esa-sceva/satcom-chunk-collection huggingface dataset:
# Free Surface Energy and Hansen Solubility Parameter Vector Field. Interface Thickness
###### Abstract  
In this paper, a three-dimensional vector field model is proposed, whose dimensions are the Hansen Solubility Parameters: dispersion parameter (\(\delta_{D}\)), polarity parameter (\(\delta_{P}\)), and hydrogen bonding parameter (\(\delta_{H}\)). The vector space that defines the field has the peculiarity of having a dispersion vector with a magnitude of 2 as its base vector, while the polarity and hydrogen bonding vectors have a magnitude of 1. A substance is characterised as a position vector, and the interaction between two substances is determined by calculating the vector difference of both, known as the interaction vector. The interaction among substances may involve solubility, swelling, cracking, surface tension, interface tension, and any physical phenomena where the intermolecular energies of dispersion, polarity or hydrogen bonding come into play. This paper studies free surface energy (surface and interfacial tension). It has been found that free surface energy is directly proportional to the square of the magnitude of the interaction vector. The proportionality constant, \(\tau\), is expressed in length units, has a value of 0.025 nm, and does not depend on the chemical nature of the substance or state of matter (solid, liquid or gas). The constant value \(\tau\) appears universal and aligns with the thickness of interfaces, thereby supporting [PERSON]'s hypothesis. This hypothesis asserts that interfaces possess actual thickness and are not merely mathematical surfaces, as originally postulated by [PERSON]. Moreover, it also has been found that the interface thickness, \(\tau\), is approximately equal to half of the Bohr radius, \(a_{0}\), which is defined by universal constants. Because the solubility parameters of thousands of substances are known and can be easily determined from their molecular structure, a good approximation of the surface and interfacial tension of any given substance can now be calculated. It has also been found that the contact angles of sessile droplets in three-phased systems can be calculated from the interaction vectors of the implicated substances.  
H  
Hansen solubility parameters; free surface energy; surface tension; interfacial tension; contact angle; vector field; interaction vector; interface thickness; cohesive energy
## 3 Results
### Extended Period Simulation (EPS) Analysis  
EPS were conducted over a 24 h cycle, analyzing demand fluctuations. The EPS was set with a 10 min step interval and a 24 h simulation duration based on SCADA telemetry data. Reservoir levels and pump operation patterns were analyzed to assess system efficiency. Peak demand periods resulted in pressure drops of 3% (11 kPa), while off-peak hours allowed for system recovery. Table 6 presents the reservoir, tank, and pump performance.  
Figure 8 presents pressure variation over the network. Service nodes are colored to show pressure zones, indicated by a color gradient from red (200-300 kPa) to magenta (900-1000 kPa), showing the pressure at different points throughout the network. The pressures of the network along its entire length are characterized as very high, exceeding 9 atm. The highest pressures are found in the flattest areas of the city to the north, where most of the network damages occur, due to the significantly elevated static pressures and the lack of pressure regulation infrastructure such as PRVs and isolation valves.  
Leakage detection simulations identified high loss areas. Figure 9 presents junction pressures and leakage rates. Junctions are colored circles representing different pressures (200-700 kPa) and sizes depending on the amount of leakage (0.098-1.2 m\({}^{3}\)/24 h). The total leakage is estimated at 10-20 m\({}^{3}\)/24 h, depending on the number of junctions with each level of leakage. Specific junction pressures vary widely and are indicated by the color of the node. The analysis of leakage hotspots indicates that leakages are pressure-induced, and occur in the flattest areas of the network, aligning with pressure distribution patterns.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time of Day** & **Reservoir/Pump Head (m)** & **Pump Flow Rate (L/s)** & **Tank Head (m)** & **Efficiency (\%)** & **Pressure Drop** \\ \hline
00:00 & 39.1 & 87.5 & 39.1 & 48.9 & –0.1\% \\
06:00 & 39.2 & 87.5 & 39.1 & 48.9 & –0.3\% \\
12:00 & 39.9 & 87.3 & 38.8 & 49.9 & –2.8\% \\
18:00 & 40 & 87.3 & 39.1 & 50 & –2.2\% \\
24:00 & 38.7 & 87.6 & 38.7 & 48.4 & 0.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reservoir, tank, and pump performance.  
Figure 8: Pressure variation across the network.
