esa-sceva/satcom-chunk-collection huggingface dataset:
# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
## 5 Simulations and Discussion
### Performance Analysis of Gurobi and AC-ETO in Different Network Scenarios  
We further study the performance of the proposed algorithm AC-ETO under different network scales and compare the results with the solutions of P1 obtained by Gurobi. As shown in Figure 7a, the deployment cost obtained by Gurobi is slightly smaller than that by AC-ETO in Scenarios 5 to 10. Figure 7b,d compare the energy consumption and network lifetime performance obtained by Gurobi and by AC-ETO under different scenarios. Similarly, it can be observed that Gurobi slightly outperform AC-ETO in all these metrics. In Figure 7c, when the network is scaled up with a larger number of nodes, the time complexity of Gurobi increases drastically, while the running time of AC-ETO does not vary much. As an example, in Scenario 9, the energy consumption by AC-ETO is 0.69E-4% greater than that by Gurobi, and the deployment cost by AC-ETO is 9.16% higher than that by Gurobi, but the time complexity of Gurobi is 1000 times higher than that of AC-ETO. As the network size increases, Gurobi cannot obtain the results in the last scenario--i.e., scenario 11--within the set time limit of 300,000 s, yet the proposed AC-ETO algorithm obtains the results efficiently. Thus, the AC-ETO is efficient in dealing with those large-scale network scenarios at the cost of a slight reduction of the optimality.  
Figure 6: Comparison of the exhaustive search, Gurobi and the AC-ETO in terms of the optimization objective.
# Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography
## 4 Method
### Eye Fatigue Detection Algorithm  
The CART classifier, known as the Classification and Regression Tree, is the advanced Decision Tree that has favorable performance in both classification and regression tasks. In contrast to traditional ID3 that uses information gain as the basis for feature selecting,  
Figure 11: Eye movement signal recognition (red+ indicates right movement, red* indicates left movement).  
the CART uses the Gini coefficient to select features. The Gini coefficient represents the impurity of the model, the smaller Gini coefficient means lower impurity, and at the same time, the better features. The Gini coefficient is defined as:  
\[\begin{split} Gini(D)&=\sum\limits_{i=1}^{n}p(x_{i} )*(1-p(x_{i}))\\ &=1-\sum\limits_{i=1}^{n}p(x_{i})^{2}\end{split} \tag{6}\]  
where \(p(x_{i})\) is the probability of \(x_{i}\) and \(n\) represents the number of classifications. The Gini coefficient for sample \(D\) with the \(A\) attribute is defined as:  
\[GiniIndex(D\mid A=a)=\frac{|D_{1}|}{|D|}Gini(D_{1})+\frac{|D_{2}|}{|D|}Gini(D_{2}) \tag{7}\]  
The number of sample dataset \(D\) is \(|D|\) and it is divided into two parts: \(D_{1}\) and \(D_{2}\), according to whether feature A takes the value of a. Consequently, the CART builds up binomial trees instead of multinomial trees. According to the Gini index minimum criterion, the most appropriate feature \(AK\) in one partition is defined as:  
\[Ak=(D1/D2*Gini(D1)+D2/D1*Gini(D2))Ak \tag{8}\]  
where \(A\) represents all feature sets and the data set \(D\) is divided into \(D_{1}\) and \(D_{2}\) based on the value of \(AK\).  
The five features extracted in Section 4.2 from a feature dataset and the data set is divided into 70% training data and 30% test data by the leave-out method, and to maintain the consistency data distribution, a stratified sampling is used. The training data is then further divided into the training set and validation set. After the partition, the data set is classified by the CART algorithm in three steps. First, the tree is generated based on training data according to the Gini index minimization criterion, then pruned based on the validation set to obtain the best subtree. To improve the generalization performance of the tree, the post-pruning method is adopted. During the post-pruning process, the non-leaf nodes of the original tree are examined from the bottom up. Then, the subtree corresponding to these non-leaf nodes is replaced with the new subtree which corresponds to leaf nodes. Finally, the best subtree used to evaluate the model performance based on the test dataset. If the accuracy of the new classification tree is improved compared to the original one, the new classification tree is divided again, otherwise, the original tree is performed, until all non-leaf nodes are traversed.
