esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Monochromatic Value Space Transformation
The monochromatic value image \(F_{ij}\) obtained after transformation is regarded as a multi-channel signal (with \(N\) segments and \(M\) elements per segment), and the Kurtosis can be written as  
\[E=K^{2}=\left(\sum\limits_{j=1}^{N}\frac{\sum\limits_{i=1}^{M}\left(F_{ij}-\mu_{ j}\right)^{4}}{\left(\sum\limits_{i=1}^{M}\left(F_{ij}-\mu_{j}\right)^{2} \right)^{2}}-3\right)^{2}, \tag{5}\]  
where \(\mu_{j}\) is the mean of column j in the image \(F_{ij}\). The solution of maximizing (5) is given in Equation (6):  
\[\frac{\partial E}{\partial w_{r}}=\frac{\partial E}{\partial w_{g}}=\frac{ \partial E}{\partial w_{b}}=0, \tag{6}\]  
Obviously, it is difficult to solve the above equation. We can learn such knowledge from [PERSON]. [PERSON] et al. [19]. The continuous coefficient \(w_{r},w_{g}\) and \(w_{b}\) in Equation (1) determine an infinite color feature set. To calculateexpediently, \(w_{r},w_{g}\) and \(w_{b}\) are discretized as integersconfined in [-2,2], and some common color spaces and models are also covered by this range, such as R + G + B, R-B, and so on, implying that it is feasible. The laser used in the paper is red, and R component of the original image is higher in content than the other components, so limit \(w_{r}\geq 0\). The value ranges of \(w_{r},w_{g}\) and \(w_{b}\) are \(w_{r}\in\{0,\ 1,\ 2\}\), \(w_{g},\ w_{b}\in\{-2,\ -1,\ 0,\ 1,\ 2\}\), where \(w_{r},w_{g}\) and \(w_{b}\) are not allowed to be zero at the same time. Then the coefficient vector can be solved by the traversal method. In Figure 5, a red single line laser is used. The optimal coefficient vector of the captured image is \((w_{r},\ w_{g},\ w_{b})=(1,\ -1,\ 0)\), and the monochromatic value space is R-G, whose transformation result of laser stripe is shown in Figure 5d. For comparison, Figure 5c shows the result in typical gray scale space \((w_{r},\ w_{g},\ w_{b})=(0.30,\ 0.59,\ 0.11)\). It is obvious that R-G space has more advantages in noise suppression and better SNR is obtained from it. The laser stripe is clearly distinguished from the background in R-G space, and the amount of data is only one third of the original image in Figure 5a. To reduce harm of cold environment to people, we usually measure snow sculpture in the daytime. Also, snowy days are not chosen to avoid uneven distribution of new snow and its impact on measurement results. The R, G, B weights in the algorithm of this paper are dynamically changed. During each measurement, we readjust the parameters according to the calculation results to adapt to the measurement environment at that time.
## 3 Experimental Setup  
In order to measure the surface pressure fluctuation downstream of the critical nozzle causing the AIV, the high-pressure gas flowrate standard system of the Korea Research Institute of Standardsand Science (KRISS) was used as shown in Figure 2[15]. The high-pressure gas flow standard system is a blow-down system in which a gas is stored in a storage tank at a high pressure using a compressor, and then the stored gas is adjusted to the specified pressure and discharged to a test pipeline. To keep pressure and temperature stable during the test, the compressed air in the reservoir is adjusted in the primary pressure regulating valve up to 50 bar and stored in the temperature control loop. Compressed air passing through the temperature control loop is supplied to the test section with specific pressure regulation in the secondary pressure regulating valve.  
