esa-sceva/satcom-chunk-collection huggingface dataset:
# Sharpness-Based Distance Detection
## 2 Target Identification and Location
### YOLOv7 Basic Model  
This paper adopts the YOLOv7 model from the YOLO series for object tracking and recognition. YOLOv7 is the base model in the YOLO family, which outperforms most known target detectors in terms of speed and accuracy in the range of 5-160 frames/s, and has the highest accuracy among the real-time target detectors above 30 frames/s known for GPU V100. Depending on the code-running environment (edge GPU, normal GPU, and cloud GPU), three basic models are set up, which are called YOLOv7-tiny, YOLOv7 and YOLOv7-W6. Compared with other network models in the YOLO series, the detection idea of YOLOv7 is similar to that of YOLOv4 and YOLOv5. YOLO's real-time object detection capabilities enable rapid recognition and tracking of a variety of objects such as vehicles, pedestrians [27; 28], bicycles, and other obstacles [29; 30]. These capabilities have been applied in many areas, including motion recognition [31] in video sequences used for surveillance [32], sports analysis [33], and human-computer interaction [34].  
In the network structure of YOLOv7, the network is composed of three modules: Input (input), Backbone (backbone) and Head (head network). When an image passes through this neural network, the Input module scales the input image with the purpose of meeting the input size requirements of the backbone network. The backbone network is composed of a CBS layer, E-ELAN (Extended-ELAN) layer [35], MPConv layer, and SPPCSPC layer. The CBS layer is the convolutional layer, and the E-ELAN layer is the high-efficiency layer; the ELAN module is shown in Figure 1. The aggregation network changes the original computational modules on the basis of the ELAN layer, and achieves this without destroying the ELAN layer through expand, shuffle, and merge cardinality to improve the learning ability of the network without destroying the original gradient path. The MPConv layer adds a Maxpool (Maximum Pooling) layer to the convolutional layer, which fuses the previously extracted feature information and improves the network's generalisability. The SPPCSPC layer adds multiple MaxPool layers to avoid the image processing operations that lead to image distortion, and also achieves the fusion of feature information at different levels. Finally, the fused feature information is detected and the results are outputted. Evaluated on the MS COCO dataset test-dev 2017, the YOLOv7-E6 achieved 55.9% AP and an AP50 resolution of 73.5% with an input size of 1280 pixels at 50 FPS on NVIDIA V100 (Nvidia, Santa Clara, CA, USA) [36].
## 8 Conclusions
### Obtained Results  
In order to see if the ANN-ALO system is successful enough at predicting future states of EEGs, it was applied to a total of 10 different EEG datasets recorded from random individuals at the Isparta State Hospital located in the city of Isparta, Turkey. The obtained findings showed that the considered ANN-ALO system can predict the future states of objective time series which means it can be used a predictive infrastructure for EEG time series. In addition, it was determined that the ANN-ALO even outperforms some other alternative hybrid systems formed with ANN and different swarm intelligence algorithm variations. Finally, the ANN-ALO is also a better prediction system than the traditional ANN-BPA system and also some traditional chaotic time series prediction approaches introducedin the associated literature. It is also important that the results here were validated statistically with the Giacomini-White Test. Except for the performed comparative works, some experiences from physicians who had used the ANN-ALO system in four separate hospitals in Isparta, Turkey were evaluated. The experiences reported in this manner showed positive results regarding the practical application of the system. All of these results showed that the ANN-ALO system can be used as an effective, and also quick, prediction approach. It was also observed that the system can be effectively used by physicians for early diagnosis and further investigations on brain activity.
## 3 Results and Discussion
### Model Fitting  
An RCCD was chosen in the current study as it permits the analysis of experimental factors at five levels offering reduced prediction error. The combination of RCCD with RSM provides useful information about the interactions among the experimental factors without the need to perform unnecessary experiments and simultaneously obtaining optimized results for the response. Four experimental factors were investigated in this study in order to determine the most suitable conditions for the extraction of safranal from the saffron stigmas by SPME. RCCD experiments were run in a random order for reducing the effect of uncontrolled variables. The RCCD design used in this study includes 16 factorial points, 6 center points, and 8 axial points selecting the value of \(\alpha=2\) for establishing rotatability conditions.  
