esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modeling of PMSG-WECS  
This section covers the mathematical modeling of the standalone, fixed-pitch, variable speed PMSG-WECS. A schematic of the overall variable speed PMSG-WECS is demonstrated in Figure 1. The principal components of a typical variable speed PMSG-WECS include: a variable speed wind turbine (VSWT), a gearbox, power electronic converters and a PMSG coupled with a VSWT.
## 2 Materials and Methods
### Data Collection Protocol  
A 500 ppm solution of Pyranine, a biodegradable fluorescent tracer (10G(r), Keystone Inc., Chicago, IL, USA) was prepared with tap water. The spray mix was agitated thoroughly to create a homogeneous solution. Tank samples were collected pre- and post-spraying to monitor any change in tracer concentration during spraying and subsequent normalization of field samples [20].  
The mylar cards and WSP samplers were installed in respective sampling zones prior to spray application as discussed in the Section 2.2.4. Since the pneumatic spray delivery based SSCDS used in this study was designed for 234 L ha\({}^{-1}\), the system was operated three times to obtain an application rate of 702 L ha\({}^{-1}\). The operating pressure during spraying stage was set at 310 kPa [21; 22]. A pair of thin film pressure transducer (model: 1502B81 EZ100 psiG, PCB Piezotronics Inc., Depew, NY, USA) coupled with a data logger (model: CR1000, Campbell Scientific, Logan, UT, USA) was installed at 1.5 and 22 m away from the inlet port to log pressure data at 1 Hz. Additionally, an all-in-one weather station (model: ATMOS 41, METER Group, Inc., Pullman, WA, USA) coupled with a data logger (model: CR1000, Campbell Scientific, Logan, UT, USA) was installed at a height of 1 m above the canopy (ISO 22522, 2007) (Table 2) to monitor the in-field weather parameters. The weather parameters were logged at 0.2 Hz.  
After spraying, the mylar card and WSPs samplers were allowed to dry for 15 min and collected and stored according to protocol described in [PERSON] et al. [20; 22].  
Figure 5: The off-target drift sampler layout (schematic not drawn on the scale) with ground samplers consists of a mylar card and water sensitive paper (WSP) kept on a wooden block beneath the tree and in the mid-row alley at a distance of 1.5 m, 4.5 m and 7.5 m away from the treated canopy. Three replicates of aerial drift samplers fixed on PVC mast at 3 m and 6 m downwind with mylar cards and WSPs fixed at a height of 3.3 m, 3.6 m and 3.9 m above the ground level (* distance measured above ground level).
## 4 Conclusions  
We are presenting a two-layer approach towards optical sensing of K\({}^{+}\) using photonic crystal sensor technology. It is capable of detecting K\({}^{+}\) in the 0.1 to 100 mM concentration range over which a visually and instrumentally detectable wavelength shift of 90 nm is induced. This range also covers the one found in human blood. The sensor films can be fairly easily prepared from affordable materials. Aside from bare-eye (visual) detection, the films can be read out by reflectometry in the visible part of the spectrum. In addition, they can be imaged using a digital camera. An increase in [K\({}^{+}\)] from 10 \(\upmu\)M to 10 mM causes the intensity of the red channel of the reflected light to decrease by 80%. The sensor is highly selective in that other alkali and earth alkaline ions remain inert. Other features include fast response, lack of photobleaching, full reversibility on exposure to plain water (_i.e._, without adding another reagent) and inertness to changes in stability, as shown in previous work [26].  
Given these features, we presume that this sensor is a viable tool for both single-shot and continuous sensing of [K\({}^{+}\)] at physiological concentrations. Conceivably, the sensor is produced in the form of a test stripe for on-site (near-patient) visual readout. This proof of principle of sensing ions using a PhC-based two-layer technology is exploiting the fact that the photonic crystals placed in a hydrogel layer can be made sensitive to ionic strength. It is combined with ion-selective membrane technology in order to make the sensor specific for an ion. Given the fact that a large variety of ionophores is known, we predict that this sensing scheme can be extended to a number of other ions and even to sensor arrays for multiple ion sensing.