To measure the surface pressure on the inner wall of pipe downstream of the critical nozzle, an experiment was conducted by installing a second pressure control valve and a critical nozzle to the straight pipe of length 50D after the U-band. The dynamic pressure sensor used for the pressure measurement was the 102B15 model from PCB Piezotronic with a resonance frequency of 500 kHz and a noise measurement sensitivity of 3.6 mV/kPa. The low-frequency response (-5%), which is a low-frequency characteristic, was 0.5 Hz, and the sensitivity was maintained at the same sensitivity throughout the frequency range of interest of this measurement. For accurate measurement of the AIV generated inside the pipe without any interference, the sensor was installed in the form of flush mounting as shown in Figure 3, which shows a sensor installed inside the pipe to measure the AIV of the critical nozzle. It was equipped with a pressure and temperature sensor to measure the reference flow rate in the pipe according to ISO 9300. A dynamic pressure sensor was installed at the position of 2D and 4D downstream of the critical nozzle. Each dynamic pressure sensor was first installed in the jig which was manufactured for the easy installation on the pipe. The AIV signal detected through the dynamic pressure sensor was recorded by the Digitizer (NI PXI 5922) through the PCB conditioner Model 482C05. The PXI 5922 can record up to 10 M signals per second at a resolution of 18 bits using two-channel simultaneously and was installed on the NI PXI 1033-Chassis and connected to the personal computer (PC) via an Express card, which runs through the LabVIEW program. Signal acquisition was made at the sampling rate of 50 kHz, and the acquired sample size was 500,000. The related measurement devices are shown in Figure 4.  
Figure 2: High pressure gas flowrate standard system at Korea Research Institute of Standards and Science (KRISS).
# Data-Driven and Machine-Learning-Based Real-Time Viscosity Measurement Using a Compliant Mechanism
## 3 Numerical Simulations and ML Models
### Discussions on the FEA and ML Model Analysis  
A modal vibration analysis was performed to determine the resonant frequencies within the frequency band of up to 400 Hz. The analysis was performed using the FEA approach, where the rigid links in the compliant mechanism were connected to each other by flexible joints. The simulation results reveal the first resonant frequency to be at 308 Hz, which is 47% higher than the vibration actuator's operating frequency. Furthermore, there is only one resonant frequency within the frequency band. The difference in the resonant and operating frequencies ensures non-resonant operation of the mechanical structure, which, in turn, results in improved system reliability, reduced possibilities of structural damage, and a lower level of noise.  
The ML model analysis for viscosity prediction shows that Multiple Linear Regression (MLR) achieves the highest accuracy, with the lowest \(MSE\) (0.69) and \(RMSE\) (0.83) and the highest \(R\)-_squared_ (0.99), indicating strong predictive performance. Random Forest (RF) also performs well, with an \(MSE\) of 3.77, an \(RMSE\) of 1.94, and an \(R\)-_squared_ of 0.98, making it a robust alternative due to its ensemble method. Support Vector Regression (SVR) achieves a moderate accuracy (\(MSE\) of 6.45, \(RMSE\) of 2.54, and \(R\)-_squared_ of 0.97). Conversely, the decision tree model has the highest \(MSE\) (12) and the lowest \(R\)-_squared_ (0.94), indicating lower predictive accuracy. Table 2 compares the details, performance, and limitations of various approaches reported in the literature with that of the present work.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **ML Model(s) Used** & **Application** & **Accuracy/ Performance Metrics** & **Fluid Type** & **Main Contributions and Limitations** \\ \hline \hline \multirow{4}{*}{This work} & MLR, DT, SVR, RF & Viscosity prediction & \(\begin{array}{c}MLR:R^{2}=0.99\text{,}\\ RMSE=0.83\text{,}\\ MSE=0.69\end{array}\) & All solutions & \begin{tabular}[]{c}Contributions: High accuracy, suitable for in-line and handheld use \\ Limitations: \\ DT model had \\ lower accuracy \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } \\  & &
## 2 Results and Discussion
### Salivary Flow Rate and Temporal Sensory Perception  
A one-way ANOVA conducted on the salivary flow rate for each panelist is shown in Supplemental Table S5. The mean salivary flow rate was 2.50 mL/min and there was a two-fold difference between the maximum (3.18 mL/min) and minimum (1.39 mL/min) salivary flow rate within panelists. Categorical salivary flow rates were designated based on the value's position above or below the mean. Values above the mean were designated as high salivary flow rate (HF); while values below the mean were designated as low salivary flow rate (LF). Within the sensory panel (\(n=8\)), there were six individuals with high salivary flow rate (2.53 mL/min to 3.18 mL/min; two panelists were considered low salivary flow rate (1.39 mL/min to 1.58 mL/min). Supplemental Table S6 shows a one-way ANOVA for the first time of perception by the two astringency subqualities and fruit retronasal aroma for salivary flow rate. The astringency subquality suede (\(p=0.001\)) was the only statistically significant attribute, whereby LF panelists perceived it significantly later post-expectoration compared to HF panelists. Based on the present results, it appears the suede subquality may reflect the established pattern where LF individuals perceived astringency later than HF individuals which aligns with the results of previous research [35; 37]. However, this panel was small in size (\(n=8\)) and had many HF panelists (\(n=6\)) compared to LF panelists (\(n=2\)). Therefore, more assertive conclusions are not possible in light of these results. Further research with a more balanced panel in terms of salivary flow rate would be required for unequivocal conclusions.