The data acquired by the RCCD were generated into a response function linear model expressed by the following coded Equation (1):  
\[\text{Y}=94.44-0.2558\text{ A}+0.3750\text{ B}-0.6181\text{ C}-0.4528\text{ D} \tag{1}\]  
where Y is the percentage area of safranal, while A, B, C, and D are the coded values of sample weight, water volume, temperature, and exposure time, respectively.  
Residual analysis does not show any discrepancies from the linear model assumptions (Figure 2a), while the lambda value 1.00, obtained from the Box-Cox test (Figure 2b) suggested that the data did not require a specific transformation.  
Figure 2: (a) Normal plot of residuals; (b) Box–Cox plot.  
Analysis of variance (ANOVA) was used to confirm the adequacy of the linear model (Table 2). The model is significant with a \(p\)-value < 0.0001. All model terms were significant (\(p\)-value < 0.05). The extraction temperature seems to have a significant effect as shown from the differences observed in the absolute abundance between the chromatograms obtained at 45, 50 and 60 \({}^{\circ}\)C (Figure 1). The non-significance of the lack of fit (\(p\)-value > 0.05) showed that the linear model was valid for the current study. Moreover, the values of R\({}^{2}\) and adjusted R\({}^{2}\) were 0.7428 and 0.7017, which are in reasonable agreement with the predicted R\({}^{2}\) value of 0.6173, thus indicating a high correlation among the predicted and actual values.
## Materials and Methods
### Isolation of Total RNA and Gene Expression Analysis by RT-qPCR  
After allowing fungal growth for five days, _A. flavus_ mycelia were carefully collected by filtration with a cell strainer (SPL Life Sciences Co., Ltd., Gyeonggi-do, Korea). The filtered mycelia were left in a mortar and ground with some liquid nitrogen. Total RNA was obtained from the filtered _A. flavus_ mycelia by the addition of the QIAzol Lysis Reagent (QIAGEN Inc., Hilden, Germany).  
Extracted RNAs were first quantified while using a \(\upmu\)Drop(tm) Plate incorporated with Multiskan(tm) Go Microplate Spectrophotometer (Thermo Fisher Scientific Inc., Ratastie, Finland). Then, the quantified RNA was examined qualitatively by electrophoresis using a 1% agarose gel with ethidium bromide. Complementary DNA (cDNA) was made using a Maxima First Strand cDNA Synthesis kit (Thermo Fisher Scientific Inc., Ratastie, Finland). For the procedure, extracted RNAs, which were equivalent to 2 \(\upmu\)g, were used for cDNA synthesis.  
cDNA (100 ng) was used for the analysis of gene expression with a Rotor-Gene SYBR Green PCR kit (QIAGEN Inc., Hilden, Germany) and RT-qPCR analysis. Specific primers for the genes were synthesized by Genotech (Daejeon, Korea) to determine the comparative levels of gene expression. The primers for the genes involved in aflatoxin biosynthesis (_vap_, _afIC_, _afID_, _afIE_, _afIG_, _afIK_, _afIL_, _erg28_, _afR_, _afIS_, and 18S rRNA) are listed in Table S1. The PCR conditions were exactly the same as those reported previously [15].  
The RT-qPCR analysis was conducted three times for each sample. Differences in gene expression between the treated samples and the controls, after the addition of the test chemical, were calculated while using the \(2^{-\Delta\Delta\mathrm{Ct}}\) method [17], followed by one-way ANOVA incorporated with [PERSON]'s test (_p_ value \(<\) 0.05). The results were recalculated after normalization with 18S rRNA expression, and the gene expression comparisons were further analyzed.
