esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermodynamic Effect of Temperature  
The effect of temperature on adsorption was studied by performing the experiments at three different temperatures (10 \({}^{\circ}\)C, 22 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C) to study how the adsorption system works in colder environments or for higher temperature process waters. The removal percentages at 10 \({}^{\circ}\)C, 22 \({}^{\circ}\)C, and 44 \({}^{\circ}\)C were 88%, 91%, and 92%, respectively. Therefore, temperature has no meaningful effect on Zn removal, and the produced material can be used effectively in a wide temperature range.  
Experiments were performed at three different temperatures, 10 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C, and the results were used to calculate the parameters of the Van't Hoff plot (paragraph 2.6, Equation (8)). \(\Delta H^{0}\) was calculated from the slope of the plot, and \(\Delta S^{0}\) was calculated from the intercept of the plot. In addition, Gibbs free energies \(\Delta G^{0}\) were calculated utilizing Equation (10). All the values are shown in Table 5, and the plot is presented in Figure 9.  
The obtained \(\Delta G^{0}\) values are negative, indicating that the adsorption process is spontaneous. The positive value of \(\Delta S^{0}\) indicates that entropy is increasing in the process, and the positive \(\Delta H^{0}\) value, which is smaller than 40 kJ/mol, refers to endothermic physisorption [47; 48].  
Figure 8: Weber–[PERSON] intraparticle diffusion model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Temperature (\({}^{\circ}\)C)** & \(\Delta\)**G\({}^{\circ}\) (kJ/mol)** & \(\Delta\)**S\({}^{\circ}\) (J/mol K)** & \(\Delta\)**H\({}^{\circ}\) (kJ/mol)** \\ \hline
**10** & \(-\)17.06 & & \\
**23** & \(-\)18.50 & 110.15 & 14.00 \\
**40** & \(-\)20.42 & & \\ \hline \end{tabular}
\end{table}
Table 5: Thermodynamic parameters of Zinc (Zn) sorption on the biomass-based activated carbon.
# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
## 2 Problem Definition
### Target System
#### 2.1.2 Discrete Event System  
A discrete event system is an event-driven system in which discrete states are updated depending entirely on the occurrence of asynchronous discrete events over time. In discrete event simulations, system states are scheduled dynamically as the simulation proceeds. Various modeling schemes (e.g., finite state machine, Petri-net, DEVS, or Markov chain) have been used for discrete event simulations [24; 25; 26; 27; 28]. Among them, the DEVS is a modular and hierarchical formalism for object-oriented modeling and analysis [2], which is our approach for modeling the discrete event system.  
Figure 2 shows the structure of a DEVS atomic model and the following specification is its formal representation [18]. The DEVS atomic model consists of three variables and four functions. As the DTSS model in Figure 1, a circle in the DEVS model represents the state variable and the squares indicate the functions. System behaviors in the model are represented by state transitions using input and output events. State variables with discrete values are updated by two transitions: when the model receives an input event, state variables are updated; otherwise, if it does not receive any input events until a certain time, an output event occurs and the state variables are renewed. The former case is conducted by an external transition function, and the latter one is carried out by an output and an internal transition function. The amount of time that a state waits for an input event is decided by a time advance function. In Figure 2, the solid arrows in the DEVS model are relevant to the former case, whereas the dotted ones correlate to the latter case. We modeled UTVs with the DEVS formalism, which will be explained in Section 5.  
\[DESM=\langle X,Y,S,\delta_{ext},\ \delta_{int},\lambda,ta\rangle\]  
\(X\) is the set of inputs;  
\(Y\) is the set of outputs;  
\(S\) is the set of states;  
\(\delta_{ext}:Q\times X\to S\) is the external transition function  
\(Q=\{(s,e)|s\in S,0\leq e\leq ta(s)\}\);  
\(\delta_{int}:S\to S\) is the internal transition function;  
\(\lambda:S\to Y\) is the output function;  
\(ta:S\to Real\) is the time advance function.
## 2 Parabolic Antenna Analysis Method
### Radiation Characteristic Analysis  
In this paper, the parabolic reflector's surface was divided and replaced with charge point sources. Consequently, the radiation pattern can be derived based on array theory  
Figure 4: Normalized induced amplitude based on (**a**) the propagation effect (\(I^{R}\)); (**b**) the feeder pattern effect (\(I^{P}\)).  