## 5 Experimental Results
### Experimental Results for CBA Algorithm
#### 5.1.3 Percentage of Zero-Value Jobs  
This metric is important as it relates to user satisfaction with the system: more jobs successfully serviced implies more users serviced and therefore more satisfied users. Figure 6 shows the percentage of zero-value jobs for both approaches at each of the different arrival rates. The average over each of the different arrival rates is also shown. Note that the rejections of jobs were implicit in the maximum reward check, they were not actually removed from the queue though it would be possible to add that functionality. It can be observed that, for all arrival rates, CBA had a lower percentage of zero-value jobs. This was less significant at the high and low arrival rates. For the high arrival rate, this was due to such a large number of jobs arriving that it was impossible to service a large number of them regardless of approach. For the low arrival rate, the difference was again likely due to the exploration part of the algorithm. The initial exploration caused a number of early jobs to be executed after the deadline while the algorithm learned the initial average reward for each possible allocation. The effect of exploration based on uncertainty after initial exploration was unlikely to have much effect on the number of jobs which give zero-value as this exploration favored jobs with reasonably high expected value already. On average, CBA provided 5% more user satisfaction than that of HBA.  
Figure 5: Value earned and energy consumed at different arrival rates.  
Figure 6: Percentage of zero-value jobs at various arrival rates.
## 4 Results and Discussion
### Spatial Turbidity Distribution in the Lagoon-Sea Transset  
Figure 4 shows the synoptical distribution of satellite-derived turbidity in the eight stations of the investigated transect (Figure 1, pink dots). Data were subdivided by flood and ebb-tide phases, to emphasize differences in relation to the tidal phase. Excluding the offshore station, which has generally low turbidity (st8), the lagoon-sea transect is characterized by a relatively uniform distribution in the ebb-tide phase, whereas extreme values are more common in the flood. Indeed, flood tide turbidity shows a few outliers for stations located in the inlet channel (st3, st5 and st6) with values up to more than four times the average turbidity. This indicates a larger effect of the flood currents on suspended sediment pattern in the transect, due to the transport of suspended material from the coasts at the north of the inlet by the interaction of longshore drift and flood tidal currents.  
Figure 4: Box-plots of the satellite-derived turbidity in **(a)** flood and **(b)** ebb-tide phases for the 8 sampling stations in Figure 1. Stations are ordered following the lagoon-sea transect for SN channel, excluding Station 7, which is located in the TRP channel. The \(\times\) mark indicates the mean value, the box represents the interquartile range, the line in the box is the median and the whiskers represent the tails of the distribution with external solid points representing the outliers.  
Figure 3: Scatter plot of the L8- and S2-derived turbidity vs. in situ turbidity measurements for 295 match-ups, sampled at the time of the satellite overpasses in 2017–2020. Turbidity values were retrieved according to [47]. Dashed green and red lines are the best-fit regression for L8 and S2, respectively. Grey solid line is the 1:1 line.
# Groundwater Detection Using the Pseudo-3D Resistivity Method: A History of Case Studies
## 3 Case Studies
### Dam Leakage Detection
The depth of the low resistivity anomaly is around 40 m.  
In Figure 5c, the shallow surface of the 0-270 measurement locations shows a low resistivity zone. At the 40 th measurement location, an obvious low-resistivity anomaly reaction is presented. The obvious seepage near the 40 th measurement point of profile 2 (Figure 5b) has a long-term influence on the surrounding soil of profile 3 (Figure 5c). An obvious low-resistivity anomaly near the 260 th measurement location in profile 3 is inferred as the leakage of the ground water pipe. Based on the stereoscopic and sectional view of the artificial field pseudo-3D resistivity method experimental results in the Xiaojiashan Reservoir, the dam leakage is inferred as the 1 st-1-120 th measurement locations \(\rightarrow\), the shallow part of the \(-\)00-\(-\)40 th measurement section of profile 2 \(\rightarrow\), the vicinity of the \(-\)40-20 th measurement location of profile 2 \(\rightarrow\), the 2 nd-20-160 th measurement locations \(\rightarrow\), and the 0-240 th measurement location of profile 3 (Figure 5c). Based on the resistivity image in Figure 5, the continuous low resistivity indicates that the leakage is parallel to the dam's direction. The seepage is inferred to flow from the east of the dam, where the reservoir water seeps through the weak layer in the dam. However, profile 1 is 30 m away from the reservoir water, so the low resistivity anomaly may also be from the east side of the dam. It is observed that the surface of the reservoir water is lower than the leakage and that the anomaly of the 120-\(-\)40 th measurement locations in profile 1 follow the vertical direction of the dam, which can be inferred as the channel of the reservoir water. Based on these observations, the leakage water is suspected to come from reservoir water and underground water near the mountain on the east side of the dam.