# A Coupled Variational System for Image Decomposition along with Edges Detection
## 2 Prior Work  
To improve the denoising capability, [PERSON] (PM) [41] discussed the following PDE of anisotropic diffusion  
\[\frac{\partial u}{\partial t}=\mathrm{div}(h(|\
abla u|)\
abla u) \tag{1}\]  
where \(h(|\
abla u|)=\frac{1}{1+K|\
abla u|^{2}}\), \(K>0\) is a contrast parameter. Due to the impact of noise, the computation of gradient is not robust to outliers. Hence, with the aid of the Gaussian convolution, [PERSON] et al. [42] introduced the modified model  
\[\frac{\partial u}{\partial t}=\mathrm{div}(h(|G_{\sigma}*\
abla u|)\
abla u) \tag{2}\]  
where \(G_{\sigma}(x)=(2\pi\sigma)^{-1}\exp\bigl{(}-\bigl{(}|x|^{2}/2\sigma\bigr{)} \bigr{)}\) is the Gaussian kernel function and \(*\) means convolution. Because Gaussian smoothing is equivalent to solving the heat diffusion equation, [PERSON] et al. [39] used an auxiliary variable \(\omega\) to split Equation (1) into the following PDEs system  
\[\frac{\partial u}{\partial t}=\mathrm{div}(h(\omega)\
abla u) \tag{3}\]  
\[\frac{\partial\omega}{\partial t}=\lambda\mathrm{div}(\
abla\omega)+(1- \lambda)(|\
abla u|-\omega) \tag{4}\]where \(\lambda\) is a balancing parameter. Later, the authors in [40] used a weighted \(\text{TV}-L^{1}\) model to replace Equation (3) and considered the following coupled variational model  
\[\min_{u}\int_{\Omega}h(\omega)|\
abla u|dx+\int_{\Omega}\mu(x)|u-f|dx \tag{5}\]  
\[\frac{\partial\omega}{\partial t}=\lambda\text{div}(\
abla\omega)+(1-\lambda)( |\
abla u|-\omega) \tag{6}\]  
where \(\mu(x)\) is an adaptive parameters, \(\omega\) is called the pseudo edge function.  
Because Equation (5) is not suitable for Gaussian noise, and the solutions of Equations (4) and (6) are not the same as the form of Equation (2), [PERSON] et al. [11] proposed the following high order coupled model  
\[\min_{u,\omega}\alpha_{1}\int_{\Omega}h(\omega)|\
abla u-v|dx+\alpha_{2}\int_ {\Omega}|\
abla v|dx+\frac{\beta}{2}\int_{\Omega}|u-f|^{2}dx \tag{7}\]  
\[\min_{\omega}\int_{\Omega}|\
abla\omega|^{2}dx+\lambda\int_{\Omega}|\
abla u -\omega|^{2}dx \tag{8}\]  
where \(\alpha_{1},\ \alpha_{2},\ \beta,\ \lambda\) are positive parameters, \(f\) is the observed image.
## 3 Results
### Comparison between the Proposed Models and Empirical Formulae
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Sample Size** & **Best Methods** & **Correlation Coefficient** & **References** \\ \hline [PERSON] et al. (1999) & 25 & \(ANN\) & 0.91 & [25] \\ \hline [PERSON] (2011) & 105 & \begin{tabular}{c} Generalized Regression \\ Neural Network \\ \end{tabular} & 0.977 & [26] \\ \hline [PERSON] et al. (2015) & 100 & Neural Network & 0.9995 & [27] \\ \hline [PERSON] et al. (2017) & 132 & \(PSO\)–\(ANN\) & 0.9685 & [1] \\ \hline [PERSON] and [PERSON] (2018) & 59 & \(ICA\)-\(ANN\) & 0.96369 & [31] \\ \hline [PERSON] et al. (2021) & 50 & \(GP\) & 0.997 & [28] \\ \hline [PERSON] et al. (2019) & 59 & \
\begin{tabular}{c} _ANFIS_ \\ \end{tabular} & 0.967 & [33] \\ \hline [PERSON] et al. (2020) & 59 &
\begin{tabular}{c} Extreme Gradient \\ Boosting \\ \end{tabular} & 0.975 & [2] \\ \hline [PERSON] et al. (2021) & 72 & \(ANFIS\)–\(GMDH\)-\(PSO\) & 0.94 & [29] \\ \hline [PERSON] et al. (2020) & 20 & \(GA\)–\(ANFIS\) & 0.9935 & [34] \\ \hline [PERSON] et al. (2020) & 43 & \(GA\)-\(ANN\) & 0.998 & [32] \\ \hline [PERSON] et al. (2021) & 100 & \(MLP\)–\(GWO\) & 0.991 & [30] \\ \hline Our study & 100 & Deep Neural Network & 0.9996 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison between the proposed DNN model and some of the empirical models found in the literature. Figure 3: Performance measures of the DNN model using the \(K\)-fold cross-validation, with \(K=5\).
