esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Greedy Scanning Data Collection Strategy
### Data Collection
#### 3.3.3 Location Information Local Updating  
To reduce the energy consumption for updating sink current location, our GSDCS only updates some head nodes' \(DN\) when the mobile sink moves into a different grid. In our GSDCS, the \(DN\) is responsible for representing the relative direction of a grid to the mobile sink. Once the mobile sink moves into a new grid cell, it broadcasts a notification packet to the head nodes of corresponding grid cells for updating the \(DN\) value. There are two cases for updating the location information when the mobile sink moves into a neighbor grid cell: (1) the mobile sink moves along the column in one collecting period; (2) the mobile sink moves into another column when goes on to the next collecting period. If the mobile sink moves along the column in one collecting period, then two head nodes need to update their \(DN\)s. One head node is in the grid cell which the mobile sink used to be, the other is in the grid cell which the mobile sink parks in currently. The procedure can be described in Figure 12. The \(DN\)s of two head nodes in the marked grids are updated. That means only two nodes consume energy when the mobile sink moves to a new grid cell along the column direction.  
If the mobile sink moves into another column when goes on to the next collecting period, all the head nodes which are in the two columns need to update their \(DN\)s. One column is which sink used to be in. The other is the one sink parks in currently. This procedure can be described in Figure 13. The head nodes in the marked grid cells need to update their \(DN\)s. That means 16 head nodes consume energy when the mobile sink moves into another column in our example.  
Figure 11: Flow chart of sink moving process.
## 2 Types of SNOM Methods
In this fashion designer tips have been fabricated with a desired aperture size and shape to improve SNOM performance by increasing resolution, maximizing transmission [9,10], or providing access to more exotic features such as magnetic near-fields using a bowtie, split ring, or pyramidal (campanile) shape [10,11]. Although there are a large number of applications that utilize c-SNOM, some significant limitations of this technique remain; (1) low light transmission through the aperture, and (2) a fundamental lower limit of aperture size of approximately 10 nm due to the skin depth of metals [12]. Poor light transmission through the tip aperture can be overcome through the use of high brightness light sources such as lasers, while the lower limit on aperture size can be circumvented through the use of apertureless tips. Apertureless-type SNOM systems, or scattering SNOM (s-SNOM), utilize light from an external source scattered from an apertureless tip which acts as an antenna to produce a source of evanescent light near the nanostructured sample surface as shown in Figure 1c. Apertureless-type operation is advantageous in that it does not suffer from the fundamental size limitations of c-SNOM, and can achieve resolution below 10 nm [1; 8; 13]. However, apertureless-type SNOM systems are more challenging to implement than their aperture-type counterparts. This is due to a relatively intense far-field background composed of light reflected or scattered directly from the sample surface. This unavoidably creates a large background signal over a spatial area much larger than the near-field domain of the tip [14]. The background signal is additionally subject to artifacts if the tip-sample distance is not very carefully controlled, making correct interpretation of apertureless SNOM images more difficult [15].
# Single-Layer Pressure Textile Sensors with Woven Conductive Yarm Circuit
## 1 Introduction  
\(E\)-textile fibers have now been recognized as one of the important materials in the wearable technology field. More and more new \(e\)-textile fibers have been actively developed and applied in wearable devices [1; 2; 3; 4; 5; 6]. Especially, as the convergence of fibers and IT technology has accelerated, many studies on electronic textile [7; 8; 9; 10] and smart fibers [11; 12; 13] have been proposed, and a variety of products have been released [14]. However, \(e\)-textile fibers and wearable technologies still have many problems yet to be solved [15; 16], especially in the pressure textile sensors field [17; 18; 19; 20]. The pressure sensors based on smart fibers are necessary for all aspects of life, such as recognition of human activities, biosignal monitoring and healthcare. Those goals require the sensors to be lightweight, flexible, easy to use, and comfortable for the wearer. So, the development of compact pressure sensors [21; 22; 23; 24] that can be a part of clothing has become a big challenge today. Most of the pressure sensors have a multi-layer structure with a low conductive layer as a spacer inserted between two high conductivelayers in order to measure the levels of pressure [25; 26; 27]. For example, [PERSON] et al. [28] proposed the sensitive capacitive pressure sensor based on conductive fabrics and a microporous-dielectric layer. [PERSON] et al. [29] presented an easy method to create a textile pressure sensor. [PERSON] et al. [30] studied a multilayer fabric pressure sensor for the real-time piezo-impedance imaging of pressure distribution. [PERSON] et al. [31] developed a high-sensitivity and low-hysteresis- flexible pressure sensor based on carbonized cotton fabric. However, the multilayer structure will increase the thickness of the sensor and cause discomfort to the wearer. Therefore, this paper proposes a new structure composed of a one-layer sensor. Nonconductive fibers are converted to conductive fibers through the carbon nanotubes (CNTs) impregnation process. Then, the sensing layer is made by the intertwining of nonconductive fibers, conductive fibers, and electrodes. The results of experiments demonstrate that the one-layer-textile-pressure sensor is really thin (0.4 mm), lightweight, and more fit for wearable devices than the conventional multilayer structure. The characteristics of resistance and hysteresis of the fabricated sensor are analyzed in the paper. We also show an application of this sensor on the glove for monitoring finger motion.