## 3 Methodology
### Experimental Framework  
In this section, we review the experimental procedure that was used in this study (Figure 1). The procedure was designed to experimentally evaluate and validate the effectiveness of FPA and its enhanced variants for detecting Android malware. Specifically, two scenarios were evaluated and explored, and the resultant Android detection model performances were compared in a non-biased and consistent manner.  
Scenario 1: First, FPA and selected ML algorithms with varying computational characteristics were specifically applied to the original Android malware datasets. The result of this experiment will empirically validate FPA's performance and efficacy in detecting Android malware. Furthermore, to resolve the inherent class imbalance in the Android malware datasets, a data sampling technique was deployed. In other words, data over-sampling (SMOTE) was employed to resolve the class imbalance in the malware datasets. SMOTE is a notable data sampling approach used to solve the issue of class imbalance [27, 28]. The results of these tests will show the influence of the data sampling approach in FPA in Android malware detection.  
Scenario 2: In this case, FPA and its enhanced variants were applied to the balanced datasets. The enhanced variants of FPA are the cascade generalization-based FPA (Cas_FPA) and rotation forest-based FPA (RoF_FPA). The performance of FPA and its variants were analysed and compared with existing Android malware detection models.  
The observed findings and conclusions derived from the experimental results (Scenarios 1 and 2) were utilized to address the research questions listed in Section 1.  
Figure 1: Experimental Framework.  
For training and testing models based on the scenarios, the datasets were divided into test (30%) and train datasets (70%). Thereafter, the training datasets were balanced using SMOTE and later used to train the experimented models using the 10-fold cross-validation (CV) technique. The K-fold (where \(k=10\)) CV approach was used for the development and evaluation of the Android malware detection models. The 10-fold CV option is based on the ability to create Android malware detection models with a low impact on the issue of class imbalance [28; 54; 55; 56; 57]. The training and testing datasets were randomly generated, with no duplicates or shared values. Eventually, for each studied dataset, the average of the returned outcomes was utilized as the final assessment criterion. In addition, each experimental process was repeated 10 times; that is, each experiment was conducted 100 times to avoid bias [58; 59; 60]. The Waikato Environment for Knowledge Analysis (WEKA) machine learning library [61] and R programming language [62] were used for the experimentation on an Intel(R) Core(tm) machine equipped with i7-6700, running at a speed of 3.4 GHz CPU with 16 GB RAM.
# Remote Monitoring of the Impact of Oil Spills on Vegetation in the Niger Delta, Nigeria
## 1 Introduction
This study, therefore, aims to address this by determining the impact of oil spills on a range of vegetation types in the Niger Delta region by analysing location-specific changes in satellite-derived NDVI values at spill and non-spill sites. This will be achieved by (1) analysing the impact of oil spill volume and time after an oil spill on the health of three types of vegetation and (2) undertaking temporal monitoring of NDVI for the three types of vegetation to determine their medium- and long-term responses to oil spills. For this study, the vegetation types are divided into three broad categories: dense, sparse, and mangrove. The hypothesised result is that (1) the oil spill volume and the time gap after the oil spill determines the impact of the oil spill event on the vegetation; (2) there is a significant difference, as revealed by the NDVI, between the health of vegetation exposed to oil spill compared to the vegetation not exposed to oil spill and (3) different vegetation types will respond differently to the oil spill events.