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Perturbation Force**} & \multirow{2}{*}{**Main Impact**} & \multicolumn{2}{c}{**Model**} \\ \cline{3-4}  & & LEO & GEO \\ \hline Earth’s non-spherical gravity & The orbital plane of LEO satellites & & EGM2008 (first 10 levels) [10] \\  & continuously rotates, and the GEO satellites drift east-west. & & EGM2008 (first 12 levels) [11] \\  & The eccentricity of the orbit will be & & \\  & changed for a long time, making the & & Sun, Moon and Other Near-Earth Planets (Ephemeris JPLDE436) \\ N body gravity & orbit more and more flat, which will & & Sun, Moon and Other Near-Earth Planets (Ephemeris JPLDE436) \\  & further increase the atmospheric & & Planets (Ephemeris JPLDE436) \\  & perturbation and reduce the orbit height. & & \\ Solid tide perturbation & Change the Earth’s gravity & & IERS2010 [12] & Not consider \\ Ocean tide perturbation & Change the Earth’s gravity & & FES2004 [13] & Not consider \\ Solar pressure & Change the eccentricity of a satellite & & ECOMC [13] \\ Earth albedo pressure & Change the eccentricity of a satellite & & Not consider \\ Atmospheric drag & Change the semi-major axis of the & & NRLMSISE-00 & Not consider \\  & satellite’s orbit & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The mechanical models used for various perturbation forces. Figure 3: Relationship between Keplerian parameters and geocentric equatorial frame [9]. variational equation of the orbital perturbation force, and the position and velocity of the satellite at any time can be calculated.
## 2 Experimental Section
### SERS Measurements  
Near-infrared Raman spectra were recorded using the Echelle-type spectrometer RamanFlex 400 (PerkinElmer, Inc., Waltham, MA, USA) equipped with a thermoelectrically cooled (\(-50\)\({}^{\circ}\)C) CCD camera and fibre optic cable for excitation and collection of the Raman spectra. The 785 nm beam of the diode laser was used as the excitation source; at this wavelength, the Cu SERS substrate is active and there is negligible fluorescence. The 180\({}^{\circ}\) scattering geometry was employed. The laser power at the sample was restricted to 30 mW for SERS studies, and the beam was focused to a 200 \(\upmu\)m diameter spot on the electrode. The integration time was 10 s. Polarisation of the light incident normally onto the electrode was linear, while the polarization of the detected signal was not discriminated in polarisation.  
Each spectrum was recorded by accumulation of 30 or 50 scans, yielding the total integration times of 300 and 500 s. Spectroelectrochemical measurements were carried out in a cylinder shaped three electrode moving cell, arranged with a nanotextured black-CuO or black-CuO/Pd as a working electrode (WE), a platinum wire as a counter electrode (CE), and an RHE reference electrode (RE). The potential of the working electrode was changed stepwise, starting from the open-circuit potential (0.7 V) and going towards more negative \(E\) values to 0 V and \(-0.5\) V for the black-CuO/Pd and black-CuO, respectively. During the experiment, the solution was continuously bubbled by pure N\({}_{2}\) gas flow to remove dissolved oxygen. The WE was placed at approximately a 3 mm distance from the cell window. In order to reduce photo and thermal effects, the cell together with the electrodes was moved linearly with respect to the laser beam with a rate of about 15-25 mm/s [23; 24]. The Raman frequencies were calibrated using the polystyrene standard (ASTM E 1840) spectrum. Intensities were calibrated by the NIST intensity standard (SRM 2241). Experiments were also carried out in heavy water D\({}_{2}\)O to distinguish ionic hydrogen species by a Raman shift, which is mass dependent; the D\({}_{2}\)O related peaks appeared at smaller wavenumbers in Raman scattering [25].
