esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of DPPH Radical and Hydrogen Peroxide Scavenging Activities in TVEE  
2,2-diphenyl-1-picrylydrazyl (DPPH) radical and hydrogen peroxide scavenging activities can be used to rapidly assess the radical scavenging activity of functional ingredient in an extract through electron transfer reactions [25]. Free radical scavenging activity is related to cell aging [26]. DPPH radical and hydrogen peroxide scavenging activities increased in a concentration-dependent manner. The DPPH radical scavenging activity of TVEE was 7.4, 13.6, 21.2, 41.6, 62.2, 84.3 and 89.4% at 0.05, 0.1, 0.2, 0.5, 1, 2, and 4 mg/mL, respectively, and the hydrogen peroxide scavenging activity was 10.8, 15.1, 24.7, 44.9, 66.4, 91.3 and 97.4% at 0.05, 0.1, 0.2, 0.5, 1, 2, and 4 mg/mL, respectively (Figure 1). These results indicate that _T. cornutus_ viscera have high radical scavenging activity. Previous studies using hydrolyzed viscera and muscle of _T. cornutus_ showed that the viscera extract had higher radical scavenging activity than the muscle extract at 0.25-2 mg/mL [7]. Moreover, a previous study comparing DPPH radical scavenging activity of abalone muscles and viscera extracts confirmed the higher effects in the viscera than in the muscle [27].
## Materials and Methods
### Experimental Design and Setup
Treatments consisted in the injection of 50 g/m\({}^{3}\) of the BAP accompanied by **(1)** an addition of sodium molybdate (Na\({}_{2}\)MoO\({}_{4}\)) at a final concentration of 5 mM, which is a sulfate analogue extensively used to inhibit the growth of sulfate-reducing bacteria [18, 23, 24]; **(2)** an addition of 2-bromoethane-sulfate (BES) at a final concentration of 3.5 mM, which is a specific inhibitor of methanogenesis [18, 25]; and **(3)** an addition of 5 g/m\({}^{3}\) of sodium acetate (CH\({}_{3}\)COONa) as enhancer for acetoclastic microorganisms. Experiment 6 (a supplementary addition of inorganic nutrients): This experiment was designed to assess a potential inorganic nutrient limitation for the performance of the BAP, which would require a redesign of the remediation protocol. Accordingly, five different treatments were established consisting in the injection of the BAP at the standard dose of 50 g/m\({}^{3}\) plus **(1)** 50 g/m\({}^{3}\) of ammonium chloride (NH\({}_{4}\)Cl); **(2)** 12.5 g/m\({}^{3}\) of monosodium phosphate (NaH\({}_{2}\)PO\({}_{4}\)) and 12.5 g/m\({}^{3}\) of disodium phosphate (Na\({}_{2}\)HPO\({}_{4}\)); **(3)** a solution of trace elements added at a dose of 1 liter of solution per m\({}^{3}\) of sediment, this was prepared with the following compounds (in mg/L): H\({}_{3}\)BO\({}_{3}\) (50), FeCl\({}_{2}\)-4H\({}_{2}\)O (2000), ZnCl\({}_{2}\) (50), MnCl\({}_{2}\)-4H\({}_{2}\)O (50), (NH\({}_{4}\))\({}_{6}\)Mo\({}_{7}\)O\({}_{24}\)-4H\({}_{2}\)O (50), AlCl\({}_{3}\)-6H\({}_{2}\)O (90) CoCl\({}_{2}\)-6H\({}_{2}\)O (2000), NiCl\({}_{2}\)-6H\({}_{2}\)O (50), CuCl\({}_{2}\)-2H\({}_{2}\)O (30), NaSeO\({}_{3}\)-5H\({}_{2}\)O (100), EDTA (1000), and 36% HCl (1 mL/L); **(4)** a combination of all the former nutrient (1+2+3) supplies without BAP and; **(5)** the same combination as in treatment 4 but including the standard BAP dose. As described in Table 1, the effect of treatments in the experiments 2 to 6 was compared to both the controls without any amendment, as well as with the treatment containing only the injection of the BPA at the standard dose of 50 g/m\({}^{3}\), as performed in experiment 1.
