esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Automatic Tracking Method of Weak Acoustic Targets within Jamming Environment
### Two-Dimensional Template Matching  
This section uses 2D template matching [2] to estimate the DOA of the interested targets. The impulse response and transfer function of the matched filter are assumed to be \(\hbar(\theta,t)\) and \(\mathrm{H}(u,v)\), respectively. As a result, the filter's output may be stated as  
\[y(\theta,t)=(I(\theta,t)+\bar{n}(\theta,t))*\hbar(\theta,t) \tag{38}\]  
where \(y(\theta,t)\) represents the output of the filter, \(I(\theta,t)\) represents the baseband signal, and \(\bar{n}(\theta,t)\) represents the additive noise in the image level. The power spectral density of \(I(\theta,t)\) and image-level noise \(\bar{n}(\theta,t)\) are denoted as  
\[\mathds{F}(u,v)=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}I(\theta,t)e^{- j(u\theta+vt)}d\theta dt \tag{39}\]  
and  
\[\mathds{N}(u,v)=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}\bar{n}(\theta, t)e^{-j(u\theta+vt)}d\theta dt \tag{40}\]  
where \(u\) and \(v\) correspond to the frequencies of the BTR within the different dimensions. With inverse Fourier transformation, the instantaneous output of the filter is rewritten as  
\[\begin{split} y(\theta,t)=\int_{-\infty}^{\infty}\int_{-\infty}^ {\infty}[\mathrm{H}(u,v)(\mathds{F}(u,v)+\\ \mathds{N}(u,v))e^{j(u\theta+vt)}]dudv\end{split} \tag{41}\]  
Now we have the instantaneous output signal-to-noise ratio (SNR) of the filter as  
\[SNR(\theta,t)=\frac{[\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}\mathds{ H}(u,v)\mathds{F}(u,v)e^{j(u\theta+vt)}dudv]^{2}}{[\int_{-\infty}^{\infty}\int_{- \infty}^{\infty}\mathds{H}(u,v)\mathds{N}(u,v)e^{j(u\theta+vt)}dudv]^{2}} \tag{42}\]  
Suppose \(\bar{n}(\theta,t)\) is the Gaussian white noise with the power density \(N_{o}/2\), Equation (42) can be simplified to  
\[SNR(\theta,t)=\frac{2[\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}\mathds{ H}(u,v)\mathds{F}(u,v)e^{j(u\theta+vt)}dudv]^{2}}{N_{o}\int_{-\infty}^{\infty} \int_{-\infty}^{\infty}|\mathds{H}(u,v)|^{2}dudv} \tag{43}\]The optimal matched filter can be obtained by maximizing Equation (43). According to the Cauchy-Bunyakovsky-Schwarz inequality, we have  
\[\begin{split}[\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}\mathds{ H}(u,v)\mathds{F}(u,v)e^{i(u\theta+vt)}dudv]^{2}\\ \leq\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}|\mathds{H}(u, v)|^{2}dudv\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}|\mathds{F}(u,v)|^{2}dudv \end{split} \tag{44}\]  
Hence, the optimal output SNR is  
\[SNR_{opt}=\frac{2}{N_{o}}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}| \mathds{F}(u,v)|^{2}dudv \tag{45}\]  
The optimal SNR is achieved when  
\[\mathds{H}(u,v)=C[\mathds{F}(u,v)e^{i(u\theta+vt)}]^{*} \tag{46}\]
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 4 Experimental Evaluation
### Service Robotics Simulation  
Figure 3 shows the four different environments used in the experiments, featuring different size, i.e., different number of cells: (i) a small two rooms environment, where the total area (\(N=16\)) is split in half by a wall with a 2-cells opening in the center (split16); (ii) an empty environment of size \(N=32\) (empty32); (iii) a facility-like environment of size \(N=32\), with several rooms connected by hallways and one big room that serves as a warehouse (facility); and (iv) a rough reproduction of the floor of the Automatic Coordination of Teams Lab (Robotics and Autonomous Systems Center, University of South California) of size \(N=64\) (lab). For the purpose of the experiments, we consider each cell \(c_{j}\) in the grid world to be either free or occupied. A free cell represents empty space where a robot can move and a task appear. For the empty32 environment, task locations are uniformly distributed. In the split16, facility and lab scenarios, potential task locations are specified such that they do not block narrow passages or corridors and only appear inside rooms (see Figure 3). An occupied cell is either an empty cell already taken by a robot--and does not allow the presence of a second robot--or represents a wall or any obstacle not allowing the presence of robots and tasks. Walls and obstacles have an impact on the communication range, following the model introduced in Equations (1) and (2). Figure 4 shows how the environment configuration impacts on the communication range for the facility and lab environments.  