# Loop-Type Field Probe to Measure Human Body Exposure to 5G Millimeter-Wave Base Stations
## 3 Probe Fabrication and Measurement
### Measurement Results  
The PD was measured at 28 GHz using the fabricated probe. Prior to the PD measurement, the coefficient product \(G_{\text{LNA}}C_{PD}\) in (2) was measured using the probe with the loop antenna removed. The far-field measurement was then conducted to measure the coefficient product \(C_{E}C_{H}\) in (2). The coefficient product was measured using the horn antenna without the slot array. As shown in Figure 5, the front panel includes the slots. This front panel was temporally removed for the coefficient measurement. The panel removal expanded the far field region around the antenna [11]; hence, the coefficient measurement  
Figure 5: Configuration of the measurement setup.  
Figure 6: Measurement setup for fabricated 5G mmWave field probe with columns and rails to move the probe.  
could be conducted in the far-field region. Such a measurement generalizes the measured coefficient because the far field has a constant wave impedance and no difference in the phases of the E- and H-fields. The probe was placed about 1 m away from the horn antenna without the slot array. After driving the horn antenna with the signal generator in Figure 5, the output voltage \(V_{PD}\) was measured at the probe output. In (2), the PD corresponding to \(E_{x,RMS}H_{y,RMS}\) was calculated from the simulation on the horn antenna in the free space. (\(\theta_{E}-\theta_{H}\)) in (2) is equal to zero according to the measurement and the simulation in the far-field region. Finally, the coefficient product \(C_{E}C_{H}\) in (2) was calculated using the measured \(G_{LNA}C_{PD}\) and \(V_{PD}\) along with the simulated \(E_{x,RMS}H_{y,RMS}\).  
After calculating all the coefficients, the EMF was generated using the horn antenna with the slot array installed and the output voltage \(V_{PD}\) was measured at each distance. The PD was then calculated using (2). Figure 7 shows the measured PD for the distance increasing from the horn antenna with the slot array. The simulated PD is also shown in the same figure for the comparison with the measured PD. The PD was simulated in the free space at the same distance from the horn antenna with the slot array. As shown in Figure 7, the single probe measures PD close to the simulated one with the maximum error of about 3 dB. For the increasing distance, the decreasing rate is almost the same in the simulation and the measurement. Figure 7 shows the measured PD after rotating the probe by \(90^{\circ}\) on the axis that passes through the probe center in the longitudinal direction. As shown in Figure 7, the proposed probe has a polarization discrimination over 10 dB.
## 2 Preliminaries and Problem Formulation
### Leader-Follower Formation Control Problem  
In this section, we consider such an MAS composed of \(N+1\) agents. There is one leader agent and \(\mathrm{N}\) follower agents in the system. The leader and the followers are represented by the \(\{0\}\) and \(\{1,2,3,\cdots N\}\), respectively. The dynamics of the follower agent can be described by:  
\[\dot{x}_{i}(t)=\mathrm{A}x_{i}(t)+\mathrm{B}u_{i}(t),i=1,2,3,\cdots N \tag{1}\]  
where \(\mathrm{A}\in\mathbb{R}^{n\times n}\) and \(\mathrm{B}\in\mathbb{R}^{n\times m}\) are the given matrices with rank(B) = m, and \(\mathrm{n}\geq\mathrm{m}\), \(x_{i}(t)\in\mathbb{R}^{n}\) is the state of the \(i\)th follower agent and \(u_{i}(t)\in\mathbb{R}^{m}\) is the corresponding input.  
The leader's dynamics are described by:  
\[\dot{x}_{0}(t)=\mathrm{A}x_{0}(t)+\mathrm{B}u_{0}(t) \tag{2}\]  
where \(x_{0}(t)\in\mathbb{R}^{n}\) and \(u_{i}(t)\in\mathbb{R}^{n}\) denote the state and the control input of the leader, respectively.  