## 3 Results and Discussion
### Results of Oxidative Stability  
The oxidative stability of oil and microencapsulated powder products is expressed in peroxide values (Table 4). The evaluation of peroxide values (PVs), expressed in meq O\({}_{2}\)/kg oil, is crucial, as it is a primary indicator of lipid oxidation and therefore a measure of oil quality. This study presents the peroxide values of two different oils (NY and KY) under varying ratios of microencapsulation (1:1, 1:4, 1:10) over time intervals of 0, 45, and 90 days. According to the results, an increase in the peroxide values was observed in both oils and microencapsulated samples during storage. Encapsulated samples showed better oxidative stability than oil. In encapsulated oils, the best oxidative stability was observed in the NY (1:10) and KY (1:10) formulation ratios. The initial peroxide values for both NY and KY microcapsules show relatively low values, ranging from 5.94 to 6.55 meq O\({}_{2}\)/kg oil, regardless of the formulation ratios. This indicates that on day 0, the oils are in a steady state; with minimal pre-existing oxidation on day 0, the peroxide values in all samples measured were not statistically significant. By day 45, the peroxide values increase for all formulations, indicating the onset of oxidation. The most pronounced increases are observed by day 90, with values ranging from 8.39 to 16.06 meq O\({}_{2}\)/kg. The best protection  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Microencapsules** & **Microencapsulation Efficiency (ME, \%) 1 ** \\ \hline NY (1:1) & 87.09 \(\pm\) 1.17 2  \\ NY (1:4) & 89.46 \(\pm\) 1.62 3  \\ NY (1:10) & 90.56 \(\pm\) 0.16 4  \\ KY (1:1) & 87.30 \(\pm\) 1.99 5  \\ KY (1:4) & 89.33 \(\pm\) 1.29 5  \\ KY (1:10) & 90.59 \(\pm\) 0.20 6  \\ \hline \hline \end{tabular}
\end{table}
Table 3: Microencapsulation efficiency of microcapsules (%).  
was observed at 1:10 formulation ratios, with the lowest value of 8.39 meq O\({}_{2}\)/kg oil peroxide in NY (1:10), indicating that 1:10 is the best formulation ratio to maintain oxidative stability. The results obtained are consistent with the microencapsulation efficiency. The formulation ratio with a high microencapsulation efficiency has a low peroxide value. Similarly, [PERSON] et al. (2017) reported that the peroxide values decreased as the microencapsulation efficiency of coconut oil increased.
## 2 Materials and Methods
### DSConv  
The idea of dual-scale convolution has been previously applied in hyperspectral image classification [17], where fixed kernel sizes operate on 1D spectral vectors. In contrast, our method is designed for 2D object detection tasks and introduces dynamically adjustable receptive fields (ranging from 2 to 15 pixels) along with softmax-based fusion. This allows DSConv to adapt to various lesion shapes while maintaining low latency suitable for edge deployment.  
Unlike traditional multi-scale convolutions with fixed kernel sizes, our DSConv introduces dynamic kernel adaptation where convolutional scales (\(s_{1},s_{2}\)) automatically adjust to lesion size during inference (range: 2-15 px). This addresses the fundamental limitation of fixed-receptive fields in detecting variably sized plant symptoms. The module employs learnable feature fusion via softmax-normalized weights (\(\alpha_{1}\), \(\alpha_{2}\)) that optimize multi-scale contributions per input, overcoming the heuristic fusion in prior works.  