## 3 Materials and Methods
### Analyzing the Dataset
#### 3.5.1 Pearson Correlation Coefficient  
This is a measure of the linear correlation between two variables. It determines the strength and direction of the relationship between two variables. By dividing the covariance of two variables by the sum of their standard deviations, the Pearson correlation coefficient is determined. The scale ranges from \(-1\) to \(1\), with \(-1\) denoting a strong negative correlation, \(0\) denoting no association, and \(1\) denoting a high positive correlation [32]. The Pearson correlation coefficient is commonly used in datasets because it is easy to understand and calculate, and it can be used to identify relationships between continuous variables. It is implemented in the dataset as it provides its advantage in feature selection and dimensionality reduction. The equation for the Pearson correlation coefficient is as follows:  
\[r_{XY}=\frac{\sum_{i=1}^{n}\left(X_{i}-\bar{X}\right)\left(Y_{i}-\bar{Y} \right)}{\sqrt{\sum_{i=1}^{n}(X_{i}-\bar{X})^{2}}\sqrt{\sum_{i=1}^{n}(Y_{i}- \bar{Y})^{2}}} \tag{1}\]  
where \(x\) signifies the sample mean of variable \(x\), and \(y\) signifies the sample mean of variable \(y\). To find the correlation between the features in a dataset, a script in Python to examine the correlation coefficient was created, and the attributes were ranked in a range of \(-1\) to \(1\). The average correlation for each feature was also computed to identify the features that would introduce less uncertainty in the dataset. When there is a strong relation between two features, one feature from the pair can be eliminated. Hence, if there is a strong relation, the value of the pair comes close to \(1\), which signifies that \(X\) is highly correlated to \(Y\), while a value close to \(-1\) suggests a negative correlation. If there is no correlation, then the correlation coefficient provides the result of \(0\)[32]. In the dataset, the threshold was set to  
Figure 6: Data statistics of generated dataset.  
0.90, as it helps obtain the highly correlated feature from the set and helps in the feature reduction. One of the pairs with a threshold greater than or equal to 0.90 was eliminated, and also the correlation relation between each of the different variables opposed to the target was also considered in the research. Figure 7 shows the Pearson correlation heatmap for the dataset where every feature pair with a higher or equal to 0.90 correlation is dropped for the machine learning model training.
## 4 Improvement to CAD
### Incorporation into CAD  
There are three basic methods for including pre-screening into CAD analysis. The first is the strict method, where the pre-screening removes the non-cancerous cases entirely from the consideration of the CAD software. The second is the probabilistic method, where the probability of the case being cancerous or non-cancerous is determined by the pre-screening system and then incorporated into the CAD analysis. The third is an optimal method, which uses learning to try to determine the optimal factors for the inclusion of the pre-screening results into the CAD analysis. These methods will be defined and compared below.  
The strict method is the simplest to define. Images that are screened as normal are removed from consideration by the CAD analysis. Since there are no false positives drawn from these cases, the number of false positives per image decreases. This is the most effective technique at reducing the number of false positives, but it is also the most dangerous as mistakes by the pre-screening system cannot be rectified by the CAD system.  
The probabilistic method relies on the statistics of the pre-screening method to adjust the output of the CAD system. To incorporate pre-screening into a CAD system, we made use of Bayes Theorem, P(CancerSite \(|\) Pre-screen) \(=\) {P(Pre-screen \(|\) CancerSite) P(CancerSite) / P(Pre-screen)}. The sites where pre-screening indicates cancer are thus given an increased probability of being cancerous, while sites where pre-screening does not indicate cancer are given a reduced probability of being cancerous. Since the pre-screening measurement is applied to an entire case, all of the sites in those cases are affected similarly.  
The optimal approach is a variant of the probabilistic approach, but instead of deriving the change from the underlying probabilities, the change is learned on a training set of cases. In theory, thisapproach can optimize the incorporation of pre-screening into CAD, but can be difficult in practice. In this case, P(CancerSite \(|\) Pre-screen) = A(Pre-screen) P(CancerSite), where A(Pre-screen) is the learned adjustment factor. This approach has more flexibility than the probabilistic approach, but is much harder to implement. The choice of what to optimize is also a concern. There are two main options, optimizing the area under the ROC curve or optimizing the accuracy of the CAD results in a certain range of specificity. Both approaches were attempted and will be discussed.
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 6 Farmhouse System
### Monitoring Station  
In terms of hardware, we are relying on a very cheap (70 GBP) commercially available mini-computer, Raspberry Pi 4B+ (RPi4), used together with an SD card, a keyboard, a mouse, and a monitor to provide a fully functional computer with basic capabilities, which is more than enough to fully fulfill the requirement of the use case. Figure 12 shows the  
Figure 11: Gateway diagram.  
Figure 10: Prototyped Gateway.  
schema of the system located in the farmhouse and the connections for each subsystem oriented to the hardware deployment. This image is the setup achieved on our premises. This hardware will be situated in the farm's control and management centre, which consists of the mini-computer, Raspberry Pi 4, with an SD card, a keyboard, a monitor, and a mouse to monitor and control the aquaponics system remotely.  