**Assumption 1**.: _The pair (\(A\), \(B\)) is stabilizable._  
**Definition 1**.: _The desired time-varying formation configuration is specified by the vector \(h(t)=\big{[}{h_{1}}^{T}\ (t),{h_{2}}^{T}\ (t),{h_{3}}^{T}\ (t),\cdots{h_{N}}^{T} (t)\big{]}^{T}\in\mathbb{R}^{4N}\), with \(h_{i}(t)=\big{[}{h_{ix}},{h_{ixix}},{h_{iy}},{h_{iyy}}\big{]}^{T}\) as a bounded piecewise differentiable function; \({h_{ix}},\ {h_{ixx}},\ {h_{iy}}\) and \({h_{iyy}}\) are the desired position and velocity relativeoffsets in X and Y directions to the leader agent. For any given initial bounded states, if the following formula is satisfied:_  
\[\lim_{t\rightarrow\infty}(x_{i}(t)-h_{i}(t)-x_{0}(t))=0,i=1,2,3,\cdots,N \tag{3}\]  
_then we say the multi-agent system has achieved the expected time-varying formation tracking._  
**Remark 1**.: _The time-varying formation is designed by the vector h(t). If the vector h(t) = 0, then the formation tracking problem become the leader-following consensus problem; in other words, the consensus problem is just a special case of the formation problem._  
**Remark 2**.: _When the MAS has achieved the expected time-varying formation tracking, the position of the leader may lie inside or outside the formation, which is specified by the h(t). For example, when \(\lim\limits_{t\rightarrow\infty}\sum_{i=1}^{N}h_{i}(t)=0\), according to the formula (3), we can obtain \(\lim\limits_{t\rightarrow\infty}\frac{\sum_{i=1}^{N}x_{i}(t)}{N}-x_{0}(t)=0\), which means that the position of the leader is in the center of the time-varying formation._
# Elemental Abundances of Moon Samples Based on Statistical Distributions of Analytical Data
## 2 Samples and Analytical Methods  
### Samples  
The exploration and research of the Moon have been launched since 1957 [25]. In 1969, the Apollo 11 mission realized a manned moon landing and retrieved moon samples, which shifted the research on the Moon from theoretical conception to practical analysis [26; 27; 28; 29; 30; 31]. Then in 1970, Luna 16 realized an unmanned moon landing sampling [32; 33; 34]. In 2020, Chang'E-5 (CE5) brought back moon samples [35]. So far, humans have retrieved moon samples 10 times, including 6 Apollo missions, 3 Luna missions, and the Chang'E-5 mission (Figure 1). The uppermost few meters of the Moon's crust, from which all the moon samples came, is a layer of loose, highly porous regolith or moon soil [24]. Samples from the Apollo missions contain lumps of rocks, breccias, surface soils, and core soils. The Apollo missions brought back a total of 380.95 kg of samples (Table 2), of which rock and breccia samples were picked up by the astronauts on the surface, and soil samples were mainly extracted by the surface scoop and deep drilling core, with a depth of 2 to 3 m. The Luna missions brought back a total of 0.301 kg of samples (Table 2). The sampling method was mechanical core drilling with a depth of 0.2 to 1.6 m. As for the CE5 mission, it brought back a total of 1.731 kg of lunar samples [1; 2; 3; 5; 6; 7]. The sampling methods were mechanical shovel sampling and drilling sampling, but the drilling samples have not been analyzed or reported until now.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] [23]** & **[PERSON] and [PERSON] [24]** \\ \hline
**Samples** & **Apollo** & **Lunar** & **Apollo 16** & **Mare** & **Highland** \\  & **Missions** & **Highland** & **Soils** & **Basalts** & **Regolith** \\ \hline Counts of & \textless{}30 & 10 & \textless{}50 & \textless{}220 & 10 \\ samples/records & \textless{}11 & 8 & 8 & 8 & 9 \\ oxides & 14 & 16 & 12 & 10 & 12 \\ Counts of rare & 3 & 14 & 8 & 5 & 9 \\ earth elements & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample counts and element counts of analyzed moon samples.