To effectively merge these multi-scale features, a weighted sum approach is employed. The outputs from each convolutional layer are multiplied by a learnable weight parameter before their summation. The weights, \(w_{1}\) and \(w_{2}\), undergo softmax normalization to ensure that they remain positive and collectively sum to one:  
\[\alpha_{1}=\frac{e^{w_{1}}}{e^{w_{1}}+e^{w_{2}}},\quad\alpha_{2}=\frac{e^{w_{ 2}}}{e^{w_{1}}+e^{w_{2}}} \tag{3}\]  
Moreover, to accommodate tensors from different convolutional outputs for optimal feature size, the module integrates a dynamic scaling factor for bilinear interpolation. This factor, a trainable parameter, adjusts the resizing of the feature maps to optimal scales learned through the model's training. Bilinear interpolation is utilized to align feature maps from various scales accurately, enhancing the effective integration of multi-scale information.
# Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement
## 6 Results and Discussions
### Factor Importance  
The importance of the variables is evaluated by a mean decrease in the accuracy index and a mean decrease in the Gini index for the explanation for the RF model, as shown in Figure 11. The more decrease the indexes have, the more important the factor is. The RI20, i.e., the increase in rutting over 20 days, is the most important factor, that is, if a point is rutting deeply in a short time, it is most likely to obtain moisture damage under the surface or surface failure soon. Following the RI20, i.e., the increase in deflection in the third test stage, the rutting at the 20 th day and 5 th day, and the position are outstanding in all variables. In these five factors, the most interesting thing is that three factors are linked to rutting, which directly presents the state of a road; a highly important factor is linked to deflection, which presents the strength of the road; and the point position is effective for predicting the project (i.e., the different construction method), which companies or materials adapted to in this road, leading this phenomenon. Based on the main important factors, it can be asserted that, before moisture damage occurs, there must firstly be a significant increase in rutting and deflection detected. Some original properties of the road, such as original rutting, original deflection, and surface splitting strength, have little weight in the model. This means that the moisture damage matters for a cumulative effect rather than the initial properties.  
Figure 10: The MDS of the RF model.  
These specified values of variables and their relationships are analyzed by density curve plots. For these important variables, the overlap section of the Y and N area is smaller, which has a higher classification strength (Figure 12). Thus, it will hold a bigger weight in the model.  
To compare the traditional analysis of properties, the three most important categories of factors from the RF model are selected for making graphics of their relationships. The void rate, the rutting increase over 20 days, and the increase in deflection in the third test stage are fitted in Figure 13.  
Figure 11: Factor importance.  
Another interesting phenomenon is found. The void rate between 4% and 6% has the lowest probability to drop in rutting and deflection [43; 44]. This finding is very similar to the Superpave construction principles. Therefore, to control the moisture damage at an early age of a road, the most important thing is to minimize construction segregation. The result proves the interpretability of the RF model, which can be easily connected to the practice work. Furthermore, the prediction of the model is rational and logical.  
Figure 12: Specified values of variables and their relationships.
# GIS-Based Optimal Route Selection of Submarine Cables Considering Potential Seismic Fault Zones
## 2 Materials and Methods
### LCPA-Based Routing Analysis  
Having defined the basic design considerations (i.e., basic criteria) and their quantitative prioritization (i.e., weight values), a preliminary multi-criteria routing analysis is conducted, implementing the LCPA technique. The latter constitutes a powerful and computationally efficient routing analysis methodology that has been broadly utilized for finding the shortest (and most cost-effective) path over a cost surface (i.e., raster surface) from a starting point to an ending point. GIS-driven LCPA is based on a variant of the [PERSON]'s algorithm [52]. The latter has been widely used to process the optimal route selection of cables [2; 5]. In contrast to other similar techniques, LCPA guarantees the solution of the shortest path problem, as all possible nodes within the entire cost surface are examined, all of the suitable \"corridors\" (i.e., paths) are identified and consequently, the most cost-effective one between the two geographic points is determined [44; 53; 54].  