Figure 5: Induced phase on the (**a**) parabolic surface and (**b**) parabolic aperture.  
for these charge point sources. The array theory-based radiation pattern [32], as expressed in Equation (16), can be obtained by considering the magnitude and phase of the induced signals, along with the phase differences based on the positions of each charge point source in the far-field region.  
\[f(\theta,\ \phi)=\sum_{n=1}^{N}\ I_{n}^{\ P}I_{n}^{\ R}\exp\Big{(}\ j\ k\hat{r} \hat{r_{n}}\Big{)}\exp(\ j\ a_{n}\ ) \tag{16}\]  
where \(\hat{r}\) is the unit vector in the radial direction based on the spherical coordinate system.  
Figure 6 shows the normalized radiation pattern calculated according to Equation (16). Here, the antenna configuration is identical to that shown in Figure 3a. In Figure 6a, the bold black solid line indicates the half-power beam width (HPBW) boundary.  
The formula for calculating the directivity [32] of the antenna is given by Equation (17) based on the radiation pattern from Equation (16).  
\[D(\theta,\ \phi)=\frac{4\pi|f(\theta,\ \phi)|^{2}}{\sum_{p=1}^{P}\sum_{q=1}^{Q} \lvert f(\mathrm{q}\Delta\theta,\ p\Delta\phi)\rvert^{2}\ \sin(\mathrm{q}\Delta\theta)\ \Delta\theta \tag{17}\]  
where \(\Delta\theta\) is \(\pi/Q\) and \(\Delta\phi\) is \(2\pi/P\). In this paper, \(P\) and \(Q\) are set to 600 and 1200, respectively.  
The analysis results of the radiation characteristics in Figure 6 indicate that the beam steering angles \((\theta,\ \phi)\) are \(6^{\circ}\) and \(225^{\circ}\), and the maximum directivity, calculated based on Equation (17), is determined to be 37.6 dBi.
# Fatigue Assessment of Prestressed Concrete Slab-Between-Girder Bridges
## 2 Materials and Methods  
### Description of Case Study Bridge  
Of the 70 slab-between-girder bridges in the Netherlands, the bridge that has the most critical slab geometry (largest span to depth ratio of 3.6 m/0.2 m = 18) is the approach bridge of the Van Brienenoord Bridge in Rotterdam, see Figure 1a. The approach spans are 50 m in length and consist of thin, transversely post-tensioned decks that are cast between simply supported post-tensioned girders, see Figure 1b [13]. The clear span of the slab is 2100 mm. The transverse prestressing level is 2.5 MPa. The duct spacing on the deck is 650 mm at the center, and at some positions it is increased to 800 mm at the center. Table 1 gives the main properties of the geometry and reinforcement of the decks. Post-tensioned crossbeams are built at the end of the spans and post-tensioned diaphragm beams are provided at 1/3 and 2/3 of the span length.  
At the time of construction, the design concrete compressive strength of the deck was B35 (\(f_{ck,cube}\) = 35 MPa) and B45 (\(f_{ck,cube}\) = 45 MPa) for the girders. Testing of the cores taken from the deck slab resulted in an average \(f_{cm,cube}\) = 98.8 MPa (\(f_{ck,cube}\) = 84.6 MPa), as a result of the continued cement hydration. For the assessment calculations, we conservatively assumed that the mean compressive cylinder strength \(f_{cm}\) = 65 MPa on the deck. The associated characteristic concrete compressive strength was \(f_{ck}\) = 53 MPa.  
Fatigue load model 1 has the same configuration as load model 1, with 0.7\(Q_{ik}\) for the axle loads, and 0.3\(q_{ik}\) for the distributed lane loads. In other words, the axle load becomes 0.7 \(\times\) 300 kN = 210 kN, and the load per wheel print becomes 105 kN. The distributed lane load is 0.3 \(\times\) 1.15 \(\times\) 9 kN/m\({}^{2}\) = 3.105 kN/m\({}^{2}\). The fatigue load model has as a reference load of 2 million trucks per year. In the Netherlands, the guidelines for the assessment of bridges (RBK [32]) uses a higher number of passages: 2.5 million trucks per year. Over a lifespan of 100 years, the result is 250 million truck passages.  
In the Netherlands, assessment is carried out using both a wheel print of 400 mm \(\times\) 400 mm (as prescribed by the Eurocode 1 NEN-EN 1991-2:2003 [1]) and a wheel print of 230 mm \(\times\) 300 mm (used for the fatigue evaluation of joints, but also often used as an additional check in assessments).