# Towards Automatic Image Enhancement with Genetic Programming and Machine Learning
## 4 Experiments in Real Estate Image Marketing
### Dataset  
To assess the quality of this system and to train the GAN, large quantities of images are required. To tackle this problem, we used the dataset provided by the Spanish company _PhotolLike_. To examine our results unbiased, we separated in an early stage 10% of the 12,090 images from the rest of the dataset. Thus, 1209 images represent the test set. Figure 5 shows the type of images that appear, that range from landscapes, house fronts, kitchens, bathrooms, and garages. The rest served as a training set to our approaches, i.e., the dataset used by the Evolutionary approach to be selected along the evaluation's Evolutionary process. To have a baseline for our measurements, we examined the original images of this test set. Figure 6 represents four histograms, one histogram for each metric. It is noticeable that the _BRISQUE_ tool is much more propitious to giving the maximum possible score than any other metric. In contrast, we see that the _NIMA_ aesthetic model only ranges between medium scores, mostly between the 4 and 6 score range. _PHIL_ evaluation shows that the test subset has a more spread-out range of scores, ranging from scores of 1 to 9.  
Upon conducting the initial experiments with ELAINE [9], we realized that we must track how the image changes after the image enhancement process. Thus, we also resort to two image similarity metrics to track the change between the input and the output: the SSIM and Mean Squared Error (MSE). SSIM extracts 3 key features from an image: Luminance, Contrast, and Structure, with the objective of capturing the degradation of structural information between two images in order to calculate their similarity. MSE is a common metric to quantify the difference in the values of each of the corresponding pixels between the sample and the reference images.  
Figure 5: Sample of images from the test dataset of real estate image marketing.
## 5 Verification of Measurements
### Display of Circle Diagram from Measured Currents for Editing of the Control to Symmetrical Currents  
A circle diagram of stator currents is provided in this subsection to show the distribution of the MMF in a five-phase induction motor during control editing to symmetrical currents. The measurement parameters, 1000 rpm and 3.5 Nm, are the same as in the previous case.  
The measured stator currents are plotted in Matlab in Figure 26a. Figure 26b depicts the \(dq\) currents, while Figure 26c depicts a circle diagram.  
This is a sinusoidal MMF, as shown in the circle diagram in Figure 26c, confirming once more that the control to symmetrical currents editing is correct.  
As seen in these processes, by changing the control, we were able to improve the properties during the failure and move closer to the prefailure state. Section 4 contains the exact results obtained.  
Figure 25: Measurement for editing of the control to nonsymmetrical currents: (**a**) stator currents; (**b**) transformation of measured _abcde_ currents into \(dq\) system; (**c**) circle diagram.  
Figure 27 depicts the measuring stand where the actual measurements were taken. The Agilent N8900 was used as a DC source between the voltage inverter circuit. The DC link voltage was 510 V. Subsequently, the five-phase induction motor was powered by two three-phase inverters with 5A IGBT transistors from Semikron. Current and voltage waveforms were recorded using a four-channel TEKTRONIX TDS 2024B oscilloscope 200 MHz, 2 GS/s. We measured voltages, currents and powers using a Yokogawa wt 1800 precision power analyzer. The torque ripple as well as the direct torque component were measured using a torque sensor with a range of 20 Nm. The engine was loaded with a dynamometer from VUES Brno. The dynamometer motor was a synchronous three-phase motor with a recuperation unit.  
