esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Connections between Microplastics and Heavy Metal Pollution within Floodplain Soils
## 3 Results
### Spatial Relationships between Plastic and Metal Concentrations  
#### 3.2.1 Total Plastic Concentration  
Total plastic concentrations (Ptot, calculation presented in Section 2.3) range from 0 to 21.12 p kg\({}^{-1}\) with an overall average concentration of 1.66 p kg\({}^{-1}\). Detailed descriptions of plastic particles' characteristics, polymer types, and additional information can be found in [PERSON] and Opp (2020) for MEP (mesoplastic, >5 mm) and CMP (coarse microplastic, >2 mm) as well as in [PERSON] et al. (2021) for LMP (large microplastic, >1 mm) and MMP (medium microplastic, >0.5 mm) size classes.  
Considering the Plot concentrations at the catchment level, it is clear that the mean values per sampling site of 2.65 p kg\({}^{-1}\) in the upper reaches (site ELM) decrease in the middle reaches (site ROT: 1.01 p kg\({}^{-1}\), site STD: 1.33 p kg\({}^{-1}\)) and increase again up to 1.96 p kg\({}^{-1}\) in the lower reaches (site LIM) (Figure 3). The mean differences in Plot concentrations between the sampling sites are significant (ANOVA \(p\) < 0.01). Despite this finding, the maximum Plot concentrations increase with increasing flow length. At the sampling site level, the highest average Plot concentrations are always reached near channel sites (proxim floodplain) and decrease with increasing distance to the river (distal floodplain), except for point STD-3 (inactive flow channel) (Table A1). The site LIM with the sampling point LIM-1 (river bank) shows the absolute maximum average Plot concentration (5.26 p kg\({}^{-1}\)) of all investigated floodplain soils. The vertical distribution is clearly divided into two sections: Higher concentrations of Plot always occur in surface soils (Ah or Ap horizons) or in the upper 40-50 cm of the soils. Below this depth, the contents decrease clearly and zero values occur more frequently (Figure 4). As also documented in one of the former studies, the main amount of Plot and the highest concentrations are thus present in the young upper soil layers of the fluvial floodplain sediment ([PERSON] et al., 2021).  
Figure 3: Overview of plastic and heavy metal loads in the Lahn catchment. Plot: total plastic concentrations (p kg\({}^{-1}\)), calculated as the mean value from all samples of a transect; Ef: enrichment factor; PLI: pollution load index (both calculated as the mean value from all metals analyzed in soil samples of a transect).  
Comparing the summed Plot contents with those of other studies in floodplain soils, it is apparent that the general level of plastic pollution within the Lahn River catchment seems to be lower than in other studied catchments [18,19,39] or than the often studied agricultural topposis [12,14,15,16,17,68]. Nevertheless, the wide spatial distribution indicates a general accumulation of plastic particles in floodplain soils, as is already known for other pollutants such as heavy metals [69].
## 4 Experimental Evaluation
### Efficiency Comparison
#### 4.2.6 Discussion  
Based on the results of our extensive experiments with embedding methods and similarity measures in Sections 4.2.3 and 4.2.5, we observe that the latter technique is _better_ than the former one to compute the similarity of nodes in graphs for the following reasons.  
* First, similarity measures _outperform_ embedding methods with all datasets in terms of effectiveness except with the BlogCatalog dataset where NetMF (i.e., with \(d\) = 256) shows the better accuracy than SimRank*; however, its efficiency is 54% less than that of SimRank* with this dataset.
* Second, similarity measures show _better_ efficiency with all datasets except with the Wikipedia dataset where DeepWalk (i.e., with \(d\) = 64) is almost 3.2 times faster than JacSim; however, for this dataset, JacSim shows better effectiveness than DeepWalk and significantly outperforms it in terms of all five evaluation metrics as observed in Table 13.
* Third, similarity measures have a very _low_ number of parameters than embedding methods, thereby leading to a _simpler_ parameter tuning process to possibly obtain a better accuracy; for example, JPRank has only three parameters as \(\alpha_{1}\), \(\alpha_{2}\), and \(\beta\) in Equation (A10), while DeepWalk has six parameters as the window size, walk length, number of dimensions, number of walks, size of training data, and learning tare.  