## 6 Conclusions  
According to the change rules of the output electrical parameters and the equivalent circuit internal parameters of the photovoltaic array in different fault states, a LSSVM multi-classifiers model in Bayesian theory has been built to diagnose the fault types of the photovoltaic array.  
The proposed method has the ability to construct an optimal multiple-classifiers model and to obtain the posteriori probabilities of the samples, which can identify the states of the photovoltaic array. Four kinds of working conditions are simulated to validate the effectiveness of the approach--that is, the normal condition, the short-circuits condition, the open-circuits condition, and the abnormal aging condition. The simulated results indicate that the method can classify the fault types accurately, which have a higher generalization ability and a good modeling effect. Furthermore, an experimental platform is built to test the experimental performance of the developed approach, while the experimental results also demonstrate the effectiveness of the fault diagnosis model in a practical system.  
This paper deeply analyzes the change rules of the output electrical parameters and the equivalent circuit internal parameters of the photovoltaic array in different fault states. It also introduces the Bayesian Framework for LSSVM into the field of photovoltaic fault diagnosis so that we can locate the faulty photovoltaic modules into a certain photovoltaic array and further diagnose its fault types, thus greatly reducing the number of sensors and the costs, while ensuring that the solar power station operates safely and stably.  
This study was conducted as part of the project cooperated with the State Grid Corporation of Qinghai, China. It is also supported by the Fundamental Research Funds for the Central Universities (2016 MS06).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**The Fault** & **The Short** & **The Open** & **The Abnormal** & **The** & **The Actual Fault** & **The Fault Type of** \\
**Types** & **Circuit** & **Circuit** & **Aging** & **Normal** & **Type** & **Diagnose** \\ \hline
11\# & 0.5163 & 0.1237 & 0.1895 & 0.1705 & 1 & 1 \\
12\# & 0.3024 & 0.1303 & 0.4596 & 0.1077 & 1 & 3 \\
13\# & 0.4926 & 0.2902 & 0.1239 & 0.0933 & 1 & 1 \\
14\# & 0.3954 & 0.2197 & 0.2124 & 0.1725 & 1 & 1 \\
15\# & 0.4697 & 0.2303 & 0.1176 & 0.1824 & 1 & 1 \\
16\# & 0.4213 & 0.1478 & 0.1355 & 0.2954 & 1 & 1 \\ \hline \end{tabular}
\end{table}
Table 6: The output probabilities of photovoltaic samples in short-circuits state.  
Figure 7: The classification of test samples.  
**Author Contributions:** All authors contributed equally to this work. [PERSON] proposed the original idea. [PERSON] designed the overall framework of this paper, built the simulation model and completed the manuscript. [PERSON] modified and refined the manuscript. [PERSON] conducted data analysis. All authors read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Discussion
### Partial Melting Processes  
Whole-rock geochemistry and mineral chemistry of primary mantle minerals can be used to manifest different processes affecting the protolith, including partial melting and metasomatism [44; 58]. It is noticeable that the examined rocks contain low concentrations of TiO2, ranging from 0.01 to 0.03 wt.%, suggesting a high degree of partial melting [59].  
On the other hand, systematic changes in primary minerals (olivine, pyroxene, and spinel) can be used as a key to deduce historical processes such as metasomatism and melting degree [60]. It is noteworthy that Cr-spinels are more resistant primary minerals and can be used to infer the degree of partial melting [60]. The examined Cr-spinels of serpentinized peridotites have high Cr# (more than 6; av. 0.754), TiO2 \(<\) 0.3, and Mg# (av. 0.42), reflecting a high degree of partial melting (\(>\)35%) (Figure 10a) that developed in a forearc setting [22; 38; 61; 62]. In terms of Cr# vs. TiO2 in Cr-spinel, they exhibit depleted mantle, and the examined sample was located in the fresh Cr-spinel of the Egyptian Eastern Desert and forearc fields (Figure 10b).  
The Cr# content of both spinel types (especially Cr-rich) can be used to detect the degree of partial melting in the host rocks [62]. The degree of partial melting in spinel mantle can be calculated using the equation described in [60] (F = 10 \(\times\) nCr# + 24), where F is the melting degree (wt.%). Based on this empirical equation, the examined rocks had experienced partial melting degrees ranging from 13.32 to 14.63% for Al-spinel, and from 17.34 to 24% for Cr-spinel, which is consistent with forearc affinity [61]. Moreover, the analyzed Al-spinels contained Al2O3 ranges from 33.55 to 38.55 wt.%, and Cr# from 0.34 to 0.39, which may be related to decompression melting in the astrosphere (Al content decreases with increases in the degree of partial melting) according to [17; 38].  