In terms of the software to show the real-time data monitoring information of the whole system, several monitoring tools are available on the market. We have been customizing Grafana as the tool to display the sensor metrics on screen. Figure 13 shows the graphical interface shown to farmers on the mini-computer. Using displayed or obtained information, the farmer decides whether to open the air pump, water pump, lights, and feeder or make other changes required to sustain fish and plant health.
## 4 Methodology  
Two different experiments were designed in the study. Sentinel data were used in the first experiment. The study area was selected from Istanbul, Mugla, and Kocaeli cities in Turkey. In this experiment, Sentinel-2A images were used. Sentinel-2A bands and spectral indices produced from the bands were stacked. Two different combinations were used as Sentinel datasets. The first dataset included five bands (R, G, B, NIR, and NDVI). The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Pixel Count** & **Percentage (\%)** \\ \hline Artificial surfaces & 1,972,691 & 30.0 \\ Forest and semi-natural areas & 2,266,083 & 34.5 \\ Water bodies & 2,331,916 & 35.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training data class distribution for Sentinel dataset.  
Figure 5: Sample image of Zurich Summer dataset (ZH-12): (**a**) original image; (**b**) ground truth.  
second dataset contained seven bands (R, G, B, NIR, NDVI, SR, and DVI). Additionally, the effect of the size of the input images used on the classification was examined.  
In the second experiment, the CNN-MRS model was applied to the Zurich Summer public dataset. At the end of the experiment, the results of the Zurich dataset were compared with other state-of-the-art CNN models.  
In the experiments, patch sizes of 32 \(\times\) 32, 64 \(\times\) 64, and 128 \(\times\) 128 pixels were used. The results are shown in the form of tables and land cover maps. In Figure 6, the flowchart of the proposed CNN-MRS model is presented.
# High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data
## 4 Conclusions and Discussion  
In response to the limitations of traditional DMO stacking in field data processing, this paper proposes a high-order OC-seislet stacking method based on seislet transform theory. This approach effectively reduces the DMO accuracy issues caused by velocity errors in CMP gathers under low-SNR conditions, achieving high-fidelity stacking results and providing an effective partial imaging solution. According to synthetic data stacking results under inaccurate velocity analysis conditions, high-order OC-seislet stacking has improved the SNR 87% compared to traditional equal-weight DMO stacking, with more obvious layering and a more accurate amplitude. The field data with diffraction waves also indicate that this method can efficiently stack reflection waves and weak diffraction waves simultaneously; therefore, the combination of high-order OC-seislet stacking and normalization, as well as thresholding denoising, has potential applications.  
Meanwhile, this stacking method also has certain limitations. In Section 2, the stacking theory based on the lifting scheme has the requirement for seismic traces to be \(2^{n}\) in the seislet stacking. To meet this condition, it is often necessary to expand the original number of seismic traces, which increases the complexity of this method to a certain extent. At the same time, in Section 3.2, the multiple Fourier-transform processing of field data in the frequency-wavenumber domain can result in rounding errors and approximations, requiring the appropriate removal of shallow outliers to count the number of stacking traces. The calculation of the absolute value of the shallow amplitude is not accurate enough. Additionally, the framework of this method also allows for the consideration of other sparse transforms, such as the Radon transform [43], to perform data stacking. This provides new technical ideas for designing stacking methods that balance a high SNR and high fidelity.  
Conceptualization, Y.L. and T.P.; methodology, T.P. and Y.L.; software, Y.L.; validation, Y.L., D.L., P.X. and J.C.; formal analysis, T.P. and Y.L.; investigation, T.P. and Y.L.; resources, Y.L. and D.L.; data curation, T.P. and Y.L.; writing--original draft preparation, T.P.; writing--review and editing, Y.L., T.P. and J.C.; visualization, T.P.; supervision, Y.L.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the National Natural Science Foundation of China, grant number 42474144.  
Not applicable.  
Figure 13: Comparison of stacking results using different methods. (**a**) NMO stacking with stronger reflected waves and very weak diffracted waves; (**b**) conventional DMO stacking with diffraction waves enhanced but reflected waves decreased; (**c**) high-order OC-seislet stacking with thresholding can simultaneously efficiently stack reflected and diffracted waves.
