esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### XPS Analysis  
A comprehensive investigation into the presence of metals and their valence state in the components of as-prepared samples relies heavily on the results of X-ray photo electron spectroscopy. X-ray photoelectron spectroscopy (XPS) revealed the presence of carbon (C), dysprosium (Dy), oxygen (O), tungsten (W) and zinc (Zn) in the forming DWZG nanocomposite, as shown in Figure 5a-f. The XPS spectroscopic element formation is consistent with the EDX analysis. The survey spectrum of DWZG shows the Zn 3d, W 4f, Zn 3p, Dy 4d, C 1s, O 1s, W 4s and Zn 2p elements in Figure 5a. High magnification XPS spectra for C 1s (284.8, 286.4 and 288.4 eV), Dy 4d (158.7 eV), O 1s (530.3 and 533.1 eV), W 4f (34.6 and 36.9 eV) and Zn 2p (1022.5 and 1045.6 eV) are shown in Figure 5b-f. The XPS peak in the C 1s spectrum (Figure 5b) is proportional to the binding energy value of 284.8 eV (C-C) (yellow), 286.4 eV (C-O) (pink) and 288.4 eV (C=O) (cyan), respectively [48]. Having a valence of Dy\({}^{4+}\), the core spectrum peak for Dy 4d\({}_{5/2}\) is seen at 158.7 eV [42] in Figure 5c. The adsorbed oxygen species causes the high intensity peak in O 1s to occur at 530.3 eV (pink) and 533.1 eV (yellow) (Figure 5d) [48]. Figure 5e displays the 35.59 eV (cyan) and 38.03 eV (pink) [44] ranges for the W 4f\({}_{5/2}\) and W 4f\({}_{7/2}\) peaks corresponds to the W\({}^{6+}\) chemical state, respectively. However, a new peak at 39.09 eV (yellow line) was observed for the W(4f\({}_{5/2}\)) peak, which was attributed to the creation of a W-C bond in the nanomaterials [49]. The binding energy of Zn 2p\({}_{3/2}\) and Zn 2p\({}_{1/2}\) is shown to be 1022.5 eV and 1045.6 eV [50], respectively, in the XPS spectra shown in Figure 5f. The effective synthesis of DWZG nanocomposite proves the binding energy values for the presented ingredients.  
Figure 4: (a) DRS-UV absorption spectra and (b) Tauc plot of DW, ZnO and DWZG nanocomposites.
## 6 Mobile Sensing Networks in the Smart City
### Smartphone-Centric MSNs  
Smartphone-centric MSNs are mainly composed of smartphones (although they may comprise other consumer electronics devices such as music players, wearable devices, _etc._), which may or may not communicate with each other, that use their available sensors to obtain information from their environment. This type of MSNs allow a paradigm called mobile crowdsensing, by which phenomena are monitored by a community of observers [50]. This paradigm has high potential in the Smart City [52, 53].  
A smartphone can generally provide information about temperature, atmospheric pressure, light intensity, GPS location, acceleration, gyro or magnetic compass, among others. Further information can be derived by processing the available sensed data samples, such as, e.g., the physical activity of the citizen or the traffic congestion while driving.  
Mobile crowdsensing is intrinsically dynamic (with user mobility in the range of pedestrian to vehicular speeds), and poses predictability issues. On the other hand, efficient operation requires the use of collaborative sensing techniques. To this end, distributed architectures and policies have been developed [52].
## 3 Nature of Input Dimension and Overfitting
### Characteristics of Harmonic Signals
#### 3.1.3. Consider Different Rotational Frequencies  
A set of rotational speeds were used to describe the frequency distinguishing ability of sparse filtering, where \(A=1\); \(\theta=0^{\circ};f_{r}=100\), \(150\), \(200\), \(250\), \(300\), \(350\), \(400\) Hz. Figure 4 shows the diagnosis accuracies using various input dimensions. The testing accuracy of all input dimensions achieved 100%. This suggests that the learned features of sparse filtering could reflect the frequency information of vibration signals. However, when the input dimension increased, the averaged testing accuracy was higher.  