## 5 Common Systems
### Timing and Synchronization
#### 5.1.2 Optical Master Oscillator and Signal Generation  
The optical master oscillator (OMO) Origami-15 from OneFive GmbH has two fiber-coupled outputs. One port with 2 mW optical power at 1 550 nm wavelength is used to produce the 142.8 MHz sinusoidal signal for the clock distribution. The other port, with 87 mW average output power, is used to generate 2.9988 GHz for S-band stations in the accelerator and laser synchronization as well as 2.856 GHz for C-band stations (see Figure 23). Both optical ports are connected to an ultra-low phase noise harmonic extraction unit (ULNHEU), where these signals originate from temperature-stabilized photo-detection, harmonic extraction and amplification. The output signals are linked to the corresponding distribution systems via temperature stable RF cables (Andrew Heliax tempered 1/4\", drifts \(-\)6 fs/m/K at 42.5 %rH around 24 \({}^{\circ}\)C for the relevant frequencies and about 0.2 fs/m/%rH at 24 \({}^{\circ}\)C). The absolute jitter of the microwave signal extracted from the free running Origami-15 and measured at the C-band frequency of 5.712 GHz including the noise of the optimized photo-receiver and the post amplifier is as low as 1.6 fs rms (1 kHz-10 MHz).  
Figure 22: Image recorded with a 1 M JUNGFRAU system for a silver behenate powder sample at the LCLS XCS instrument. The image is averaged over 10,000 LCLS pulses.
# Removal of Volatile Solids from Greywater Using Sand Filters
## Materials and Methods
### Determination of specific deposit (VS) distribution in vertical profile of sand filter  
The samples of sand were taken from the first three filter columns (2, 3, 7) and examined after 6 days of the experiment. After 14 days, the next batch of sand samples were taken from columns 1, 4, 8 and examined. The last three columns (5, 6, 9) were examined after 21 days as per VS (kg m\({}^{-3}\)). The vertical profile of the filters were conventionally divided into 16 layers with the aim of determining organic matter content. The first seven layers (depths) of the filters (from which the organic matter was examined) were determined at a distance of about 2.5 cm. The next six layers were determined at a distance of about 5.0 cm. The thinner upper layers were determined according to the expected higher changeability of dry mass in depth. The experiment was conducted at room temperature (about 20 \({}^{\circ}\)C) in a laboratory.  
The maximum specific deposit was specified using column number 10 (Figure 1). It was identified at the time of operation, when the value of the infiltration velocity dropped below the hydraulic load (greywater stagnating on the sand filter surface).  
The simulation, performed on the basis of the \(\lambda\) filter coefficient (Equations 1-3, [41]) using the Iwasaki formulae [1], was conducted for an estimated specific deposit.  
\[\frac{\partial C}{\partial h}=-\lambda C_{0} \tag{1}\]  
\[\frac{\partial\sigma}{\partial t}=-q\cdot\frac{\partial C}{\partial h} \tag{2}\]  
where:  
\(C\): = volatile solids concentration in greywater (VS, kg m\({}^{-3}\));  
\(C_{0}\): = initial volatile solids concentration in greywater (VS, kg m\({}^{-3}\));  
\(h\): = filter depth (m);  
\(\sigma\): = specific deposit of volatile solids (VS, kg m\({}^{-3}\));  
\(q\): = hydraulic load of greywater (m d\({}^{-1}\));  
\(\lambda\): = filter coefficient (1 m\({}^{-1}\));  
\(t\): = time (d).  
\[\lambda=\lambda_{0}\cdot\left(\frac{\sigma_{\max}-\sigma}{\sigma_{\max}}\right) \tag{3}\]  
where:  
\(\sigma_{max}\): = constant (maximum specific deposit of solids) (VS kg m\({}^{-3}\)),  
\(\lambda_{0}\): = initial filter coefficient (1 m\({}^{-1}\)).  
The measurement of the initial filter coefficient \(\lambda_{0}\) was taken on the filter column, considering the length of the sand filter and the difference between inflow and outflow in turbidity removals [42]. The mean value of \(\lambda_{0}\) was 2.6 m\({}^{-1}\). The simulation performed on the basis of the obtained initial filter coefficient (Equations 1-3, [41]) did not give values comparable to measured values for a specific deposit. Additional measurements were taken considering the difference of the initial filter coefficient at depth. The filter factor was proposed for verification using data of VS depth distribution after 21 days of the experiment. The relationship of the initial filter coefficient with the filter depth is described by the exponential function \(\lambda_{0}\)(\(h\)) = 0.3\({}^{-0.5h}\).