Figure 27: Measuring station from the measurement of a five-phase induction motor.  
Figure 26: Measurement for the editing of the control to symmetrical currents: (**a**) stator currents; (**b**) transformation of measured _abcde_ currents into _dq_ system; (**c**) circle diagram.  
Engine control was performed using the Launchpad TMS320f28069 development board from Texas Instrument. The control of the board was implemented using Matlab/Simulink environment.  
To measure the stator currents required for control, we created a measuring board, where the current measurement was performed using a LEM current sensor LTS 6 NP. We also used the created board to shift the PWM signals from the 3.3 V control board to 15 V for the inverter.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 4 Discussion and Perspectives  
### Discussion  
The observational model of the GNSS phases contains so many factors that when trying to interpret time variations of the estimated coordinates of a station, one is always in doubt as to which factor is the origin of the variation. Thus, interpretation of the adjusted coordinates of the station needs some considerations coming from outside the information of the phase measurement alone. We have already seen, for instance, that the signal in \(X\) on the base C3-C2 has a clear correlation with the temperature (Figure 8), but it is not known whether the temperature produces elongation on the basis of expansion of the deck or via tropospheric correction, which is usually neglected for such short bases. The answer comes from a combination of different considerations: structurally, point C2 is clamped to Deck 2, which is leaning on a pier and, to some extent, is free to move in the longitudinal direction of the bridge; if the effect was due to imperfect modeling of tropospheric effects, a similar signal should be present in the base C3-C1, which is very close to the other base. In addition, the longer basis \(S\)-C2 should be much more affected by tropospheric bias, while the signal in the \"absolute\" position of C2 displays a signal very similar to that of the \"relative\" position.  
Moreover, the thermal expansion coefficient \(\alpha\) estimated in the previous section has a value quite comparable to that of reinforced concrete. All these factors point in the direction of confirming that the phenomenon seen as variations of components of the base is, in fact, thermic elongation. This is important because in a step in the post analysis we can identify it and get information on the structural behavior of the bridge. Further, by removing this component, one is left with a smoother series, i.e., with smaller residuals that give an obvious advantage from the point of view of monitoring interpretation.  
Coming to the hourly solutions for C3-C2, we again find a periodic signal in longitudinal direction along the bridge length that is strongly correlated to the daily temperature excursion. Once more, comparison with C3-C1 excludes a tropospheric origin, and we ascribe it to thermal expansion.  
We can observe that in the hourly solution, the \(Z\) component of both C3-C1 and C3-C2 seems to contain a periodic signal, too, although it is affected by more noise. The two signals in this case have a similar pattern, which is not surprising considering that structurally, so to speak, one station is leaning on the other. We presume, therefore, that this last signal is a common shift due to thermal expansion of the pier on which both C1 and C2 are leaning.
# Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols
###### Abstract  
In this paper, we obtained the optimized network allocation vector (NAV) for underwater handshaking-based protocols, as inefficient determination of the NAV leads to unnecessarily long silent periods. We propose a scheme which determines the NAV by taking into account all possible propagation delays: propagation delay between a source and a destination; propagation delay between a source and the neighbors; and propagation delay between a destination and the neighbors. Such an approach effectively allows the NAV to be determined precisely equal to duration of a busy channel, and the silent period can be set commensurate to that duration. This allows for improvements in the performance of handshaking-based protocols, such as the carrier sense multiple access/collision avoidance (CSMA/CA) protocol, in terms of throughput and fairness. To evaluate the performance of the proposed scheme, performance comparisons were carried out through simulations with prior NAV setting methods. The simulation results show that the proposed scheme outperforms the other schemes in terms of throughput and fairness.  