## 3 Results and Discussion
### Antifungal Activity  
The antifungal effect of the fibers (Table 4) was demonstrated on _Sabouraud_ dextrose agar.  
The antifungal assay revealed that the fibers inhibited the growth of _Candida albicans_ (Figure 5) and _Candida parapsylosis_. The inhibition zone of blank fibers could be explained by the alcohol content of the fibers [39; 40]. The diameter of the inhibition ring was only 3 mms for the blank fibers, compared to the itraconazole-containing fibers, which had diameters above 6 mm. The inhibition zone of the fibers could not be attributed only to the alcohol content [41], because, during fiber formation in the electrostatic field, the solvent evaporated. A flow rate of 2 mL/h led to a smaller inhibition zone, while increasing the needle-to-collector distance showed wider inhibition zones. Another study showed a similar result, where 3.8 mm inhibition zones were measured for empty fibers of PVA [42].  
The effectiveness of the solid dispersions was also demonstrated by [PERSON] et al. in the case of nanoparticles against _Candida albicans_ via in vitro and in vivo experiments on mice [43; 44]. In the study, the inhibition zones were equal in diameter for both the solid dispersions and the microfibers, suggesting the diffusion of ITZ into _Sabouraud_ dextrose agar.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Codes** & **E** & **D** & **N1** & **N2** & **N3** & **N4** & **N5** & **N6** & **N7** \\ \hline Inhibition zone & 3.0 & 7.1 & 7.1 & 6.9 & 7.5 & 7.0 & 6.6 & 6.5 & 6.2 \\ \hline \hline \end{tabular} E (empty fibers) and D (drug-containing dispersion) for samples N1 to N7.  
\end{table}
Table 4: Inhibition zones of the antifungal assay.  
Figure 5: Inhibition zones for Candida albicans for samples (N1–N7), empty fibers (E), and dispersion of ITZ (D).
# An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
## 4 RiceMan Application Development
### Observation Data Composition  
RiceMan implements the observation composition mechanism to identify diseases from multiple observations. If observations are located nearby each other, occur coincidentally, and are composable, then they are composed and are collectively treated as a single observation. Figure 7 illustrates an example in which each observation \(O_{1},O_{2}\), and \(O_{3}\) occurs in different geolocations and dates as described in Table 3.  
Here, suppose that \(O_{1}\) is collected first; after that, \(O_{2}\) and \(O_{3}\) are collected. RiceMan users can compose past observations with the current observation for disease identification, which is implemented as follows:  
1. Past observations that satisfy three conditions are retrieved: a distance threshold, a date threshold, and a composability boolean, from the database;  
Figure 6: Sequential steps of using the RiceMan’s user interfaces from providing observation data (a user’s input) to receiving the results.  
Figure 7: An example of practical observations \(O_{1},O_{2}\), and \(O_{3}\) in the study where \(O_{1}\) falls outside the distance and duration threshold of \(O_{2}\) and \(O_{2}\) falls inside the thresholds of \(O_{3}\).  
2. An OWL class expression is created from a database object, representing a current observation manifested with certain characteristics and intersects it with the retrieved past OWL class expressions;
3. The new observation is used for the subsequent disease identification based on the subsumption reasoning.  
The observation composition mechanism helps RiceMan users delve into specific results when a single observation captures partial or broad abnormalities. Nevertheless, this mechanism may yield the same results as considering a single observation. That is when there are no observations that satisfy the three conditions. For instance, using the composition function with \(O_{2}\) yields the same results as without using this function, i.e., it returns the rice brown spot fungal disease. This is because there are no nearby observations with \(O_{2}\). On the other hand, RiceMan yields the rice blast fungal disease, the rice sheath rot fungal disease, and the rice brown spot fungal disease for \(O_{3}\) when the composition function is disabled (cf. the top part of Figure 8). In contrast, it yields the rice brown spot fungal disease for \(O_{3}\) when this composition functionality is opted in (cf. the bottom part of Figure 8). When a single observation contains broad abnormal characteristics, diverse results can be obtained. Hence, this composition mechanism helps users narrow the disease identification from many possible disease candidates.