# A Synthetic Nervous System Controls
## 2 Methods
### Design Process for a Single Joint
#### 2.5.6 Optimization Process  
The last part of our design method is the optimization of four parameters per joint: \(G_{syn,P}\), \(G_{syn,I}\), \(T_{max,ext}\), \(T_{max,flx}\). \(G_{syn,P}\) and \(G_{syn,I}\) are the synapse strengths from the difference and integral positive and negative neurons in the joint position control network, respectively (Effectively the proportional and integral gain of the feedback controller, respectively; Figure 4). \(T_{max,ext}\) and \(T_{max,flx}\) are the amplitudes of the muscle stimulus-tension curves (Equation (3)). These parameters together determine the stiffness and torque output of each joint. The optimization was automated for each joint in our SimScan tool (Section 2.1). For each joint, an identical, four-step reference trajectory was provided, and the following objective function, \(f_{obj}\), was minimized using MATLAB's built in fmincon function, an interior-point, gradient-based nonlinear program solver:  
\[\begin{split} f_{obj}=(\text{maxOvershoot}-\text{desiredOvershoot}) ^{2}+(1000\times(\text{maxRiseTime}-\text{desiredRiseTime}))^{2}\\ +(1000\times(\text{settlingTimeMax}-\text{desiredSettlingTime} ))^{2}\end{split} \tag{8}\]  
This function calculates the squared deviation of the tracking performance from the desired values. This value is computed separately for each step in the reference trajectory, and added for all four steps to evaluate one parameter value combination. In our trials, desiredOvershoot, desiredRiseTime, and desiredSettlingTime where chosen to be 0, 0.02, and 0.03, respectively. The factors of 1000 were included to weight the different terms in \(f_{obj}\) approximately equally.  
Every joint's parameters were optimized to track the commanded position very well (Figure 9). One example of a joint controller's reference tracking before and after optimization is included. This chosen case, while still good, was the worst joint in terms of reference tracking; the other 29 joints that were optimized produced even better reference tracking results.  
Figure 8: Importance of synaptic strength to the integral neuron in joint position control. (**a**) Commanded position and perceived position for the selected case. Lower integration speed and larger amplification tuned during the optimization process results in good reference tracking; (**b**) Voltage of integral neuron for the selected case. Lower integral speed results in voltages far away from saturation points; (**c**) Commanded position and perceived position for the undesirable case. The joint never gets to its commanded position because the integral neuron saturates; (**d**) Voltage of integral neuron for the undesirable case. Higher integration speeds cause the integral neuron to reach the synaptic thresholds, losing its ability to send information.
# HI-Sky: Hash Index-Based Skyline Query Processing
## 2 Related Study
### Parallel and Distributed Skyline Computation  
Parallel and distributed skyline query methods have been proposed to rapidly compute skylines in parallel and distributed environments. The critical aspect of performance for these skyline queries is the assignment of data distributed to each system or processor. Therefore, various data-partitioning techniques have been proposed. In particular, [PERSON] et al. [18] proposed a skyline query method that has received significant attention and is based on angle-based space partitioning (ABSP). Grid partitioning, which is a traditional partitioning approach, can potentially create a partition that does not contain a skyline. By contrast, these unnecessary partitions are less likely to be created when using ABSP because it reflects the topological characteristics of the skyline. Because the local skyline generated in each partition using ABSP is likely to become the global skyline, which is the final skyline query result, ABSP can respond more quickly to the skyline query request because it operates using distributed systems. However, in ABSP, as the numbers of dimensions and partitions increase, the likelihood of these unnecessary local skylines occurring significantly increases, and though they do not become global skylines at the partition boundary, overall efficiency is significantly reduced as a consequence. Therefore, although this method can respond quickly when used in a distributed environment, it is unsuitable for high-dimensional data.  
New skyline query methods based on MapReduce technology have recently been proposed and have shown excellent results in the field of big data processing. Early applications of MapReduce included traditional skyline query methods [19], and existing distributed environments were also converted into Hadoop environments [20]. However, these methods are still ineffective on big data because they require dominance tests on all data to compute the skyline. Therefore, recent work has yielded a new method of combining MapReduce with index structure-based skyline methods. These methods can obtain skylines from a large number of data in a single scan by using group-to-group comparison instead of data-to-data comparison [21; 22; 23; 24]. Also, skyline queries using an environment like SpatialHadoop [25], an extended Hadoop that supports various indexes for spatial data, have been proposed [26]. However, because their performance is highly dependent on the index structure used, which attracts continuous attention in the field of skyline computation.