In addition, depletion of Na2O (av. 0.12 wt.%), K2O (av. 0.013 wt.%), Al2O3 (av. 2.1 wt.%), Cr2O3 (av. 0.628 wt.%), and TiO2 (av. 0.048 wt.%) in the examined Cpx (Figure 4c,d), and the high forsterite (Fo = 91.94-98.13, Figure 4a) content of the analyzed olivine relics, reflect depleted mantle that had been subjected to high partial melting in a forearc setting [38].
## 4 Review of Existing Methods and Capabilities
### Path Planning  
Path planning requires knowledge of target or mission needs in order to properly complete the planning algorithms. We will define three primary types of path planning: Fixed Target, Moving Target, and Target Search and Surveillance. Additionally, a system that can complete multiple scenarios within the three primary areas is valuable. An extension to multiple scenarios would be multiple aircraft supporting either the same type of mission or multiple scenarios.
## 4 Vehicle Trajectory Prediction Model
### Input and Output  
The input and output of the prediction model are denoted as \(X\) and \(Y\), respectively, as shown in Equation (10). \(T_{h}\) and \(\tau\) represent the observation time for the input and the prediction time range for the output, respectively. During the input phase, parameters such as the vehicle's position, speed, acceleration, and steering angle were recorded with a time step of 0.1 s, until \(T_{h}\) was reached. In the prediction phase, the vehicle's motion parameters are predicted using the same time step of 0.1 s, until \(\tau\) was reached.  
\[\left\{\begin{array}{l}X=X_{t-T_{h}},X_{t-T_{h}+1},\cdot\cdot\cdot,X_{t};\\ Y=Y_{t+j},j=1,2,\cdot\cdot\cdot,\tau;\end{array}\right. \tag{10}\]  
\[\left\{\begin{array}{l}X_{t}=\left[x_{t-p^{*}}^{k}y_{t-p}^{k}\right]\text{, }k \in[1,2,\cdot\cdot\cdot,N],p\in[0,1,2,\cdot\cdot\cdot,T_{h}];\\ Y_{t+j}=\left[x_{t+q^{*}}^{k},y_{t+q}^{k}\right]\text{, }k\in[1,2,\cdot\cdot\cdot,N],q\in[0,1,2, \cdot\cdot\cdot,\tau].\end{array}\right. \tag{11}\]  
In the equation, \(X_{t}\) represents the input range and its dimensions (\(T_{h}\) time steps \(\times\)\(x,y\) coordinates \(\times\) number of vehicles), and \(Y_{t+j}\) represents the output range and its dimensions (\(\tau\) time steps \(\times\)\(x,y\) coordinates \(\times\) number of vehicles).
## 3 Results
### Skin Macule Characterization System  
In this section, we present some of the results of the system for the detection and monitoring of macules in diabetic patients. With the processing of the images using the WAIS (Wireless Image System), the segmentation of the patients' maculae into vascular, atrophic and traumatic was obtained, as well as the percentage of damage in the patient. A specialized software for skin macules called Skin Macules Characterization Software (SMaC(r)) was designed in MATLAB GUI; by analyzing the macules of this study (vascular, petechiae, atrophic and traumatic), it has been possible to identify the properties of each one of them, such as shade indices and morphological properties. It can be said that, with this system, it is possible to differentiate the types of macules since significant differences of \(p<0.05\) were found.  
In Figure 8, we can see a screen of the SMaC(r) software for the segmentation and characterization of macules; it shows some images of the processing, data of the morphological properties and shadow indices of a macule by trauma, and it seeks to keep track of the changes of the macules in diabetic patients and thus help the treating physician to provide correct treatment for the patient.  
Figure 8: SMaC® software for segmentation and characterization of macules in diabetic patients. The macule shown is due to trauma.