To illustrate this phenomenon, we selected two different output dimensions of weight matrixes for comparison analysis, i.e., 100 and 200. The learned features and the spectra of weight matrixes are shown in Figure 5. It is seen in Figure 5 that the frequency interval in the spectra of weight matrixes was equal to that in the samples, and the amplitudes of spectra were proportional to the features with the corresponding frequency. This resulted in steady, clear, and different learned features within the various samples. However, the frequency interval of weight matrixes was equal to 100 Hz in Figure 5, twice the size of the frequency interval of samples. The features of several samples, whose frequencies were 150, 250, 350 Hz, depended on the amplitudes of adjacent frequencies in the spectra of the weight matrix. This also resulted in unclear learned features, as in Figure 5. Therefore, the frequency resolution of weight matrix depends on the input dimension.  
From the inspection of the properties of a series of harmonic signals, several conclusions can be summarized:  
1. Sparse filtering is unable to classify the initial phase information.
2. Sparse filtering can recognize the amplitude information, but the input dimension does not affect the learned features of sparse filtering.
3. The learned features of sparse filtering can reflect the frequency information. Additionally, the frequency resolution of weight matrix depends on the input dimension. The features are unstable when the input dimension reduces in size.  
Figure 4: Diagnosis results using various input dimension of harmonic signals group of different frequencies.
## Appendix A Unal Frequency Correlation  
Unal [3] expresses the growth time (depending on boiling parameters, such as subcooling, fluid velocity, and pressure). This correlation can be expressed as:  
\[t_{g,Ulmal}=\frac{1}{1.46 CB\phi} \tag{1}\]  
with B defined as:  
\[B=\frac{\Delta T_{sub}}{2(1-\frac{Pc}{p_{l}})} \tag{2}\]  
where \(\Delta T_{sub}\) is the subcooling between the water fluid and the wall temperature in \([K]\).  
\(\phi\) is expressed as:  
\[\phi=\left\{\begin{matrix}\left(\frac{p_{l}}{v_{0}}\right)^{0.47}&if:v>0.61\ \mathrm{m/s}\\ 1&if:v<0.61\ \mathrm{m/s}\end{matrix}\right\} \tag{3}\]  
where \(v\) is the fluid velocity in \([m/s]\).  
The C coefficient is expressed as a function of the pressure \(P\) in the bar:  
\[C=65-5.69\times 10^{-5}(P-10^{5})\ \ for:1<P<10\ \mathrm{bar} \tag{4}\]  
The range of validity of this correlation is given in Table 1.  
For minimal subcooling, the growth time of a bubble was calculated for several pressures (within the validity range of the correlation).  
For this study, we chose to keep the maximum growth time for a bubble under Unal conditions:  
\[t_{g}\approx 5\times 10^{-3}\ \mathrm{s} \tag{5}\]
# On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information
## 2 Related Work
### Age of Information
Given the above, Figure 2 shows a sample of the Age of Information from the perspective of a node receiving update packets from the same source, i.e., successive packets of, e.g., a sensor measurement, demonstrating the characteristic sawtooth pattern expected for AoI. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Interpretation** \\ \hline \(X(t)\) & Stohastic process observed by the source node \\ \(t_{i}\) & Timestamp of extraction of packet \(i\) \\ \(\lambda\) & Average rate of creation of status updates in the source node \\ \(\mu\) & Average rate of of transmission of status updates to the destination node \\ \(t^{\prime}_{i}\) & Timestamp at which status update \(i\) is received from the destination node \\ \(N(i)\) & Index of the most recent update at time \(t\) \\ \(u(t)\) & Timestamp of the most recent update at time \(t\) \\ \(\Delta(t)\) & Random process of Age of Information \\ \(Y_{i}\) & The \(i\)-th interarrival time, i.e., the time elapsed between the creation of \\  & update \(i\) and the previous update \\ \(T_{i}\) & The \(i\)-th system time, i.e., the time required to generate status update \(i\) \\  & from source until the receipt of this update from the destination node \\ \(\Delta_{T}\) & Average age of a status information system, considering an observation \\  & period (0, \(T\)) \\ \(\Delta\) & Average Age of Information \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of mathematical symbols used in this work. Figure 1: The basic model for Age of Information. In order to understand more about the process \(\Delta(t)\), we provide some more definitions: the \(i\)-th interarrival time \(Y_{i}\) is defined as the time elapsed between the creation of update \(i\) and the previous update, i.e., update \(i-1\):  