As already introduced in Section 3.2.1, we consider \(R\) identical holonomic robots. At each time-step the set of robots is randomly shuffled to remove any bias introduced by ordered sequential decisions. This allows to avoid ties since we use the reshuffled sequential order of robots to prioritize the decisions. At each step, a robot is asked to perform one of the available actions: wait at its current position, move to an adjacent free cell, or work on a task located in the cell it is occupying. Robot actions are performed after the decision phase that constitutes the main contribution of this work and is summarized in Algorithm 1.
## 5 Discussion
### Paleowater Depth  
A considerable amount of research indicates that variations in the content of Sr and Ba in sedimentary environments can serve as indicators of ancient salinity fluctuations [26]. The Sr/Ba ratio to some extent reflects the depth of the sedimentary water body. Generally, the Sr/Ba ratio decreases from the coast to the deep sea. Additionally, Zr in sedimentary environments is a typical lithophilic element primarily transported mechanically and deposited in offshore areas. However, the distribution of Zr in sedimentary rocks is influenced by Al elements. Thus, the Zr/Al ratio can represent the proximal terrestrial components and variations in water depth transported over short distances [23,27]. Generally, a smaller Zr/Al ratio indicates a greater distance from the shore and deeper waters. By selecting Sr/Ba and Zr/Al values sensitive to ancient salinity changes for spot discoveries (Figure 8), it was found that the depth of the sedimentary water body during the deposition period of the Qiongzhusi formation corresponds closely to ancient salinity fluctuations. During the deposition period of the Qiongzhusi formation, significant marine transgression occurred, which led to the alleviation of the previously restrictive sedimentary conditions in southwestern Sichuan, resulting in a more favorable environment for the deposition of organic-rich shales. This led to deepening water and decreasing salinity, gradually forming a sedimentary environment dominated by shallow to deep water continental shelves. Overall, the shale of the Qiongzhusi formation exhibits a trend of decreasing water depth from bottom to top.  
Figure 7: REE vs. La/Yb binary diagram of shale provenance in the Qiongzhusi formation in the southern Sichuan Basin (Based on the modification by [PERSON], 1978).
## 2 Samples and Experimental Methods  
### Samples  
Qinshui Basin, as one of China's most important energy basins, is a syncinmore located in Shanxi Province with large reserves of natural gas and coal. Shanxi Formation and Taiyuan Formation are the major exploration targets for the exploitation of coal and the co-exploration of coal unconventional gases in Qinshui Basin [5; 9; 11; 12; 22; 34]. Carboniferous Shanxi Formation is a set of coal-bearing strata originating in continental-oceanic interactive facies in the study area [22]. The deposits of Shanxi Formation are formed in the delta plain and delta front environment. As revealed by previous studies, Shanxi Formation is a major coal-bearing stratum developed in a regional marine regression depositional environment setting of the epicontinental sea with thickness between 30-70 m and finer particle size upward consisting of conglomerate, sandstone, sandy mudstone, mudstone, and coal [42].  