In addition to the above findings, we observed that DeepWalk and its variants (i.e., Line and NetMF) show better effectiveness than other embedding methods in the task of similarity computation of nodes in graphs. Furthermore, it is shown that increasing the value of \(d\) (i.e., number of dimensions) does not help improve the accuracy of embedding methods in computing the similarity of nodes.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 1 Introduction
### Contribution  
Beyond the existing studies, we formulate expressions to approximate the area and ASE of a HAP beam footprint pointed at any given distance within the HAP service area. Additionally, we propose an enhanced and validated recursive beam-pointing algorithm, which forms cells over an extended coverage area using multiple beams from the HAP. The algorithm compensates for broadening, starting from the sub-platform point cell (SPPC), and it provides flexibility on cell size variations and the level of overlap needed between the cells. Proper overlap control considerably enhances the HAP system performance [22]. We validate that the resulting beam boresight coordinates is near optimal by using SA. Whereas the use of SA for cellular placement has been studied previously [10; 23], the studies have all focused on terrestrial base station deployment instead. In a practical HAP system, the proposed algorithm will run once at the deployment stage to acquire the boresight coordinates required to form contiguous cells. Specifically, the contributions of this paper are:  
* A closed-form expression for the evaluation of the area covered by a HAP beam footprint on the ground when pointed at any given distance away from the sub-platform point is derived. This is useful for estimating the number of cells required to provide adequate coverage over a given service area.
* Theoretical expressions for the evaluation of average per-user capacity, average SE, and average area SE of a cell pointed at any given distance from the sub-platform point are derived and used to analyse the performance bounds of the extended HAP coverage.
* A recursive beam-pointing algorithm [9] with flexibility to control the amount of overlap between neighbouring beams while maintaining adequate coverage is enhanced and validated with the theoretical frameworks and derivations, and simulated annealing. The algorithm minimises the level of interference, which arises due to the overlap of neighbouring antenna beams main lobes.
* An elaborate discussion on a technique for extending contiguous coverage from a HAP notably beyond the much studied \(\leq\)30 km radius area and up to \(\geq\)60 km, by exploiting the multiple beam-forming capability of a horizontal planar antenna array and the favourable signal propagation characteristics of HAPs.
# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 3 Representing Prior Knowledge
### Generating by Density  
As discussed in Section 3.1, brightness is insufficient to separate ships from land, but density is a promising feature, because the pixels on the ships are more dense than that on land. We use DBSCAN [84] for the filtering in this stage. The input of the DBSCAN is the foreground pixels of the brightness filtering. DBSCAN owns two hyper-parameters to tune: \(Eps\) and \(MinPts\). Two points are considered neighbors only if the distance between them is no bigger than \(Eps\). A core point has at least \(MinPts\) points in its neighbors.  
Instead of adopting the strategy introduced in the original paper [84], we use the metric IoU to tune the hyper-parameters. Experiments are conducted using different combinations of \(Eps\) and \(MinPts\), and the final set of parameters with the highest IoU is selected. The IoU of a single image and the IoU of the whole dataset are calculated as shown in Equations (3) and (4):  
\[IoU=\frac{U}{\hat{X}+X-U} \tag{3}\]  
\[Average\ IoU=\frac{\sum_{i=1}^{n}I_{i}\times B_{i}}{\sum_{i=1}^{n}B_{i}} \tag{4}\]  
In Equation (3), \(U\) represents the number of points where the clustering result overlaps with the real ship. \(X\) represents the number of points in the clustering result. \(\hat{X}\) represents the number of real ship points. In Equation (4), \(n\) is the total number of images. \(I_{i}\) represents the IoU of the ith image. \(B_{i}\) represents represents the number of targets on the \(i\)th image.  
After sequentially applying K-Means clustering and DBSCAN clustering, for each pixel on the SAR images, we obtain a binary value for it. A pixel with value one is considered a part of ship, while a pixel with value zero is taken as background. Such matrix is denoted as prior knowledge map because the values are calculated based on prior knowledge of SAR images. The prior knowledge map has the same size as the SAR image but with a single channel.