# Kinematic Analysis and Verification of a New 5-DOF Parallel Mechanism
## 3 Kinematic Analysis
### Establishment of Inverse Kinematics Equations
The velocity equation of the mechanism is obtained by taking the derivative of Equation (11) with respect to time. Similarly, the acceleration equation of the mechanism is obtained by taking the derivative of the velocity equation with respect to time. \[\left( \begin{array}{c}\dot{U}_{1z}\\ \dot{U}_{2z}\\ \dot{U}_{3z}\\ \dot{U}_{4z}\\ \dot{U}_{5z}\end{array} \right)=\left( \begin{array}{cccc}\frac{\partial U_{1z} }{\partial x}&\frac{\partial U_{1z}}{\partial y}&\frac{\partial U_{1z}}{ \partial z}&\frac{\partial U_{1z}}{\partial x}&\frac{\partial U_{1z}}{ \partial\delta}\\ \frac{\partial U_{2z}}{\partial x}&\frac{\partial U_{2z}}{\partial y}&\frac{ \partial U_{2z}}{\partial z}&\frac{\partial U_{2z}}{\partial x}&\frac{ \partial U_{2z}}{\partial\delta}\\ \frac{\partial U_{1z}}{\partial x}&\frac{\partial U_{3z}}{\partial y}&\frac{ \partial U_{3z}}{\partial z}&\frac{\partial U_{3z}}{\partial x}&\frac{ \partial U_{3z}}{\partial\delta}\\ \frac{\partial U_{4z}}{\partial x}&\frac{\partial U_{4z}}{\partial y}&\frac{ \partial U_{4z}}{\partial z}&\frac{\partial U_{4z}}{\partial x}&\frac{ \partial U_{4z}}{\partial\delta}\\ \frac{\partial U_{5z}}{\partial x}&\frac{\partial U_{5z}}{\partial y}&\frac{ \partial U_{5z}}{\partial z}&\frac{\partial U_{5z}}{\partial x}&\frac{ \partial U_{5z}}{\partial\delta}\\ \end{array} \right)\left( \begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{z}\\ \dot{\beta}\end{array} \right) \tag{12}\]  
where \(U_{iz}(i=1,2,\ldots,5)\) represents the coordinate of the \(Z\) axis of the five sliders in the static coordinate system.
## Appendix C Position of a Point with Regard to a Closed Curve  
In order to detect if a wind turbine is inside the CA, it is necessary to program a function to deduce whether a point is inside, outside, or in the contour of a closed polygon. It is convenient to first define the operation \(\stackrel{{\bullet}}{{+}}\) as well as its opposite \(\stackrel{{\bullet}}{{-}}\) as  
\[a,b \in [-\pi,\pi],\] \[a\stackrel{{\bullet}}{{+}}b \Leftrightarrow \left\{\begin{array}{ll}a+b\quad\mathrm{if}\quad a+b\in[-\pi, \pi]\\ a+b+2\pi\quad\mathrm{if}\quad a+b<-\pi\\ a+b-2\pi\quad\mathrm{if}\quad a+b>\pi\end{array}\right. \tag{13}\] \[a\stackrel{{\bullet}}{{-}}b \Leftrightarrow \left\{\begin{array}{ll}a-b\quad\mathrm{if}\quad a-b\in[-\pi, \pi]\\ a-b+2\pi\quad\mathrm{if}\quad a-b<-\pi\\ a-b-2\pi\quad\mathrm{if}\quad a-b>\pi\end{array}\right. \tag{14}\]  
The operation \(\stackrel{{\bullet}}{{+}}\) together with the interval \([-\pi,\pi]\) constitutes an abelian group satisfying commutativity, closure, associativity, the existence of a neutral element (0), and an opposite element (the opposite of \(c\) is \(-c\)).  
With these definitions, and assuming that a closed curve \(C\) is composed of a sequence of \(m\) points \((x_{i},y_{i})\) (in which \(x_{m}=x_{0}\) and \(y_{m}=y_{0}\)), then in order to ascertain whether a point \(P(x^{p},y^{p})\) is inside or outside this curve, it is sufficient to evaluate the summary  
\[\Gamma=\sum_{k=1}^{m}\biggl{\{}\arctan(y_{k+1}-y^{p},x_{k+1}-x^{p})\stackrel{{ \bullet}}{{-}}\arctan(y_{k}-y^{p},x_{k}-x^{p})\biggr{\}} \tag{15}\]  
By means of [PERSON] theorem, it can be demonstrated that  
\[P(x,y)\ \mathrm{is\ inside}\ C \Leftrightarrow \Gamma=2\pi\quad\mathrm{or}-2\pi \tag{16}\] \[P(x,y)\ \mathrm{is\ outside}\ C \Leftrightarrow \Gamma=0 \tag{17}\]With this formulation, then \(\Gamma=\pi\quad\text{or}\;-\pi\) for points belonging to the contour. In order to detect this situation, it is preferable to check each value of the summary in (A6). The point will belong to the contour if and only if any of the addends yield \(-\pi\) or \(\pi\).