The samples used in the study were taken from Y-4 well in the Yangzhuang exploration area of the Xishan coalfield (Figure 2). In total, 15 representative core samples, including 5 coal samples, 5 shale samples, and 5 tight sandstone samples, were selected from Y-4 well. The basic information of samples is shown in Table 1. The material compositions of coal samples differ greatly with shale and sandstone with OM content higher than 90%. All coal samples are vitrinite-rich and belong to high-rank coal (with the mean maximum reflectance of vitrinite (R\({}_{\text{o,max}}\)) \(\geq\) 2.50). The average volume fractions (Vol, %) of vitrinite and inertinite were 91.78% and 5.30%, respectively. The average content of minerals ranges between 0.5-6.4%. Kaolinite and quartz minerals were identified using FESEM and EDS. The material compositions of shale samples are mainly clay minerals and quartz with average proportions of 33.66% and 50.94%, respectively. The TOC content of shale samples ranges from 1.65% to 2.38%, with an average value of 2.004%. Some fragment fossils of plants can be found on the cross sections of shale samples. In terms of mineral components, the heterogeneity of material components in shale samples is relatively strong due to the differences in sedimentary environment and sedimentary conditions. Samples T1-T5 belong to tight sandstone reservoir according to porosity (4.76-8.06%) and air permeability data (0.08-0.25 \(\times\) 10\({}^{-3}\) \(\upmu\)m\({}^{2}\)). Among them, sample T1 is argillaceous siltstone, T2-T4 are fine-grained sandstone, and T5 is medium-grained sandstone.  
Figure 2: The study area of the Yangzhuang exploration area, the Xishan coalfield, and sampling locations of the Transitional Upper Carboniferous Shanxi Formation in Y-4 well.  
in strata was discussed based on the comprehensive analysis of qualitative and quantitative experimental results.
## 3 Mathematical Modeling
### Mathematical Modeling of the Variable Motor Deflection Angle Control System  
The variable motor deflection control system consists of an electro-hydraulic servo valve and hydraulic cylinder. First, the current control signal of the electro-hydraulic servo valve is given to change the direction of the valve. Then, the flow of hydraulic fluid into the hydraulic cylinder changes, pushing the piston rod of the hydraulic cylinder to displace it. Finally, the displacement of the piston rod drives the variable motor oscillation angle to change the size of the displacement, thus realizing the motor-speed control. The control schematic is presented in Figure 2.  
Figure 2: Control system schematic: 1—electro-hydraulic servo valve; 2—hydraulic cylinder; 3—variable motor; 4—input signal.  
Model the electro-hydraulic servo valve as a proportional element:  
\[i=K_{p}u \tag{6}\]  
\[X_{V}=K_{sv}i \tag{7}\]  
where \(i\) is the current, \(K_{p}\) is the servo amplifier gain, \(u\) is the output voltage, \(X_{V}\) is the displacement of the valve core, and \(K_{SV}\) is the gain.  
Electro-hydraulic servo valve flow equation:  
\[Q_{L}=K_{q}X_{V}-K_{c}p_{L} \tag{8}\]  
where \(Q_{L}\) is the servo valve flow, \(K_{c}\) is the servo valve pressure gain, and \(p_{L}\) is the load pressure.  
Combining Equations (6)-(8) yields the following:  
\[Q_{L}=K_{q}K_{p}K_{SV}u-K_{c}p_{L} \tag{9}\]  
Perform the Laplace transformation on Equation (9):  
\[Q_{L}=K_{q}K_{p}K_{SV}u(s)-K_{c}P_{L}(s) \tag{10}\]  
Hydraulic cylinder flow equation:  
\[Q_{L}=A_{p}\frac{dX_{p}}{dt}+C_{tp}p_{L}+\frac{V_{l}}{4\beta_{e}}\cdot\frac{ dp_{L}}{dt} \tag{11}\]  
where \(A_{p}\) is the piston effective area, \(X_{p}\) is the piston displacement, \(C_{tp}\) is the total leakage factor, and \(V_{l}\) is the total compression volume.  