CSMA/CA; hidden node; MAC; NAV; throughput; underwater acoustic sensor networks Article
## 4 Conclusions  
In this study, we have evaluated the in situ electrochlorination of seawater, which is used as a coolant for coastal power plants in terms of TRO production and formation of inorganic chlorination byproducts such as \(\text{ClO}_{3}{}^{-}\) and \(\text{ClO}_{4}{}^{-}\). Compared to the practice of directly adding \(\text{OCI}^{-}\) to cooling seawater, the in situ electrochlorination method based on Ti/RuO\({}_{2}\) electrodes produced much less \(\text{ClO}_{3}{}^{-}\), while a similar level of TRO could be achieved with a high TRO production rate of 16.8-18 mg \(\text{Cl}_{2}/\text{cm}^{2}/\text{min}\). Moreover, \(\text{ClO}_{4}{}^{-}\) was not observed during electrochlorination, while the direct \(\text{OCI}^{-}\)-injection system could still result in the production of \(\text{ClO}_{4}{}^{-}\). The lower or no production of \(\text{ClO}_{3}{}^{-}\) or \(\text{ClO}_{4}{}^{-}\) by the electrochlorination of seawater was mainly attributed to two reasons. First, less \(\text{OCI}^{-}\) is available for \(\text{ClO}_{3}{}^{-}\) formation because the \(\text{OCI}^{-}\) supply is limited by the electrode surface area and reaction time. Secondly, the formation of \(\text{ClO}_{3}{}^{-}\) or \(\text{ClO}_{4}{}^{-}\) is affected by the electrode material. In other words, if the electrode material is carefully chosen, the production of harmful reaction byproducts can be prevented or minimized.  
Based on the results of our study, in situ electrochlorination technology proves to be a marine environmentally friendly and sustainable method for controlling biofouling in the pipes of a cooling system in a coastal power plant. Therefore, this technology should be preferentially considered as an option for antifouling of the cooling system for a thermoelectric power plant.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/2/229/s1](http://www.mdpi.com/2076-3417/9/2/229/s1)]([http://www.mdpi.com/2076-3417/9/2/229/s1](http://www.mdpi.com/2076-3417/9/2/229/s1)).  
H.K. and B.-I.S. conceived and designed the experiments; G.-H.K., Y.A, B.J. and M.H. performed the experiments; J.Y. and Y.A. analyzed the data; J.Y., Y.A., and N.S. prepared the paper; H.K. edited the paper.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20171520101740).  
The authors declare no conflict of interest.
# Fast, Lightweight, and Efficient Cybersecurity Optimization for Tactical-Operational Management
## 3 Experiments Design and Result
### Definition of Initial Status  
In order to ensure uniformity in all experiments, we deemed it appropriate to utilize a set of randomly chosen chromosomes that would serve as the initial cybersecurity status for hypothetical assets, where their criticality level necessitates the application of IG1, IG2, or IG3. To prevent any potential bias in the operation of FLECO stemming from the use of specific initial statuses, we generated 15 unique, randomized initial statuses for each implementation group, allowing for testing under diverse circumstances.
# Determining the Optimal Positions of Infill Balise Groups for ERTMS/ETCS Level 1 Applications
## 2 Materials and Methods
### Optimization for Multiple Trains  
When dealing with multiple trains with differing speeds or driving dynamics, the positions of the infill balise groups need to be determined in a way that overall best results are achieved. The farthest infill balise group always needs to be placed based on the largest IP of all trains expected so that it is technically possible to allow an unhindered journey if the following section is free. The next infill balise group's positioning depends on the number of train types to be considered and the total number of infill balise groups possible or desired. If two train types are present, the second infill balise group can be placed at the IP or Permitted Point of the second train type. If more than two train types need to be considered, the second infill balise group can either be placed as presented above or at the mean IP of all trains. A weighting factor based on the traffic mix can be applied. Any additional infill balise groups should then be placed in such a manner that the operational impact measured by the KPI \"weighted additional runtime\" is minimized.  
These considerations are not yet part of the algorithm presented in this paper and need to be developed in detail, analyzed, and later implemented. The version of the algorithm presented here then can be used to evaluate the weighted additional runtime for each train type individually.  
Figure 5: Example of the three-dimensional plot showing the additional runtime over all possible positions of the infill balise groups. The color gradient indicates low additional runtimes in green and high additional runtimes in red.