## 1 Introduction  
Recent advances in the technological application of high-temperature superconductors (HTS) [1] are based on the concept of a coated conductor, a strong metallic substrate (e.g., nickel and iron alloys) that supports a superconducting film deposited epitaxially on a buffer layer and protected via the application of a conductive metal (e.g., silver or copper). The different processes involved in the realization of a CC and the different architectures possible in terms of buffer layer structure and materials involved lead to a vast outcome concerning the characteristics of the tapes. The different productive choices influence the superconducting properties, such as the critical temperature and the magnetic field and angular behaviors of the superconducting currents, which are highly dependent on the crystalline microstructure. In this framework, i.e., the increase in commercial CC production driven by fusion applications [2] and the enhancement of their performance, a solid benchmark platform is necessary to be able to measure their properties in the operating conditions of superconductivity laboratories that operate with such conductors. To fully characterize and validate the tapes' properties, experimental facilities able to deliver large amounts of current (e.g., I \(>\) 1000 A) at high magnetic fields (\(\mu_{0}\)H \(>\) 12 T) performing measurements at different angular configurations are, in fact, required. This is required in order to support the optimization of the production process so that industriesare able to tailor the CCs' properties for applications to achieve the best performance for the development of cables and coils.  
In this work, we report our findings regarding the development of a simple but effective high-current probe. The probe is designed to work in liquid helium bath, includes HTS leads in order to minimize both helium evaporation and thermal transfer to the sample, and is conceptualized to allow a modular sample-holder design comprising an angular measurement sample holder. To the best of our knowledge, not many groups perform angular measurements of REBCO-coated conductors on full 4 mm tapes at liquid helium temperature (e.g., [3; 4; 5]). This work thus represents a useful guide to approaching and solving the experimental problems that can arise in this kind of challenging measurement. The work addresses several aspects, such as the production of simple and effective current leads, the design of different sample holders, and the adoption of different sample mounting strategies and soldering alloys, and reports a comparison of the results obtained for different samples in different conditions.
## 5 Results and Discussion  
### Stability Check  
Table 6 shows the most characteristic stability values for each of the specified structural alternatives and loading conditions.  
For the transverse metacentric radius (BM), the largest value is obtained in the \(V_{2}\) model. This is mainly due to the fact that the \(V_{2}\) model has a larger beam, which results in a higher buoyancy inertia. It is worth mentioning that as the inertia approaches that of a rectangle (with a high waterplane coefficient), it grows according to  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Version**} & \multicolumn{2}{c}{**Loading**} & \multirow{2}{*}{\(\mathbf{\Delta}\) **(Tom)**} & \multirow{2}{*}{**KB (m)**} & \multirow{2}{*}{\(\mathbf{BM}\) **(m)**} & \multirow{2}{*}{**KG (m)**} & \multirow{2}{*}{\(\mathbf{GM}\) **(m)**} \\  & & & & & & \\ \hline \multirow{6}{*}{\(V_{0}\)} & \(L_{1}\) & 35,870 & 6.49 & 7.83 & 13.4 & 0.91 \\  & \(L_{2}\) & 35,924 & 6.49 & 7.82 & 13.52 & 0.8 \\  & \(L_{3}\) & 19,854 & 4.21 & 10.64 & 7.94 & 6.92 \\  & \(L_{4}\) & 19,256 & 4.12 & 10.83 & 8.18 & 6.  
which shows consistency in the results. Similarly, model V\({}_{1}\) has the smallest metacentric radius due to its smaller beam.  
With respect to the center of gravity (KG), it can be noted that model V\({}_{2}\) has the lowest center of gravity compared with the other models. This is due to the fact that the breadth is smaller in the \(V_{2}\) model. It is necessary to distribute the load over the beam and length to equalize the load with the other models, which does not affect the height of the KG. Also, the model with the higher depth has the highest center of gravity.  
Metacentric height (GM) is a value indicating stability at small angles. Therefore, a higher GM provides a higher righting arm, which means higher stability. The V\({}_{2}\) model is expected to have the largest GM because it has the largest BM and the smallest KG. As for the V\({}_{3}\) model, despite not having the lowest BM, its lower KG disadvantages it, placing it as the option with the lowest stability.