\[Y_{i}=t_{i}-t_{i-1}. \tag{4}\]  
In addition, system time \(i\), denoted by \(T_{i}\), corresponds to the sum of queue time and service time of update \(i\), and it is defined as the time required to generate status update \(i\) from source until the receipt of this update from the destination:  
\[T_{i}=t_{i}^{\prime}-t_{i}. \tag{5}\]  
Assuming that the procedure \(\{\Delta(t)\}\) is ergodic (a stochastic process is characterized as ergodic if the ensemble average of an observable sample equals the time average. In such case, any collection of random samples from the stochastic process represents the average statistical properties of the entire process), the average age can be calculated using a time average. Considering an observation period (\(0\), \(T\)), the average age of a status information system is calculated as:  
\[\Delta_{T}=\frac{1}{T}*\int_{0}^{T}\Delta(t)\,dt. \tag{6}\]  
Taking the length of the observation interval \(T\) to infinity and observing Figure 2, it is proven that the expression for the average Age of Information is:  
\[\Delta=\frac{E[Y*T]+E[Y^{2}]/2}{E[Y]}, \tag{7}\]  
where it is stated that \(\lambda=\frac{1}{E[Y]}\) is the steady state rate of creating status updates and \(E[. ]\) is the mean value operator.
## 3 Results and Discussion
### Temperature Distribution  
During the stable complete-combustion process, data retrieval was conducted once every 15 min by recording the temperatures in the radiative and convective sections of the furnace and the emission concentrations in the flue gas. Combustion instability did not occur in either case (diesel alone and the blend of 5% FPBO + 95% diesel). Figure 5a,b show the temperature distributions with time for the pure diesel (with an air flow rate of 246 Nm\({}^{3}\)/h) and the blend of 5% FPBO + 95% diesel (with an air flow rate of 256 Nm\({}^{3}\)/h), respectively, at a fixed amount of liquid fuel supply rate of 20 L/h. As shown in Figure 5a,b, due to the fact that the wall temperature of the combustion chamber had reached a near-steady state, the temperature change was very small during stable combustion under the optimum operating conditions (which met the minimum residual oxygen in the flue gas required for complete combustion). In other words, all the temperatures (\(T_{\rm w1}\)-\(T_{\rm w5}\) and \(T_{\rm g1}\)-\(T_{\rm g5}\)) remained at almost constant values during the stable combustion processes.  
Figure 4: Combustion tuning process for burning (**a**) diesel alone; and (**b**) the blend of 5% FPBO + 95% diesel. The optimal air flows are point E for diesel alone and point C for the blend.  
Figure 5: Temperature distributions with time for burning (**a**) diesel alone; and (**b**) the blend of 5% FPBO + 95% diesel.  
As shown in Table 3, the FPBO had a high oxygen content of 56.31 wt % and a smaller heating value (LHV: 14.41 MJ/kg) than the diesel fuel (LHV: 43.13 MJ/kg). Therefore, at a fixed fuel flow rate (20 L/h), the wall temperatures (\(T_{\text{w1}}\)-\(T_{\text{w5}}\)) in the radiative section and the gas temperatures (\(T_{\text{g1}}\)-\(T_{\text{g5}}\)) in the flue gas section dropped slightly when burning the blend of 5% FPBO + 95% diesel as compared with burning diesel alone, as illustrated in Figure 6a,b as well as Table 4. More specifically, in the primary combustion zone, when firing the blend of 5% FPBO + 95% diesel a lower \(T_{\text{w2}}\) (1188.0 \({}^{\circ}\)C) was obtained as compared to when firing diesel alone (1279.2 \({}^{\circ}\)C). Figure 6b also demonstrates that the flue gas temperature decreased gradually from \(T_{\text{g1}}\) to \(T_{\text{g5}}\). This tendency is related to the measurement positions and the effects of cooling by using a heat exchanger.