## 3 Case study  
### Study River  
The case study site is the Shanxi River watershed situated in the upstream Feiyun River drainage basin in Southern Zhejiang province, China (Figure 1). The total drainage area is approximately 2302 km\({}^{2}\) and consists of 70.60% forest, 21.05% agriculture, 2.40% water bodies, and 1.81% residential area. The Shanxi River watershed has more than 14 main tributary streams. Two reservoirs, the Shanxi Reservoir and Zhaoshandu Reservoir, were formed by constructing the Shanxi Dam and Zhaoshandu Dam, which are located in the Shanxi town and Zhaoshandu town, respectively. Additionally, the Shanxi River watershed is an important drinking water source for 5 million people living in Wenzhou city, Pingyang county, Ruian county, Wencheng county, and Taishun county. Its annual water supply is 1.2 billion m\({}^{3}\). The intake is located 1 km from Zhaoshandu Dam on the left side. Based on the River Continuum Concept developed by [PERSON] et al. (1980), the watershed is attributed to the mountain river type, and river orders have been calculated so that rivers have been grouped into headwaters (orders 1-3) and medium-sized streams (orders 4-6). According to stream order classification, the watershed has been divided into 48 sub-catchments or reaches.  
We have set 38 monitoring and sampling cross-sections along the river and 3 sampling vertical lines in each cross-section. Some sampling points are under water at 1 m, 3 m, 7 m, 10 m, 20 m, 30 m, and on the bottom in vertical lines. Under normal conditions, the flow condition, water quality,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Health Grade** & **Alarm Level and Signal** & **Description** \\ \hline Healthy & No Warning & Natural and good service \\ Fair & Blue Warning & Minor threats and degradation \\ Poor & Yellow Warning & Some threats and degradation \\ Serious & Orange Warning & Many threats and serious degradation \\ Critical & Red Warning & Extensive threats and almost lost \\ \hline \hline \end{tabular}
\end{table}
Table 1: Health grade and alarm level categories in RHP-EWS.  
and biologic variables have been monitored with sensors and sampled once per month since 2008. When urgent or emergency events occur, these data are collected every hour.
# Effect of Characteristics of Different Types of Bauxite Clinker on Adhesion
## 4 Effects of Aggregate on Adhesion  
### Effects of Chemical Components on Adhesion of Asphalt  
From the chemical composition analysis of these six types of calcined bauxite (Table 4), the content range of SiO\({}_{2}\) was 6.74-29.12%. The acid aggregate stone had an SiO\({}_{2}\) content greater than 65%, neutral aggregate stone had a content between 52% and 65%, and alkaline aggregate stone had a content less than 52% [33]. Therefore, calcined bauxite is an alkaline aggregate. Calcined bauxite's dominant mineral components are Fe\({}_{2}\)O\({}_{3}\), Al\({}_{2}\)O\({}_{3}\), and SiO\({}_{2}\), and the main difference between the chemical compositions of different types of calcined bauxite was the Al\({}_{2}\)O\({}_{3}\) content.  
He [34] applied an improved SHAP net adsorption method and carried out adhesion and de-bonding tests on Fe\({}_{2}\)O\({}_{3}\), Al\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), and CaO with three different asphalts. The results are summarized in Figures 5 and 6. With respect to the adsorption and hydrolysis resistance and de-bonding performance, the adhesive abilities of these four oxides were: Al\({}_{2}\)O\({}_{3}\)\(>\) Fe\({}_{2}\)O\({}_{3}\)\(>\) CaO \(>\) SiO\({}_{2}\).  
In the six calcined bauxite samples in this study, Fe\({}_{2}\)O\({}_{3}\) accounted for 1-3% of content, whereas Al\({}_{2}\)O\({}_{3}\) made up 50-90%. This means Al\({}_{2}\)O\({}_{3}\) plays a main role in affecting the adhesion. When compared with calcined bauxite with lower amounts of Al\({}_{2}\)O\({}_{3}\), calcined bauxite containing high amounts of Al\({}_{2}\)O\({}_{3}\) showed a stronger adhesion with the asphalt when exposed to a dry environment. The hydrolysis resistance was in the order: CaO \(>\) Fe\({}_{2}\)O\({}_{3}\) \(>\) Al\({}_{2}\)O\({}_{3}\) \(>\) SiO\({}_{2}\). CaO showed the best adhesion with asphalt when water was present, whereas SiO\({}_{2}\) was worse, and Al\({}_{2}\)O\({}_{3}\) showed the lowest hydrolysis resistance.
## 5 A Prediction Model for Rock Mass Classification Ahead of the Driving Face
### Intelligent Prediction Fundamentals for Rock Mass Classification
#### 5.1.2 Long Short-Term Memory (LSTM)  
LSTM demonstrates superior memory retention in processing time-series data. Figure 6d illustrates the fundamental unit structure of an LSTM neural network [36]. The schematic defines the following: \(X_{t}\): input feature vector at time step \(t\); \(y_{t-1}\): network output at time step \(t-1\); and \(c_{t-1}\): cell state at time step \(t-1\)[37]. The LSTM unit integrates the following three gating mechanisms:  
(1) Forget gate: This governs the retention/discard of historical cell states;  
(2) Input gate: This incorporates dual activation functions for feature normalization;  
(3) Output gate: This regulates current state propagation.  