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 2 Related Works and Contributions
In a single cell scenario with 1000 devices present, they show that their proposal can reduce the packet error rate by 20% with respect to legacy LoRaWAN networks. In this paper, we consider multiple co-located network operators, unlike the vast majority of the state-of-the-art that work on single-cell, single operator networks [2; 4; 5; 7; 9; 10; 12]. This invalidates many of the state-of-the-art proposals that presume that their algorithms can be addressed in a centralized manner, an unrealistic assumption in a multi-operator deployment. Our proposals compute solutions at the different operator levels with the results relayed back to the devices during their listening windows. This means that our proposals can be implemented for all LoRa device classes, unlike many state-of-the-art algorithms that require device-level intelligence [11; 12]. Additionally, our work does not rely on classical scheduling, as in [10], an unrealistic approach with a 1% duty cycle limitation in place. We summarize our contributions, as follows:  
1. We propose an optimal formulation for SF and channel assignment in a multi-operator LoRaWAN deployment. We divide the problem into two: (a) an SF assignment subproblem and (b) a channel assignment subproblem. We propose non-cooperative games for each subproblem, which we solve successively until reaching their pure Nash equilibrium. 2. We propose a semi-distributed approach for finding the NEs of the decoupled problem based on best response dynamics. 3. After proving their convergence, we propose a replicator dynamics based learning approach for finding the NE for the channel selection game. We thereafter propose a distributed approach for solving the decoupled problem. 4. We simulate our proposals and compare them to the concurrent approach to SF and channel selection in LoRaWAN networks. We highlight the gains that they bring in terms of throughput, packet delivery ratios, and energy efficiency.
# Photovoltaic Cell and Module I-V Characteristic Approximation Using Bezier Curves
## 4 Results
### Parameter Variation  
In order to further validate the proposed method, in this section we analyze the temperature and irradiance influence for the MSMD290 AS-36_EU Monocrystalline PV module. An extensive study of the parameter influence over the PV cell can be found in [12]. It is important to notice that the Bezier approximation is not related to any of the parameter variation, just to the specified points \(P_{sc}\), \(P_{mp}\), \(P_{oc}\) and the parasitic resistances \(R_{sl0}\) and \(R_{s0}\) as already stated. The challenge becomes in this case the finding of the new position for the control points and the new values for the parasitic resistances.  
Figure 12: Bézier approximation using the least squares method.  
Figure 13: The relative error of the least squares method Bézier based approximation compared with the actual data. The absolute error \(\Delta I=I_{Bezier}-I\) is also indicated.  
[PERSON] et al. [5] accurately describe the short circuit current variation as in (22):  
\[I_{sc}=\left(\frac{R_{sh}+R_{s}}{R_{sh}}I_{sc,ref}+k_{I}\Delta T\right)\frac{G}{G _{ref}}\approx\left(I_{sc,ref}+k_{I}\Delta T\right)\frac{G}{G_{ref}} \tag{22}\]  
[PERSON] and [PERSON] [9] propose for the \(V_{\alpha,cell}\) the following variation (23):  
\[V_{\alpha,cell}=V_{0,cell,ref}+a\frac{kT}{q}ln\frac{G}{G_{ref}}+k_{v}\Delta T \tag{23}\]  
Equation (23) proved to be too conservative in this case, as larger \(V_{\alpha,cell}\) variations were observed. A better approximation is the empirical law (24):  
\[V_{\alpha}=29.579+2.1934\ln G \tag{24}\]  
A possible way for defining \(R_{sh}\) behavior is suggested in [12], as in (25) with \(k_{Rsh}\) estimated as 8 for the best fit.  
\[R_{sh}=R_{sh,ref}\left(\frac{T_{ref}}{T}\right)^{k_{Rsh}} \tag{25}\]  
For \(R_{s}\), a linear variation law (26) is given in [12] with \(\alpha_{Rs}=-0.01\text{K}^{-1}\), again for the best fit:  
\[R_{s}=R_{s,ref}\left[1+\alpha_{Rs}\left(T-T_{ref}\right)\right] \tag{26}\]  
Figure 14 shows the irradiance influence for the I-V module characteristic, where the approximated data using our proposed method is plotted with solid lines and the experimental data is represented with markers. \(I_{sc},~{}V_{\alpha c},~{}R_{sh}\), and \(R_{s}\) were computed using (22), (24-26) respectively.  
The temperature dependent Bezier curves resulted from our algorithm compared with the actual data are introduced in Figure 15. Once again, the results show a very good correlation between the modeled data and the actual data.  
Figure 14: Bézier approximation of the I-V irradiance dependent characteristics for the MSMD290 AS-36_EU monocrystalline PV module. The lines represent the computed curves, whereas the markers represent the actual data.