})\). Due to \(k=(0,0,1)\), the normal vector is calculated as follows:  
\[Np=\textbf{O}\textbf{P}_{e}\times k \tag{33}\]  
By means of the Equations (32) and (33), the following equations are derived:  
\[\left\{\begin{array}{l}x\prime=x\pm r\cdot\frac{dy}{\sqrt{dx^{2}+dy^{2}}}\\ y\prime=y\mp r\cdot\frac{dx}{\sqrt{dx^{2}+dy^{2}}}\\ z\prime=0\end{array}\right. \tag{34}\]  
Among them, the tool-setting point for form grinding is regarded as the center point \(O\) of the addendum of the grinding wheel. The movement path of the center \(O\) of the diamond wheel is set as O'A\(\rightarrow\)AB\(\rightarrow\)BC\(\rightarrow\)CD\(\rightarrow\)DE\(\rightarrow\)FF\(\rightarrow\)FG\(\rightarrow\)GH\(\rightarrow\)HA, and this cycle is repeated until the required profile of the grinding wheel is dressed. The dressing trajectory is shown in Figure 12.  
Figure 11: Trajectory of grinding wheel dressing with diamond wheel.
## 3 Results and Discussions
### Outcomes of Scheme I  
In scheme I, a five-fold cross validation method was used in each record. So, there were five outcomes in every record in the scheme I. To facilitate marking, we used indices to mark the five parts. Table 2 showed the outcomes of the fivefold cross validation. When indices was 1, the mean MAE \(\pm\) SD equaled \(1.79\pm 1.73\) mmHg; when indices was 2, the mean MAE \(\pm\) SD equaled \(1.79\pm 1.76\) mmHg; when indices was 3, the mean MAE \(\pm\) SD equaled \(1.91\pm 1.84\) mmHg; when indices was 4, the mean MAE \(\pm\) SD equaled \(1.81\pm 1.79\) mmHg; whenindices was 5, the mean MAE \(\pm\) SD equaled \(1.99\pm 2.00\) mmHg. The scheme I's mean MAE \(\pm\) SD was \(1.85\pm 1.82\) mmHg. In the scheme I, both MAE and SD were low.
## 2 Materials and Methods  
### Data Acquisition, Phantom Description and VOI Segmentation  
Phantom images were acquired using a Siemens Biograph mCT PET/CT scanner (Siemens Healthineers, Chicago, IL, USA). It combines a 64-slice CT with a TOF PET axial field of view of 21.8 cm. The PET scanner is composed of four rings of 48 detector blocks, with each one containing 13 \(\times\) 13 lutetium oxyorthosilicate (LSO) crystals with dimensions of 4 \(\times\) 4 \(\times\) 20 mm\({}^{3}\). Three phantoms were used for PET/CT acquisitions: a PMMA cylindrical phantom with a 10 L nominal volume, an IEC NEMA Body Phantom (PET/IEC-BODY/P, Data Spectrum Corporation, Hillsborough, NC, USA) and an IEC NEMA Torso (ECT/TOR/P, Data Spectrum Corporation, Hillsborough, NC, USA), named P1, P2 and P3, respectively, in the following. All phantoms were firstly filled with a solution of distilled water and diethylenetriaminepentaacetic acid (DTPA) at a concentration of 0.2 g/L to prevent the binding of 90Y to phantom walls. Subsequently, a 90Y-trichloride solution was added. P1 was uniformly filled with a clinically relevant quantity of 90Y activity concentration of 0.2 MBq/mL. P2's spheres were filled with a 90Y activity concentration of 4.8 MBq/mL. Lastly, P3's liver compartment was filled with a 90Y activity concentration of 1.2 MBq/mL. Activity measurements, used as ground truth, were performed using an ENEA-INMRI portable triple-to-double coincidence ratio (TDCR) counter, which provides a relative standard uncertainty below 1% [22; 23; 24]. Weightings of liquid solutions were performed with high-precision balances, providing accuracies below 0.01 g (Sartorius, mod. ED224S, Goettingen, Germany). For all phantoms, images were acquired in the list mode for 30 min in a single-bed position PET/CT using Siemens ultraHD PET algorithm (i.e., ToF + HD PET). Different matrix sizes, numbers of EI and Gaussian filters were used in the image reconstruction process to evaluate the effects of these parameters on the data accuracy for clinical dosimetric purposes. VOIs were drawn based on CT images and a verification of volumes was performed in order to limit edge effects.