In this study, LSTM treated specific positions along stratigraphic Longitudinal Deformation Profiles as sequential time steps, effectively capturing spatiotemporal dependencies.
## 3 Method
### Efficient Calculation of the Shortest Distance Function (SDF)  
The SDF function calculation we used is based on library [48], where instead of the SDF, which computes the distance to all the faces of the surface, this value is approximated by the distance to the faces adjacent to the nearest neighbors of the grid point \(\left(\theta_{j},\varphi_{j},R_{i}\right)\) on the \(i\)-th sphere with radius \(R_{i}\).
## 5 Results
### Fair Technology Distribution  
We present now the results associated to the experiments carried out to determine the optimal selection of the number of nodes, per technology, such that the vulnerability of the entire network is minimized subject to a capital expenditure constraint. From Section 5.1, only three different technologies are needed for the networks in Figure 6. In our calculations, we set: Technology 1 with \(\alpha_{1}=6/15\) and \(Q_{1}=1\) [a.u.], Technology 2 with \(\alpha_{2}=5/15\) and \(Q_{2}=2\) [a.u.], and Technology 3 with \(\alpha_{3}=4/15\) and \(Q_{3}=3\) [a.u.]. We comment that the nodes associated to the PSRNGs with the highest vulnerability index are also the most inexpensive ones. Figure 9 shows, for the design process of the Sprint network, the relationship between the maximum CAPEX available and the number of purchased nodes belonging to different PSRNGs. We note that, as the CAPEX amount increases, a better distribution of node technologies can be achieved, according to the technology risk indexes. This behavior comes from the fact that the CAPEX has no direct implication on the objective function.  
Figure 10 shows, for the case of the Sprint network topology, the effect of technologies' risk index on the number of selected nodes per technology. Here the CAPEX constraint has been relaxed, i.e., the upgrade budget to purchase technology was set to infinity. We arbitrarily selected the values for the risk indexes to show how their different combinations affect the number of nodes to be selected for each technology. As expected, for node technologies with larger risk indexes (0.5 and above) the number of specified nodes is smaller than those with lower risk indexes (below 0.5). This behavior is intensified in situations where the risk indexes exhibit large differences among them. In such cases, technologies with smaller risk indexes turn out to be the most selected ones, as depicted in the four  
Figure 8: The distribution of the optimal number of selected technologies for 1000 different realizations of the risk matrices. The number of available technologies was set to 15.  
Figure 7: The optimal number of selected technologies as a function of the available technologies for three different values of risk matrices.  
cases at the right-hand side of Figure 10. On the contrary, when risk indexes exhibit similar values, the number of nodes per technology becomes evenly distributed.
## Materials and Methods
### Extraction and Assay Activity of Geraniol Syntase from Tea Leaves  
The GES extraction and analysis methods were as described elsewhere [19], with a modification. Fresh powdered tea leaf material (1.5 g) was mixed with 5 mL buffer A (pH 7.2, 50 mM HEPES (hydroxyethyl piperazine ethanesulfonic acid) buffer containing 5 mM ascorbic acid, 10 mM MgCl\({}_{2}\), 5 mM dithiothreitol (DTT), 5 mM sodium bisulfite, 10% glycerol, 0.1% Tween 20, and 5 mM ethylene diamine tetraacetic acid). Then, 0.05 g PVPP (polyvinyl pyrrolidone), and 0.5 mL protease inhibitor solution (0.05 g/mL, CompleteTM ULTRA Tablets, Mini, EASYpack Protease Inhibitor Cocktail, Roche, Mannheim, Germany) were added. The mixture was homogenized on ice, completed to 10 mL with buffer A. The homogenate was vortexed for 1 min followed by ultra-sonicated for 10 min at 4 \({}^{\circ}\)C. After centrifuge, the resultant supernatant (5 mL) was desalted over a PD-10 column (SephadexTMG-25M, GE Healthcare, Pittsburgh, PA, USA) equilibrated with buffer B (pH 7.2, 25 mM HEPES buffer containing 100 mM KCl and 10% glycerol). The protein was eluted with 5 mL buffer B, and the eluate was collected and used as the crude GES fraction from tea leaves. The experiments were performed in three replicates.  