###### Abstract  
This study assessed the bleaching and abrasiveness levels of different kinds of toothpaste with various RDA values on nanohybrid and microhybrid composite samples using a novel Press-on Force-Guided brushing simulator. One hundred and forty disc-shaped samples were prepared using two nano-hybrid and three microhybrid composites and divided randomly into four subgroups (n = 7). The samples were immersed in a coffee solution for 144 h and then brushed using R.O.C.S. (Remineralizing Oral Care Systems) brand toothpaste with different RDA values [Sensitive Instant Relief (SIR), Sensation Whitening (SW), and their combination with PRO Polishing (PP) (once a week)] using a brushing simulator for 140, 280 and 560 strokes (140 strokes correspondence to one week of real-time brushing). The level of surface roughness and color change (\(\Delta\)E) were measured before and after the simulated brushing. Color changes were evaluated in Photoshop CC software through \(\Delta\)E\({}_{0}\)* values generated from before and after L, a*, b* parameters on sample photographs taken by a mobile dental photography tool. The surface structure of samples was measured before and after the brushing using a profilometer. The measurements were analyzed in SPSS V23 software by Analysis of Variance and the Bonferroni Test, and the level of significance was set at <0.05. Regarding \(\Delta\)E values comparisons, there were no significant differences between the toothpastes after 2 weeks of brushing. SW (2.82 \(\pm\) 1.24), SIR + PP (2.78 \(\pm\) 0.98), and SW + PP (2.84 \(\pm\) 1.22) values were found to be similar after one month of brushing (\(p<\) 0.007). Regarding surface roughness comparisons between the toothpastes, two-week and one-month brushing values were found to be similar and statistically rougher than the initial values. Using R.O.C.S. PRO Polishing with low-abrasive toothpaste may increase the whitening effect by enhancing color recovery.  
tooth brushing; surface roughness; whitening toothpaste; discoloration; abrasiveness 2025
## 2 Materials and Methods
### Minerals Content Determination  
#### 2.3.1 Reagents and Standards  
Water was purified in the Mili-Q system (Millipore; Vienna, Austria), the used chemicals were of analytical grade. Hydrated lanthanum chloride (Cl\({}_{3}\)La \(\bullet\) 7H\({}_{2}\)O) used in determination of minerals content was purchased from Merck (Darmstadt, Germany). Other reagents, ammonium molybdate VI, sodium sulfate IV and hydroquinone were purchased from \"POCH\" S. A. (Gliwice, Poland). Minerals standards (magnesium, potassium, calcium and phosphorus) were diluted with 0.1 M nitric acid at the concentration of 1 mg/cm\({}^{3}\).
## 2 Materials and Methods
### Field Data Collection
#### 2.5.3. Surface Temperature Data  
Nadir-looking T\({}_{\mathrm{s}}\) data were measured using SI-111 IRT sensors (Apogee Instruments, Logan, UT, USA). Each measurement station had one SI-111 IRT sensor installed 1 m above the canopy, nadir-looking, during the data collection campaign at IIC and LIRF. The IRT sensors were placed in a 4-meter-tall vertical post and raised to higher heights until the canopy reached maximum h\({}_{\mathrm{c}}\), always keeping the 1-meter distance between the sensor and the top of the canopy. The FOV of SI-111 IRT sensors is a 22\({}^{\circ}\) half-angle. The sensors have a fast response time (<1 s) and a small uncertainty (\(\pm\)0.20 \({}^{\circ}\)C) when the target temperatures are between \(-\)20 \({}^{\circ}\)C and 65 \({}^{\circ}\)C. The ground-based area where most of the thermal radiation is sensed by the IRTs was equivalent to a 3-meter-diameter circumference. If we consider the upper part of the canopy, the footprint of the area sensed by the IRTs was about 1 m in diameter. The T\({}_{\mathrm{s}}\) data, alongside measured R\({}_{\mathrm{n}}\) and buried sensors to calculate surface G, were recorded every minute and averaged every 15 min in either a CR1000 or CR3000 datalogger (Campbell Scientific Inc., Logan, UT, USA) in each measurement station at LIRF and IIC. The SI-111 IRT sensors provided the input data to run the TSEB approaches in this study considering all RS sensors but the MSR.