## 3 Results and Discussion
### SSR Detection Based on Optimal Features
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{6}{c}{**KNN**} \\ \cline{2-7}  & \multicolumn{2}{c}{**RF-CA**} & \multicolumn{2}{c}{**SPA-CA**} & \multicolumn{2}{c}{**SFS-CA**} \\ \cline{2-7}  & Infected & Healthy & Infected & Healthy & Infected & Healthy \\ \hline Infected & 39 (79.6\%) & 10 (20.4\%) & 32 (69.6\%) & 14 (30.4\%) & 32 (69.6\%) & 14 (30.4\%) \\ Healthy & 0 & 46 (100\%) & 0 & 49 (100\%) & 0 & 49 (100\%) \\ Kappa value & & 0.791 & & 0.702 & & 0.702 \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{6}{c}{**LDA**} \\ \cline{2-7}  & \multicolumn{2}{c}{**RF-CA**} & \multicolumn{2}{c}{**SPA-CA**} & \multicolumn{2}{c}{**SFS-CA**} \\ \cline{2-7}  & Infected & Healthy & Infected & Healthy & Infected & Healthy \\ \hline Infected & 44 (89.8\%) & 5 (10.2\%) & 39 (88.6\%) & 5 (11.4\%) & 41 (89.1\%) & 5 (10.9\%) \\ Healthy & 6 (13\%) & 40 (87\%) & 5 (9.8\%) & 46 (90.2\%) & 5 (10.2\%) & 44 (89.8\%) \\ Kappa value & & 0.768 & & 0.788 & & 0.789 \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{6}{c}{**NB**} \\ \cline{2-7}  & \multicolumn{2}{c}{**RF-CA**} & \multicolumn{2}{c}{**SPA-CA**} & \multicolumn{2}{c}{**SFS-CA**} \\ \cline{2-7}  & Infected & Healthy & Infected & Healthy & Infected & Healthy \\ \hline Infected & 38 (77.6\%) & 11 (22.4\%) & 32 (72.7\%) & 12 (27.3\%) & 35 (76.1\%) & 11 (23.9\%) \\ Healthy & 2 (4.3\%) & 44 (95.7\%) & 1 (2\%) & 50 (98\%) & 0 & 49 (100\%) \\ Kappa value & & 0.728 & & 0.720 & & 0.766 \\ \hline \hline \end{tabular} \
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{6}{c}{**ELM**} \\ \cline{2-7}  & \multicolumn{2}{c}{**RF-CA**} & \multicolumn{2}{c}{**SPA-CA**} & \multicolumn{2}{c}{**SFS-CA**} \\ \cline{2-7}  & Infected & Healthy & Infected & Healthy & Infected & Healthy \\ \hline Infected & 44 (91.7\%) & 4 (8.3\%) & 39 (97.5\%) & 1 (2.5\%) & 41 (97.6\%) & 1 (2.4\%) \\ Healthy & 5 (10.6\%) & 42 (89.4\%) & 5 (9.1\%) & 50 (90.9\%) & 5 (9.4\%) & 48 (90.6\%) \\ Kappa value & & 0.810 & & 0.872 & & 0.873 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{6}{c}{**SVM**} \\ \cline{2-7}  & \multicolumn{2}{c}{**RF-CA**} & \multicolumn{2}{c}{**SPA-CA**} & \multicolumn{2}{c}{**SFS-CA**} \\ \cline{2-7}  & Infected & Healthy & Infected & Healthy & Infected & Healthy \\ \hline Infected & 48 (88.9\%) & 6 (11.1\%) & 49 (96.1\%) & 2 (3.9\%) & 53 (94.6\%) & 3 (5.4\%) \\ Healthy & 4 (9.8\%) & 37 (90.2\%) & 3 (6.8\%) & 41 (93.2\%) & 2 (5.4\%) & 37 (94.6\%) \\ Kappa value & & 0.787 & & 0.894 & & 0.892 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The classification results of models based on the optimal wavelengths.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 3 Methodology
### Self-Supervised Loss Functions
#### 3.5.5. Keypoint-Driven Image Reprojection Loss (\(L_{img\_reproj}\))  
This loss is specifically used in Stage 2 to improve the template-to-transformed-template alignment capability of the learned keypoints. Its objective is to encourage the predicted keypoints to be such that an estimated transformation between two template images (a source template and its transformed version), based on these keypoints, accurately warps the source template to match the target-transformed template. The mechanism involves extracting keypoint coordinates from heatmaps predicted for a source template image and a target-transformed template image, assuming a direct \(k\)th keypoint correspondence based on the learned class-specific keypoint ordering. An affine transformation (\(T_{est}\)) is estimated from the \(K\) correspondences (\(\{p_{src,k}\leftrightarrow p_{tgt,k}\}_{k=1}^{K}\)), using least squares. The source template image (\(I_{src}\)) is then warped using this estimated transformation (\(T_{est}\)) to produce \(I_{src}^{\prime}=\text{Warp}(I_{src},T_{est})\). The loss is calculated as the mean-square error between the warped-source template image (\(I_{src}^{\prime}\)) and the target-transformed template image (\(I_{tgt}\)) as follows:  
\[L_{img\_reproj}=\text{MSE}(I_{src}^{\prime},I_{tgt})=\frac{1}{H_{img}\cdot W_{img }}\sum_{h=0}^{H_{img}-1}\sum_{w=0}^{W_{img}-1}(I_{src}^{\prime}(h,w)-I_{tgt}(h, w))^{2} \tag{8}\]  
where \(H_{img}\) and \(W_{img}\) are the height and width of the images, and the summation is over all the pixel locations (\((h,w)\)). This loss directly pushes the keypoints toward locations that result in accurate image alignment between templates and their transformations. This loss is used in Stage 2 but not in Stage 1.