Initially, all of the origin's neighboring cells are checked along the horizontal, vertical and diagonal directions, and the one that contains the lowest cost value is identified. Subsequently, the cell with the lowest value is converted into the starting point and the next cell with the lowest value is identified. Through an iterative procedure, the user-defined origin and destination points are eventually connected. Then, the already-found cells with the lowest cost value are utilized in a back-propagation process from the destination to the origin point and the desired least-cost path is finally derived [55; 56]. In the ArcMap commercial GIS software package, LCPA is carried out through the serial application of GIS spatial analyst tools [26; 43].  
Lastly, it is important to clarify that the routing analysis can be undertaken multiple times depending on the number of different scenarios that have to be examined (i.e., all time different weighted factors are assigned to the considered criteria) according to the user's preferences, and consequently, several alternative routings are derived.
# A Microbial Screening in Silico Method for the Fitness Step Evaluation in Evolutionary Algorithms
## 2 Materials and Methods
### Scalability and Run Time Complexity  
One of the possible drawbacks of using a colony of synthetic bacteria to build the evaluation function of an optimization problem is scalability. Thus, the colony grows exponentially and in some class of optimization problems, this property of the bacterial agents can lead to a high computational cost. In the case that a high number of bacterial agents are needed to solve a NP hard problem, the result will be an exponential consumption of resources, e.g., memory or execution time. In addition, the version [16] of Gro runs the simulations faster than the original version beta.4 [14]. For instance, in preliminary studies, we found that in non-programmed bacteria, i.e., without holding a script to be executed, the time it takes for the colony to reach a size of 10,000 bacterial agents differs considerably (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Profit** & **40** & **35** & **18** & **4** & **10** & **2** \\ \hline Weight & 100 & 50 & 45 & 20 & 10 & 5 \\ \hline \end{tabular}
\end{table}
Table 1: 0/1 knapsack instance.  
In order to prevent the above scenario in each of the screening in silico experiments, we set a time limit or maximum simulation time, \(t_{\rm s}\), which was set at a value equal to 150. This procedure restricts the maximum time of a simulation experiment. In consequence, we avoided the excessive consumption of computer resources and an excessively long execution time. In previous experiments, it was observed that the length of 150 min (simulated time) is enough to build a satisfactory fitness function in several repetitions of the screening experiment (Figures 7 and 8). In each version of Gro [14,16], we evaluated the total run time of two instances of a SAT problem with different complexity. In particular, we evaluated a 2-SAT proposition:  
\[\begin{array}{ll}\phi=&(OR\;(X[0])\;(X[2]))+(OR\;(X[0])\;(not(X[3])))+\\ &(OR\;(X[1])\;(not(X[3])))+(OR\;(X[1])\;(not(X[4])))\end{array}\]  
and 3-SAT proposition:  
\[\begin{array}{ll}\phi\;=\;(OR\;(X[0])\;(not(X[4]))\;(not(X[2])))+(OR\;(X[2]) \;(not(X[2]))\;(X[3]))+\\ (OR\;(X[3])\;(not(X[4]))\;(X[4]))+(OR\;(not(X[0]))\;(X[4])\;(X[3])))+\\ (OR\;(X[2])\;(X[3]))+(OR\;(not(X[0]))\;(X[4])\;(X[3]))+\\ (OR\;(not(X[3]))\;(not(X[0]))\;(not(X[4])))+(OR\;(X[1])\;(not(X[5]))\;(X[2]))+\\ (OR\;(X[5])\;(X[4])\;(not(X[2])))+(OR\;(X[0])\;(not(X[4]))\;(X[1]))+\\ (OR\;(X[2])\;(X[1])\;(not(X[4])))+(OR\;(not(X[2]))\;(X[0])\;(not(X[5])))+\\ (OR\;(not(X[0]))\;(not(X[5]))\;(X[3]))+(OR\;(not(X[4]))\;(not(X[5]))))\end{array}\]  
The experiments were conducted on a computer with an Intel Core i7-3770 CPU at 3.40 GHz with 16 GB of RAM.  
Figure 9: Screening for the search of the optimal combination of a mutation rate and number of generations values. Experiments conducted for the SGA (**A**) with antibiotic screening procedure and (**C**) screening based on the Lac operon. Experiments performed for the SMGA (**B**) with antibiotic screening procedure and (**D**) with the Lac operon screening method.