To assay the GES activity, 1 mL crude enzyme solution was mixed with geraniol diphosphate (GPP) (5 \(\upmu\)g), and then reacted at 30 \({}^{\circ}\)C for 90 min. Then, volatiles were collected on the SPME (50/30 \(\upmu\)m, DVB/CAR/PDMS, Stableflex (2 cm), Supelco Inc.) at 42 \({}^{\circ}\)C for 30 min. The controls were GPP or enzyme solution alone. The enzyme reaction products were analyzed by GC-MS. Column: SUPELCOWAXTM 10, 30 m \(\times\) 0.25 mm, film thickness 0.25 \(\upmu\)m, Supelco Inc. The carrier gas was helium (flow rate, 1.0 mL/min). The injection port temperature was 230 \({}^{\circ}\)C and operated in splitless mode. The splitless time was 1 min. The initial temperature of oven was 60 \({}^{\circ}\)C and kept for 3 min, then programmed to 240 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, and held at 240 \({}^{\circ}\)C for 10 min. Full scan mode was applied with mass range of _m_/_z_ 40-200.
## 3 Results and Discussion  
### Reference Values for Sugar Content in Breakfast Cereal Samples  
The minimum and maximum sugar contents measured by HPLC for all of the breakfast cereal samples (purchased and industry-provided) are listed in Table 1. According to these results, the minimum content of individual and of total sugars were similar for the store-bought and company-provided samples. However, the maximum sugar content, mainly the sucrose and the total sugar, exhibited higher sugar levels in the purchased samples than those provided by industry (Table 1). Both the industry-provided and purchased breakfast cereals showed a broad distribution  
Figure 1: The novel handheld Fourier Transform near-infrared (FT-NIR) spectral sensor prototype used to analyze breakfast cereal samples (**a**), the inner section of the handheld FT-NIR spectral sensor prototype (**b**), and the NeoSpectra-Micro development kit (**c**).  
(large standard deviation) (Table 1). Various other researchers have measured comparable sugar levels in commercial breakfast cereals [6; 11; 16; 36; 37; 38].  
The sugar content listings on the nutrition facts labels of the store-bought cereal samples were compared with our HPLC findings. Eight out of 60 samples (13%) were not in compliance with total sugar content declaration, having significantly (more than \(\pm\)20% difference) higher total sugar content than the declared values, except one sample with less sugar content than the declaration. For instance, one of those samples reported a total of 10.6 g sugars/100 g of cereal, but our HPLC analysis found 18.2 g total sugars/100 g of cereal, which was 72% higher than the declared value. In a corn-based sample, the manufacturer listed 9.7 g sugars/100 g of cereal; the HPLC data showed 12.7 g/100 g of cereal, which was 31% higher than the declared value. In another sample, even though the manufacturers stated that the product had 0 g of sugar, we found 3.8 g of total sugar in 100 g of cereal. Products that are not in compliance with the declared content are possibly recalled, which can be avoided through the application of reliable and rapid testing that allows real-time decision-making to implement early corrections. Because the cereal production is continuous and cannot be tracked simultaneously using results from traditional techniques, the importance and the necessity of a technique that provides accurate and fast results become apparent.
## 2 Design of DSCT Specimens  
### Types of Module joints  
A DSCT wind turbine tower consists of concrete between the inner and outer tubes; therefore, it is heavier than a steel tower and a high capacity tower crane is required to build a single-body DSCT wind turbine tower. In terms of construction and transportation, modularizing a DSCT wind turbine tower could enhance economic feasibility. Modular joints that can be assembled onsite are required for the construction of a modular DSCT wind turbine tower; the joints should be developed and designed to sustain the stresses of the tower.  
There are three main methods of assembling the modules of a DSCT wind tower: connecting the inner tubes and outer tubes as shown in Figure 2, connecting concrete by using shear keys as shown in Figure 3, and joining all three of them as shown in Figure 4.  
Figure 1: Cross section of DSCT wind turbine tower.  
Figure 3: Shear keys for concrete connection (**left**: protruding type, **middle**: doughnut type, **right**: bolt type).  
In this study, three types of module joints were developed and their performances were confirmed by experiments. The first is a socket type connection (Type-S) between the upper and lower modules as shown in Figure 5.  
The second type of module joint is similar to Type-S; however, there is an H-shaped connector between modules as shown in Figure 6 (Type-H). In this type, an H-shaped connector is attached to the upper module by welding or by bolting; then, the upper module is assembled with the lower module by bolting at the construction site. Figure 7 shows the last type of joint that links all three components of a DSCT wind turbine tower: inner tubes are connected by bolting, outer tubes are joined by welding, and concrete parts are linked with shear keys fixed by injecting mortar (Type-W).  
Figure 4: Connection by bolting, welding, and injected mortar.  
Figure 5: Socket type (Type-S).  
Figure 6: H-type connector (Type-H).