Perform the Laplace transformation on Equation (11):  
\[Q_{L}=A_{p}sX_{p}(s)+C_{tp}P_{L}(s)+\frac{V_{l}P_{L}(s)}{4\beta_{e}}s \tag{12}\]  
Hydraulic cylinder force balance equation:  
\[A_{p}p_{L}=m_{i}\frac{d^{2}X_{p}}{dt^{2}}+B_{p}\frac{dX_{p}}{dt}+KX_{p}+F_{L} \tag{13}\]  
where \(m_{i}\) is the total mass of the piston, \(B_{p}\) is the viscous damping factor, \(K\) is the load spring stiffness, and \(F_{L}\) is the piston load force.  
Perform the Laplace transformation on Equation (13):  
\[A_{p}P_{L}(s)=m_{i}s^{2}X_{p}(s)+B_{p}sX_{p}(s)+KX_{p}(s)+F_{L}(s) \tag{14}\]  
Combining Equations (10), (12), and (14) yields the following:  
\[X_{p}(s)=\frac{K_{qpSV}A_{p}u(s)-K_{ce}\Big{(}1+\frac{V_{l}}{4\beta_{e}K_{ce}} s\Big{)}F_{L}(s)}{\frac{m_{i}V_{l}}{4\beta_{e}}s^{3}+\Big{(}m_{i}K_{ce}+\frac{B_{p}V_{ l}}{4\beta_{e}}\Big{)}s^{2}+\Big{(}A_{p}^{2}+B_{p}K_{ce}+\frac{KV_{l}}{4\beta_{e}} \Big{)}s+KK_{ce}} \tag{15}\]  
where \(K_{ce}\) is the total flow pressure factor, \(K_{qpSV}=K_{q}K_{p}K_{SV}\).
# Haskap Juicing Method Effects on Haskap Juice Quality
## 4 Conclusions  
This study showed that haskap lab-scale juicing methods influence most juice qualities, from basic physicochemical characteristics to nutritional values. In general, the metrics differed among all three methods with centrifugal juicer juice have the highest values of multiple compounds, the wine press with intermediate values, and the lowest values in the steam juices.  
In terms of sugar and acid content, the differences among the juices obtained from the three methods varied by assay. The centrifugal juicer produced haskap juice with a sugar content 1.7 times higher than juice produced by steam juicing across SCC and specific sugar types. Juice made with centrifugal juicer had higher SCC than the pressed juice, but glucose and fructose levels were similar between these two methods. Similarly, the effects of juicing methods on juice acids and acidity varied among metrics. All methods differed in pH (centrifugal < press < steam). Steam juicing reduced levels of malic acid but levels of the most abundant acid (citric) were similar among juice methods.  
In terms of nutraceutical benefits, centrifugal juicers seemed to extract and/ or retain more beneficial compounds. The DPPH assay of antioxidant capacity provided some of the most stark effects of juice methods with the juice for the centrifugal juicer have four times the antioxidant capacity compared to steam juicing, and 50% greater antioxidant capacity than press juicing. In other measures of \"health-promoting\" compounds (i.e., total phenolics, ascorbic acid, flavonoids, and anthocyanins), juice made with the centrifugal juicer also had better performances compared to steam-juiced juice.  
This study will provide general information for haskap fruit applications. The results suggest that the centrifugal juicer might be the most effective method to extract juice from haskap berries without consideration for the cloud value and sensory evaluations. Further aroma and taste evaluations of haskap juice are necessary to provide more information for processors.  
Methodology and Investigation: Z.W. and A.S.; Original Writing: Z.W.; Writing--Review and Editing: Z.W., A.S., Z.M. and B.J.; Berry Harvesting: B.J.; Berry Processing: Z.W., A.S., B.J. and A.K. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Montana Department of Agriculture Specialty Crop Block Grant, grant number 23 SC00309. Project title: From Traditional to Unique Wines-Strategies to improve Montana's Fermentation of Novel, Local Fruits.  
Not applicable.  
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors would like to thank the farm manager [PERSON] and the summer crew from Western Agricultural Research Center. Without their help, the research cannot be performed successfully.  
The authors declare no conflict of interest.
# Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography
## 2 System Structure and Working Principle  
### How EOG Works  
Electrooculography (EOG) is a technique that detects eye movements by measuring the electrical potential between the cornea and the retina. The eye assumes the role of the electrode between the cornea (positive electrode) and the retina (negative electrode). Any rotation of the eye in any direction causes a change in the electrical signal of the eye [10]. Previous research has established that the amplitude of the EOG signal typically varies between 50 uv and 3500 uv, with a frequency of 0-100 Hz. The amplitude of EOG and the angle of eye movement showed a similar linear trend when the eye movement direction is in the range of \(\pm\)50 degrees horizontally and \(\pm\)30 degrees vertically [11]. Figures 1 and 2, respectively, show the schematic diagram of the change in the amplitude of EOG caused by right eye movement and left eye movement.  
As shown in Figures 1 and 2, when the eyeball turns to the right, the voltage of the EOG changes in the positive quadrant, conversely, it changes in the negative quadrant.
# Measuring the Centrality of DNS Infrastructure in the Wild
## 5 Resolver Centrality Analysis
### Centrality of iRDNS  
**Finding 1: More than 90% of forwarding resolvers are ultimately backed by fewer than 5% (4071) of indirect resolvers, which are operated by about 300 IP providers.** Previous studies have revealed that the imbalance between the numbers of FDNSes and iRDNSes [12; 13], where 90% of Internet users are served by 10 K resolvers from the users' perspective [2]. Compared with previous studies, our finding uncovers the client-side DNS infrastructure is much more centralized in terms of forwarding relationships between different types of resolvers. From Table 3, we observe that the number of the FDNSes is substantially larger than that of the iRDNSes by a wide margin, approximately 50 times greater than the number of iRDNSes. Despite the vast numbers of FDNSes on the Internet, all DNS queries will be forwarded to and processed by 50 K iRDNSes. Given the dynamics of iRDNSes, we obtain the union of all the iRDNSes among the ten measurement rounds in Table 3 and obtain a total of 80 K iRDNSes. Figure 6 shows a CDF of the number of resolvers that serve the most FDNSes. More than 90% of all FDNSes are ultimately backed by less than 5% (4071) of indirect resolvers, where more than 70% of all FDNSes end up being served by only 1.2% (1K) iRDNSes. The 4071 iRDNSes are operated by about 300 IP providers, with the top 10 providers operating 72.3% of these iRDNSes. This indicates that indirect resolvers are highly concentrated among a small number of IP providers. They can be classified into two types: public DNS providers and localized Internet service providers. Google is the largest provider owing to the prevalence of Google Public DNS. Apart from public DNS providers such as Google, Cloudflare, and OpenDNS, Internet service providers, such as PT Telekomunikasi Indonesia, CHINA UNICOM, Turk Telekom, and Chinanet, also operate considerable top indirect resolvers. Surprisingly, the top 1 iRDNS is an iRDNS of Iran Telecommunication Company, serving more than 1.04 million FDNSes. This does not necessarily suggest that it is the most widely used iRDNS, but it might be aligned with the censorship policy in Iran [36].
## 4 IoT Systems in the Pre-Disaster Stage
### Sensors  
Sensors used in the pre-disaster stage mainly collect environmental data. Accelerometers are usually used to detect earthquakes [38; 39]. Except for accelerometers, more sensors are used to detect landslides, such as inertial sensors, bar extensometers, and borehole inclinometers. More publications focus on flood monitoring. Floods may occur more frequently in cities. One reason is that urban drainage systems often become saturated due to prolonged and intense rainfall [40]. Regarding flood monitoring systems, scholars use more sensors, such as rain gauges [40; 41], water level sensors [42; 43; 44; 45; 46; 47], water pressure sensors [41; 45; 47], cameras [41; 48; 49], soil moisture sensors [42], weather sensors [42], drones with drones [42], water presence sensors [44; 50], temperature sensors [50], and a triaxial accelerometer [50]. The sensors are usually powered by solar batteries [41; 50]. To save the energy cost of the sensors, [PERSON] et al. [51] introduced a model with a harmony search algorithm and improved hybrid Particle Swarm Optimization to select cluster heads. Based on Particle Swarm Optimization, they developed a multi-hop routing system with enhanced tree encoding and a modified data packet format. The single computer boards are the Raspberry Pi [43; 48] and Arduino [43; 44]. We will further discuss the sensors used for different disaster types in the following content.