## 5 Variability Analysis
### Comparison of Predicted and Measured Coefficient of Earth Pressure at Rest  
As mentioned earlier, there is no direct formula for predicting the coefficient of earth pressure at rest using earth pressure; however, it can be indirectly predicted using the internal friction angle (\(\phi\)). Therefore, the internal friction angle was calculated using a direct shear test [29] on the sample soil. The test confirmed that it was 47\({}^{\circ}\).  
Table 2 shows the empirical relationship between the coefficient of earth pressure at rest and the internal friction angle of soil proposed by other researchers [30; 31; 32; 33; 34; 35; 36; 37; 38; 39], as well as the results of substituting the internal friction angle of the sample used in this study. The calculation result of the prediction formula using the internal friction angle ranged from 0.219 to 0.406, and the average was 0.273.  
All theoretical values were lower than the measured values in Table 1 except for Matsuoka and Sakakibara [37] in loading condition. This can be attributed to two factors: the lateral earth pressure is overestimated, or the vertical earth pressure obtained by the theoretical formula is small. The former can be caused by many variability factors (for instance, measurement error and relocation of soil particles due to gravitational acceleration) during measurement. The latter is attributable to the vertical earth pressure exceeding the theoretical value due to differences in the ground conditions (a structure with an empty interior such as a box culvert).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Conditions** & **Loading** & **Unloading** \\ \hline The number of data & 60 & 60 \\ Average, \(\overline{x}\) & 0.405 & 0.516 \\ Standard deviation, \(s_{x}\) & 0.160 & 0.232 \\ COV (\%) & 39,586 & 44.983 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variability analysis for the coefficient of earth pressure at rest under the loading and unloading conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **References** & **Equations** & **Results** \\ \hline
1 & [PERSON] [30] & \(K_{0}=\left[1+\frac{2}{3}sin\phi\right]\left[\frac{1-sin\phi}{1+sin\phi}\right]\) & 0.231 \\ \hline
2 & [PERSON] [31] & \(K_{0}=1-sin\phi\) & 0.269 \\ \hline
3 & [PERSON] [32] & \(K_{0}=tan^{2}\left[45-\frac{\phi-9}{2}\right]\) & 0.238 \\ \hline
4 & [PERSON] [33] & \(K_{0}=\frac{1}{2}\left[\frac{1+\frac{5}{3}-3\frac{5}{3}(sin\phi)}{1-\frac{5}{ 3}+3\frac{5}{3}(sin\phi)}\right]\) & 0.250 \\ \hline
5 & [PERSON] and Ireland [34] & \(K_{0}=0.95-sin\phi\) & 0.219 \\ \hline
6 & [PERSON] [35] & \(K_{0}=0.97[1-0.97(sin\phi)]\) & 0.282 \\ \hline
7 &
\begin{tabular}{c} [PERSON] \\ (mentioned by [PERSON] [36]) \\ \end{tabular} & \(K_{0}=tan^{2}\left[45-\frac{\phi}{3}\right]\) & 0.316 \\ \hline
8 & Matsuoka and Sakakibara [37] & \(K_{0}=\frac{1}{1+2 sin\phi}\) & 0.406 \\ \hline
9 & [PERSON] [38] & \(K_{0}=\frac{1-sin(\phi-11.5)}{1+sin(\phi+11.5)}\) & 0.265 \\ \hline
10 & [PERSON] [39] & \(K_{0}=0.95(1-sin\phi)\) & 0.255 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Equations and results of coefficient of earth pressure at rest by references.
## 4 Experiment Results and Analysis  
We evaluate the performance of the proposed method on two datasets from Pavia University and Salinas. The Pavia University dataset contains 103 bands, which covers the wavelength from 430 nm to 860 nm. It has 610 \(\times\) 340 pixels and nine classes to be classified. The Salinas dataset contains 204 bands, which covers the wavelength from 400 to 2500 nm with 512 \(\times\) 217 pixels and has 16 classes. Four commonly used performance metrics are utilized to evaluate the model: overall accuracy (OA), average accuracy (AA), kappa coefficient, and testing time. In the experiment, we randomly selected 200 samples per class as training sets (as shown in Tables 1 and 2), and the rest of the samples for testing sets. All the experiments were conducted using Python 3.6 on a computer with an 11G GPU.