# A Method for Estimating the Resultant Expanded Uncertainty Value Based on Interval Arithmetic
## 5 Example of the Proposed Analysis Method Application
### Second Example  
In the second example, the analysis method was employed to ascertain the resulting expanded uncertainty value of the measurement chain outlined in the study [10], specifically when the mentioned measurement chain handles a polyharmonic input signal. As per the referenced work, the uncertainty vector for successive error signals is structured as:  
\[\mathbf{U}=\begin{bmatrix}U_{x,r}&U_{x,d,1}&U_{x,d,2}&\ldots&U_{x,d,N}\end{bmatrix}, \tag{25}\]  
where the number \(N\) of the components of the indicated vector depends on the number of analyzed harmonics of the dynamic error signal, the subsequent variances of which are described by the equation:  
\[\sigma_{x,d,i}^{2}=\frac{\left(E_{s,o,i}\cos\bigl{(}\hat{\varphi}_{y}(\omega_{ s,o,i})\bigr{)}-E_{s,o,i}\bigr{)}^{2}+\left(E_{s,o,i}\sin\bigl{(}\hat{\varphi}_{y}( \omega_{s,o,i})\bigr{)}\right)^{2}}{\hat{\varphi}_{y}(\omega)}\approx-6.26 \cdot 10^{-13}\omega^{2}-5.73\cdot 10^{-7}\omega, \tag{27}\]  
where the amplitude of the selected harmonic  
\[E_{s,o,i}=\frac{\pi}{8}(2i-1)^{-2}E_{s,o}, \tag{28}\]  
depends on the amplitude \(E_{s,o}\) of the processed signal [10]. As indicated in the cited work, the harmonics of the dynamic error signal are analyzed whose pulsation meets the condition \((2i-1)\omega_{x,o}<\frac{1}{2}\omega_{s}\) for the sampling pulsation \(\omega_{s}=2\pi 48,000\) rad. The expanded uncertainty values for the analyzed signals are as follows:  
\[U_{x,r} =c_{n}\sigma_{x,r}, \tag{29}\] \[U_{x,d,i} =c_{d}\sigma_{x,d,i}, \tag{30}\]  
where \(\sigma_{x,r}^{2}=0.13\) uV [10].  
Analyzing the case where the pulsation of the signal \(s(t)\) processed by the discussed measurement chain is \(\omega_{s,o}=2\pi 4000\) rad, the vector described in Equation (25) takes the form:  
\[\mathbf{U}=\begin{bmatrix}U_{x,r}&U_{x,d,1}&U_{x,d,2}&U_{x,d,3}&U_{x,d,4}\end{bmatrix}, \tag{31}\]while the matrix of coherence coefficients takes the form:  
\[\mathbf{h}=\begin{bmatrix}1&h_{r,d,1}&h_{r,d,2}&h_{r,d,3}&h_{r,d,4}\\ h_{d,1,r}&1&h_{d,1,2}&h_{d,1,3}&h_{d,1,4}\\ h_{d,2,r}&h_{d,2,1}&1&h_{d,2,3}&h_{d,2,4}\\ h_{d,3,r}&h_{d,3,1}&h_{d,3,2}&1&h_{d,3,4}\\ h_{d,4,r}&h_{d,4,1}&h_{d,4,2}&h_{d,4,3}&1\end{bmatrix}, \tag{32}\]  
where the subsequent values of its elements are determined following Equation (14), akin to the previous example.  
Ultimately, the calculation of the resulting expanded uncertainty value is carried out in accordance with Equation (7), after substituting the values specified in Equations (31) and (32). The same analysis method was also applied in the study [19], although it did not incorporate the correction proposed in Equation (13), leading to less precise results compared to utilizing the suggested method version.
# A Convex Optimization Algorithm for Electricity Pricing of Charging Stations
## 4 Case Studies
### Discussion  
The Pareto front obtained is shown in Figure 6a, and an equilibrium solution is selected to show the results. As shown in Figure 6b, charging price is higher in the peak period and lower in the valley period, so as to guide electric vehicle users to charge in the valley period. However, charging prices are the same as in many periods of time, in order to avoid users concentrating on charging at the low price period, resulting in new load peaks. Under the guidance of charging price, the charging power of different electric vehicles is shown in Figure 6c. It can be seen that charging load is mainly concentrated in the night time and some electric vehicles charge in the afternoon, due to their parking characteristics. The load curve of distribution network after orderly charging of electric vehicles is shown in Figure 6d. Under the guidance of charging price, charging load mainly concentrates on the period of low power consumption, which greatly reduces the load fluctuation of the distribution network. It can be seen that the cooperative game between DSO and CSs can achieve win-win situation and maximize social welfare.  
Figure 4: The basic price of DSO.  
Figure 5: The parking time of EVs.  
Figure 6: Simulation results. (**a**) shows the Pareto front. (**b**) shows the charging prices. (**c**) shows the charging load of charging stations. (**d**) shows the total load of distribution system.  
The voltage distribution in the distribution network is shown in Figure 7. It can be seen that the return mapping algorithm can correctly restore the original variables. At the same time, the minimum voltage of distribution network is 0.9921 pu, which occurs at 9 a.m. on bus 31. The minimum operating voltage allowed in the distribution network is 0.93 pu. In other words, the voltage security of the distribution network can be guaranteed under the scheduling strategy in this paper. As shown in Figure 8, the relaxation error of MILP is larger than that of MISOCP but both are less than \(10^{-7}\). Compared with the numerical value of decision variables, this part of the error can be neglected. It can be seen that the second order cone relaxation has high accuracy. At the same time, polyhedral approximation will not greatly reduce the accuracy of the model.  