## 3 Results and Discussion
### Polarization Characteristics of Absorption Effects  
Aerosols with optical absorption effects often have an important impact on atmospheric visibility. In this part, we selected four kinds of aerosols with different absorption characteristics as our third experimental sample group: Na\({}_{2}\)SO\({}_{4}\), fly ash, Arizona dust, and carbon spheres, whose polarization data have also been shown in Figure 9. According to their appearance and references [26,28], carbon spheres are light-absorbing particles, and Na\({}_{2}\)SO\({}_{4}\) particles almost have no absorption effect. The reported imaginary parts of the refractive index of fly ash and Arizona dust [27,30,31] are within the range 0.002-0.03, meaning possible weak absorption effects. Based on the mean values, PFR and Wilk's Lambda of the experimental polarization indices in Figure 10, we can examine the influence of aerosol absorption on multidimensional polarization indices.  
From the scatter distribution of 24-dimensional polarization indices in Figure 9, the four types of sample data show different divisions and clustering. From Figure 10a, P-Hdop (60\({}^{\circ}\)), P-Pdop (60\({}^{\circ}\)), P-Pdop (85\({}^{\circ}\)), and R-Hdop (60\({}^{\circ}\)) can distinguish the four types of samples clearly. Besides, Figures 1 and 9 also show similar monotonic changes of these polarization indices, that is, the values of polarization parameters decrease with the increasing particle absorption characteristics (marked by red inverted triangle symbols). This experimental phenomenon is consistent with our previous research reports [32]. We can also see that, in H-Hdop (30\({}^{\circ}\)), only the Na\({}_{2}\)SO\({}_{4}\) that has no absorption effect is separated clearly.  
Figure 8: **(a)** Mean values of multidimensional polarization indices; **(b)** PFR indices; **(c)** Wilk’s Lambda indices. The red triangles mark index 2 (H-Pdop (30\({}^{\circ}\))), index 4 (H-Pdop (60\({}^{\circ}\))), index 16 (P-Pdop (115\({}^{\circ}\))), and the blue triangles mark index 10 (P-Pdop (30\({}^{\circ}\))), index 11 (P-Hdop (60\({}^{\circ}\))), index 12 (P-Pdop (60\({}^{\circ}\))).  
In this case, the incident light with a P (horizontal) linear polarized state and the 60\({}^{\circ}\) detection angle can achieve better discrimination of aerosols with different absorption characteristics. And based on the trend of polarization parameters, it is further possible to estimate the change of aerosol absorption property or absorption-type particle proportion.
## 2 Nanofluid Thermal Conductivity
### Effects of Nanoparticle Size on Nanofluid Thermal Conductivity
It was also suggested that Kapitza resistance led to the decrease in thermal conductivity at larger nanoparticle sizes, due to thermal waves scattering at the base liquid-nanoparticle boundaries. However, it was observed that the thermal conductivity of TiO\({}_{2}\)-water nanofluid increased with increased nanoparticle size. It was suggested that this increase was caused by nanoparticle clustering. The conflicting relationships between PVA- and water-based nanofluids were explained by the higher viscosity of PVA in comparison to water allowing a thicker adsorption layer to form around the nanoparticles. From the experimental observations discussed above, it can be suggested that smaller nanoparticle size leads to increased thermal conductivity. However, more experimentation should be completed to determine the significance of phonon scattering in nanofluid thermal conductivity.