## 2 Materials and Methods
### Electrochemical Measurements  
Electrochemical measurements were performed using a cylindrical cell (18650) assembled in an argon-filled glove box. LiNi0.8 Co0.15 Al0.05O2/NCA (MTI, CA, USA) was used as the counter electrode; monolayer polypropylene film (Celgard 2400) was used as the separator, and 1-mol/L LiPF6 dissolved in a mixture of ethylene carbonate and diethyl carbonate (EC/DEC = 1:1, v/v) was used as the electrolyte. The working electrode was prepared with active materials, acetylene black, carboxymethyl cellulose (CMC), and styrene-butadiene rubber (SBR) obtained from MTI, CA, USA with the weight ratio of 93:1:1:5 in deionized water. The mixture was stirred for 5 h at room temperature; then, it was coated onto both side of copper foil using the doctor blade method. The copper foil was dried at 80 \({}^{\circ}\)C for 30 min in a vacuum oven. The load mass of active material was approximately 18 mg/cm\({}^{2}\). The overall assembly process of cylindrical Li-ion cells is described elsewhere [19; 20]. The electrochemical performance of materials was studied by galvanostatic charge-discharge cycling tests using a battery testing system (Neware BTS 6000, Neware Electronic Co., Shenzen, China) at various current rates (1 C = 372 mAh g\({}^{-1}\)) and room temperature (25 \({}^{\circ}\)C).
###### Abstract  
This paper analyzes low-Earth-orbit (LEO) satellite downlinks when an airborne interference source moves parallel to the satellite trajectory by considering the relative angle differences between the satellites and the interference sources. To make the experimental interference situations more like actual environments, the LEO trajectories are obtained from two-line element set (TLE) data. Airborne interference sources with various altitudes move parallel to the LEO trajectories, and a jamming to signal \((J/S)\) ratio is calculated based on the relative angle differences between the ground station, the LEO satellite, and the interference source. To accurately calculate the \(J/S\) ratio, we should apply the sidelobe gain from which the interference signal enters to the ground station antenna. In order to calculate the relative angle difference \(\psi\), the coordinates of the satellite and the interference source are converted from the World Geodetic System 1984 (WGS84) to the ground station-centered east-north-up (ENU) system. The resulting \(J/S\) ratio demonstrates that the distance and the relative angle difference \(\psi\) between the ground stations, LEO satellite, and airborne interference source appear to be important factors causing changes in the \(J/S\) ratio. Among them, the relative angle difference \(\psi\), which determines the sidelobe gain of the ground station antenna, is the most significant factor affecting the \(J/S\) ratio variation.  
LEO satellite; link budget; Trajectory; antenna radiation patterns; interference situation; relative angle; coordinate system conversion; \(J/S\) ratio 2024 16 321 10.3390/rs16020321
## Materials and Methods
### Fiber-Optic Sensing System and Data Evaluation
The whole process of data analysis is summarized in Figure 1.  