# Smart Intravenous Infusion Dosing System
## 5 Smart IV Infusion Dosing System  
Figure 3 shows the proposed smart IV infusion dosing system (SIVIDS) consisting of three essential parts: (i) sensing and computation layer, (ii) communication layer and (iii) user layer. The first layer refers to the detection and signaling of liquid levels in the IV bottle, and consists of input elements such as sensors, buttons, and controllers for  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Room**} & \multirow{2}{*}{**Patient**} & \multirow{2}{*}{\begin{tabular}{c} **Distance** \\ **(m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **NRT** \\ **(s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **IV** \\ **Bottles** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Visual** \\ **Control** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Total** \\ **Time 1** \\ **(s)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Total Distance 2** \\ **(m)** \\ \end{tabular} } \\ \hline \multirow{2}{*}{PR-1} & P1 & 21 & 10.87 & 5 & 5 & 108.70 & 210 \\ \cline{2-7}  & P2 & 23 & 11.90 & 5 & 5 & 119.00 & 230 \\ \hline \multirow{2}{*}{PR-2} & P1 & 24.5 & 12.68 & 5 & 5 & 126.80 & 245 \\ \cline{2-7}  & P2 & 26.5 & 13.71 & 5 & 5 & 137.10 & 265 \\ \hline \multirow{2}{*}{PR-3} & P1 & 28 & 14.49 & 5 & 5 & 144.90 & 280 \\ \cline{2-7}  & P2 & 30 & 15.52 & 5 & 5 & 155.20 & 300 \\ \hline \multirow{2}{*}{PR-4} & P1 & 31.5 & 16.30 & 5 & 5 & 163.00 & 315 \\ \cline{2-7}  & P2 & 33.5 & 17.33 & 5 & 5 & 173.30 & 335 \\ \hline \multirow{2}{*}{PR-5} & P1 & 35 & 18.11 & 5 & 5 & 181.10 & 350 \\ \cline{2-7}  & P2 & 37 & 19.14 & 5 & 5 & 191.40 & 370 \\ \hline \multirow{2}{*}{PR-6} & P1 & 36 & 18.63 & 5 & 5 & 186.30 & 360 \\ \cline{2-7}  & P2 & 38 & 19.66 & 5 & 5 & 196.60 & 380 \\ \cline{2-7}  & P3 & 40 & 20.70 & 5 & 5 & 207.00 & 400 \\ \cline{2-7}  & P4 & 42 & 21.73 & 5 & 5 & 217.30 & 420 \\ \cline{2-7}  & P5 & 42 & 21.73 & 5 & 5 & 217.30 & 420 \\ \cline{2-7}  & P6 & 40 & 20.70 & 5 & 5 & 207.00 & 400 \\ \cline{2-7}  & P7 & 38 & 19.66 & 5 &signal interpretation, as well as output elements, such as an audible signal (buzzer), flow regulation valve, and a device for displaying the current state of the system (display). The second layer refers to communication by enabling coupling between the first and third layers using a wireless communication module on one side and a wireless router on the other. In addition, the communication layer allows internet access, and thus data from the devices of the other two layers can be stored directly on the cloud server. The third and final layer allows the medical staff to monitor and visually display the infusion reception in real time for each patient individually but also features a visual display of possible alarms at the nurse room (R-A1) for monitoring. It should be noted that all layers are of the modular type, allowing, if necessary, the upgrade and improvement of the entire system, including the replacement or addition of components or the use of another computer or mobile application.
## 4 Experimental Verification
Static single-node ranging experiments were carried out to evaluate the underwater communication effects and maximum communication range of this underwater current electric field communication system. As shown in Figure 14, the distance between the transmitting and receiving electrodes was changed. By adjusting the amplification multiples of the power amplifier circuit and front-end automatic gain amplifier circuit, the signal voltage was adjusted to an appropriate level. The BER was calculated based on the number of correct and incorrect information bits received through the PC serial port. When the communication distance was less than 11.2 m, the communication bit error rate was 0.0001%; when the distance was greater than 11.2 m, the bit error rate increased rapidly as the distance increased; when the communication distance was 11.6 m, the communication BER was about 50%; when the communication distance was 12 m, the BER was 99%; and when the communication distance exceeded 12.5 m, the communication bit error rate was 100%. This underwater current electric field communication system can achieve underwater communication within an 11.2 m range with a bit error of 0.0001%. In comparison, under the same transmission power, the communication distance was 2.4 m for the 2 ASK modulation and 3.8 m for the FSK modulation. In contrast, this system has a longer communication range at the same bit error rate [28]. Figure 14: Comparison of communication distances and BER for different modulation schemes. Figure 13: Underwater electric field communication angle experiment.