In this paper, the proposed algorithm was compared with GA [14] and PSO [15]. As can be seen from Table 1, the performance of GA and PSO is not ideal in solving this problem. In contrast, MISOCP and MILP can obtain the optimal solution in a very short time. Besides, MISOCP and MILP get the global optimal solution according to the convex optimization theory. Moreover, MILP is more efficient than MISOCP without losing accuracy. Therefore, the polyhedral approximation algorithm proposed in this paper can effectively improve the efficiency of solving this problem.  
Figure 8: Relaxation error of the model.  
Figure 7: Voltage distribution in the distribution network.
## Materials and Methods  
### Sample Preparation  
Two synthetic coals with a specific kind of nitrogen functional group (also known as fuel nitrogen) were prepared from nitrogen compounds and cellulose by compaction heat-treatment using an electrical-heating high-pressure reactor, as illustrated in Figure 1. Here, two nitrogen compounds, 8-hydroxyquinoline (C\({}_{9}\)H\({}_{7}\)NO) and 4-hydroxycarbazole (C\({}_{12}\)H\({}_{9}\)NO), were used to synthesize synthetic coals with cellulose according to our previous work [36]. The synthetic coals have physical properties highly similar to real coals. The X-ray photoelectron spectroscopic (XPS) analysis demonstrated that only one binding energy peak appears at 398.4 eV or 400.7 eV in two synthetic coals as illustrated in Figure 2, indicating fuel nitrogen of pyridinic or pyrrolic forms in two synthetic coals, respectively. The compaction heat-treatment did not change the chemical form of fuel nitrogen from 8-hydroxyquinoline or 4-hydroxycarbazole. The synthetic coal with pyridinic nitrogen was denoted as \"M6\" hereafter, while the other containing pyrrolic nitrogen was denoted as \"M5\".  
Two real coals were chosen for comparison including Qiancheng bituminous coal (QC) and Pingzhuang lignite (PZ). The proximate and ultimate analyses of experimental fuel samples are listed in Table 1. No ash nor sulphur was present in synthetic coals due to the absence of corresponding constituents in the preparation materials. The reactivity of synthetic co  
Figure 1: The schematic diagram of the experimental system for synthesizing coal samples.  
Figure 2: The binding energy curves of fuel nitrogen in synthetic coal samples. (**a**) Synthetic coal “M6”; (**b**) Synthetic coal “M5”.  
resemblance to that of lignite during oxy-fuel combustion [36]. Hence, the synthetic coal is a highly effective substitute for coals in term of in-depth research on the transformation of fuel nitrogen and NO\({}_{x}\) reduction under oxy-fuel conditions. Three mineral matters, ferric oxide (Fe\({}_{2}\)O\({}_{3}\)), sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)), and calcium carbonate (CaCO\({}_{3}\)), were used to investigate the catalytic effects of minerals on the formation and reduction of NO\({}_{x}\) from different functional groups of fuel nitrogen. Each additive of 5% by weight was loaded with synthetic coals during the present experiments.
## 3 Proposed Model  
The conventional method of bridge inspection relies on the subjective visual judgment of inspectors. This study aims to enhance the efficiency and objectivity of such inspections by introducing a deep-learning-based approach. High-resolution imagery acquired via UAV-mounted cameras was utilized, and a convolutional neural network (CNN) architecture was employed to perform automatic multi-class damage detection and classification tailored to the structural characteristics of Korean railway bridges. Specifically, different loss functions were applied and evaluated to determine their effectiveness in improving detection performance. A relevant study by [PERSON] [21] proposed an automated monitoring system for infrastructure using a U-Net framework integrated with a novel loss function called DBCE Loss. Infrastructure structures, which are exposed to natural environmental conditions over long periods, are often subject to surface contamination and discoloration. Moreover, UAV-acquired images frequently include complex backgrounds, such as trees, vegetation, and soil, which may hinder accurate detection. This study focuses on evaluating detection models that are robust enough to be deployed in real-world industrial environments, where such challenges are prevalent. [PERSON] [22] conducted an in-depth study on bridge crack detection and proposed a U-Net-based crack identification algorithm named CBAM-U-Net. This model achieved detection standards by measuring the maximum crack width and length with errors of only 1-6% and 1-8%, respectively. Similarly, [PERSON] [23] introduced SA-U-Net, which integrates attention mechanisms with multi-scale feature extraction to effectively improve classification accuracy. These studies exemplify ongoing efforts to enhance defect detection performance through modifications and extensions of the U-Net architecture.