For sensor ranges that can be approximated as localized sensors, data processing was carried out based on the procedure of random quadrature demodulation (RQD) proposed in [35] for this type of distributed interferometric data. High pass filtering required in RQD was represented here by the data evaluation step of numerical differentiation as this leads to a similar effect. Subsequently, the signal was rectified and summed up over the relevant range of bins. Numerical simulation of the propagation of acoustic waves in cylindrical waveguides was carried out using the MATLAB toolbox PC disp (Pochhammer-Chree dispersion) [36]. Pipe geometry chosen for simulations was defined by an inner radius of 20.35 mm and an outer radius of 24.15 mm and was embedded in one layer. Assuming steel as the pipe material further parameters used were a bar speed of 5047 m/s, Poisson ratio of 0.3 and a shear modulus of 76.9065 GPa.
## 4 Discussion  
### Quantification of Bedforms  
The previous studies have classified dune length and height into various distribution types based on bedform data from flume experiments and field observations. For instance, the probability density distribution of bedform elevation has been described as a Rayleigh distribution [50], a Weibull distribution [51], an Exponential distribution [52], a Gamma distribution [53], and a Beta distribution [54]. In this study, dune bedforms were categorized into 2D and 3D types, a distinction not commonly observed in prior research. Additionally, this paper employed multiple definitions of dune length and height (Figure 5), referencing the existing literature [5]. The distribution patterns of dune length and 2D dune height observed in this study align with those reported in earlier studies [5], specifically lognormal and Gamma distributions, which indirectly supports the validity of the conclusions drawn in this research.  
However, the 3D dune heights in this study followed a lognormal distribution and differed from the distribution pattern of the 2D dune heights, and the crest heights and trough depths also differed from the previous findings [5, 51, 52, 53]. The primary reason for these discrepancies is that this study distinguished the dune bedforms into 2D and 3D according to the characteristics of the longitudinal and cross-sectional morphology profiles after the experiments to analyze the shape parameters distribution patterns separately. From the perspective of enhancing the scope of existing research, differentiating the bedforms to characterize the distribution of physical parameters of dunes is particularly significant.  
This study focused on the characteristics of the dune length and height, the crest height, and the trough depth. Future research should include additional bedform features, such as continuity and directionality. Moreover, examining a broader range of flow conditions and bedload grain size compositions is essential to validate the applicability of the findings presented here.
## 2 Passive Radar Performance Principle
### Bistatic Radar Equation  
The radar equation is a mathematical expression that relates the radar range at which the specific performance is obtained to the parameters that characterize the radar (transmitted power, antenna gains,  
Figure 4: Geometry for bistatic range resolution calculation.  
Figure 5: Geometry for Doppler resolution calculus.  
operation frequency, _etc._), the desired target (radar cross-section (RCS)) and the radar scenario (bistatic geometry, propagation losses, multipath, clutter).  
For the bistatic geometry depicted in Figure 2, the power scattered by a target can be calculated as:  
\[P_{R}=\frac{P_{T}G_{T}G_{R}\lambda^{2}\sigma_{bis}}{\left(4\pi\right)^{3}\left( R_{R}R_{T}\right)^{2}}\cdot A \tag{5}\]  
where \(P_{R}\) is the received power, \(P_{T}\) is the power transmitted by the opportunity emitter, \(G_{T}\) and \(G_{R}\) are the transmitter and receiver antenna gains, respectively, \(\lambda\) is the signal wavelength, \(A\) represents the total propagation losses and \(R_{T}\) and \(R_{R}\) are the target-to-transmitter and target-to-receiver distances, respectively. The bistatic RCS, \(\sigma_{bis}\), models the power scattered by the target towards the PR when it is illuminated by the OI.
# Improvement of Intake Structures in a Two-Way Pumping Station with Experimental Analysis
## 1 Introduction
He concluded that when the circumferential pressure pulsation of impeller caused by rotor-stator interaction is not uniform, which can cause a kind of pitch diameter mode resonance of impeller. [PERSON] et al. [13] investigated the effect of various geometry features on the pressure pulsations by Taguchi methods in a double suction pump; they concluded that the cutwater gap and vane arrangement are the two strongest influences on the pressure pulsation. In this paper, the inlet flow field of the two-way pumping station was investigated, specifically focused on the impeller inflow. Considering that little research has been done on this kind of pumping station, this paper creatively optimizes the shape of the bell mouth of the two-way intake. Three different bell mouth heights were designed. The unsteady pressure at the outlet of intake were measured by the model test. The pressure fluctuation was analyzed via the Welch method and the continuous wavelet transform (CWT). The influence of bell mouth height on the pressure fluctuation and flow stability were analyzed. Finally, the inflow situation of the impeller was improved.