## 2 Materials and Methods
### Numerical Particle Model
This process is considered in the model through the following reaction:  
\[\text{Inorganic K (K}^{+})\ \rightarrow\ \phi_{1}\text{KCl}+\phi_{2}\text{K}_{2} \text{SO}_{4}+\phi_{3}\text{K}_{2}\text{CO}_{3}\] (R1)  
where the constant \(\phi_{1}\) is calculated based on the Cl content by assuming that Cl in the biomass is mainly bonded to potassium [9]. The potassium in excess amount to stoichiometric Cl can be bonded to S, forming K\({}_{2}\)SO\({}_{4}\), or bonded to the carbonate group as K\({}_{2}\)CO\({}_{3}\). The organic potassium is transformed to char-bonded potassium as the particle temperature increases. A part of the organic potassium is transformed into carboxyl groups, releasing atomic potassium into the gas phase [7]. The transformation and decomposition of the organic potassium is considered in the model through the following reactions:  
\[\text{Organic K (R-COOK) }\rightarrow\ \text{Char-K}\] (R2)  
\[\text{Organic K (R-COOK) }\rightarrow\ \text{R-COOH (carboxyl groups) }+\text{CO}_{2}+\text{K(g)}\] (R3)  
It was suggested by [PERSON] et al. [11] that an ion-exchange reaction with chain-bonded carboxylic groups and KCl occurs, resulting in alkaline carboxylate and release of hydrogen chloride (HCl), as presented in the following reaction,  
\[\text{R-COOH (carboxyl group)}+\text{KCl }\leftrightarrow\ \text{R-COOK}+ \alpha_{1}\text{HCl}+\alpha_{2}\text{CH}_{3}\text{Cl}\] (R4)  
Evaporation of KCl is the main source of potassium and chlorine release at relatively low temperature, 970-1070 K [7; 9; 36]. This is considered in the model through reaction (R5),  
\[\text{KCl(s) }\rightarrow\ \text{KCl(g)}\] (R5)  
At higher temperatures, potassium can be released through evaporation of K\({}_{2}\)SO\({}_{4}\) or K\({}_{2}\)CO\({}_{3}\) or reaction of these compounds with the gas phase H\({}_{2}\)O. A four step reaction scheme is considered in the model for high temperature release of potassium during the char combustion, as presented in the following reactions,  
\[\text{K}_{2}\text{SO}_{4}(\text{s})\ \rightarrow\ \text{K}_{2}\text{SO}_{4}( \text{g})\] (R6)  
\[\text{K}_{2}\text{SO}_{4}(\text{s})+\text{H}_{2}\text{O }\rightarrow\ 2\text{KOH}+\text{SO}_{2}+0.5\text{O}_{2}\] (R7)  
\[\text{K}_{2}\text{CO}_{3}(\text{s})\ \rightarrow\ 2\text{K(g)}+\text{CO}_{2}+0.5 \text{O}_{2}\] (R8)  
\[\text{K}_{2}\text{CO}_{3}(\text{s})+\text{H}_{2}\text{O }\rightarrow\ 2\text{KOH}+\text{CO}_{2}\] (R9)  
The final part of the model is related to the sulphur release. Not all sulphur in the biomass structure is bonded to potassium. A large portion of sulphur may be found in the free ion form, S\({}^{2-}\)[9]. A two-step reaction scheme is assumed for the release of sulphur, as follows,  
\[\text{Inorganic K (K}^{+})\ \rightarrow\ \phi_{1}\text{KCl}+\phi_{2}\text{K}_{2} \text{SO}_{4}+\phi_{3}\text{K}_{2}\text{CO}_{3}\]0 (R10)The set of reactions, as presented above, provide the sub-model for the release of potassium. The kinetic rate constants and the complete set of assumptions and verification of the model can be found in [30].