# Demonstration of a Hybrid B5G System Integrating VLC and RF-Based Technologies with Access Networks
## 2 Literature Review
To the best of our knowledge, this represents the first 5G NR optical wireless network implementation with three alternative access networks. Another advantage compared to our previous work is the possibility of increasing the VLC coverage area, due to the use of an LED, as well as the cost reduction compared to the laser solution.
## 3 Case Study--Results
### Case No. 2  
In case no. 2 the crude preheat trains were selected from another real-life HEN coupled with a CDU rated 220 kg/s of crude oil. Fourteen shell-and-tube, two-pass HEs with straight tubes and floating heads are connected as schematically shown in Figures 3 and 9.  
For each HE, the relationship between the fouling factor \(R_{f}\) and time \(t\), was determined using method described in the work [16]. In this case, all measurements of temperature and mass flow at the inlet and outlet of the studied HEs were available. Obtained \(R_{f}\) values of HEs that were used in the simulation studies are presented in Table 2.  
As shown in the HEN scheme in Figure 3, the desalted crude-oil stream is split in parallel branches and after exchangers E3-14 AB and E3-16 ABC the branches are connected again into the one preheat train. There is one simple control setup with PID controller 3. In the control loop 3 with exchanger E3-18 AB as indicated in Figure 3, the controlled variable is the tube-side outlet temperature \(T_{3}\) before the preflash column, while the manipulated variable is shell-side by-pass mass flow rate (which can be changed by the action of control valve 3). The other process variables are the disturbances.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**HE** & \multicolumn{2}{c}{**Fouling Factor \(R_{f}\times 10^{-3}\)**} & \multicolumn{2}{c}{**Total Heat Transfer**} & \multicolumn{2}{c}{**HE**} & \multicolumn{2}{c}{**Fouling Factor \(R_{f}\times 10^{-3}\)**} & \multicolumn{2}{c}{**Total Heat Transfer**} \\
**no.** & \multicolumn{2}{c}{**(m\({}^{2}\)K/W) after Period of**} & \multicolumn{2}{c}{**Coefficient for**} & \multicolumn{2}{c}{**no.**} & \multicolumn{2}{c}{**(m\({}^{2}\)K/W) after Period of**} & \multicolumn{2}{c}{**Transfer Coefficient**} \\  & **1** & **2** & **3** & **Clean HE** & & **1** & **2** & **3** & **for Clean HE** \\  & **year** & **years** & **years** & \(U\) (W/m\({}^{2}\)K) & & **year** & **years** & **years** & \(U\) (W/m\({}^{2}\)K) \\ \hline E1-11 AB & 0.903 & 1.118 & 2.235 & 728 & E2-11 & 0.608 & 1.115 & 2.235 & 691 \\ E1-12
## 2 Methods
### Model Simulation
#### 2.5.3 The Test Phases  
There are a lot of performance measure methods that could be utilized to evaluate the accuracy. Therefore, we choose two metrics in this study: the mean square error (_MSE_) and mean absolute percentage error (_MAPE_) to comprehensively test the network performance. _MSE_ is a measure of the average of the prediction error squares; it can assess the variation of the model. A smaller _MSE_ value represents a better prediction of the model. _MAPE_ is a measure of the accuracy of the prediction methods used in statistics for performance evaluation and comparison.  
\[MSE=\frac{1}{N}\sum_{n=1}^{N}(y_{n}-\hat{y}_{n})^{2} \tag{13}\]  
\[MAPE=\frac{1}{N}\sum_{n=1}^{N}\abs{\frac{y_{n}-\hat{y}_{n}}{y_{n}}}\times 100\% \tag{14}\]  
where \(y_{n}\) and \(\hat{y}_{n}\) represent output value and predicted value of _n_th data for agricultural investment climate evaluation. \(N\) is the total number of data used for agricultural investment climate evaluation. The results are listed in Table 4.  
Through network training, the threshold values of the agricultural investment climate evaluation index in Table 3 were simulated and calculated, and the output results were used as the basis to classify the levels of CEE countries' agricultural investment climate. According to the critical value of agricultural investment climate level simulation results, the evaluation level is divided into I-IV degree. The threshold results are shown as follow:  
- Level I. good investment climate (\(\geq 0.6484\))  
- Level II. relatively good investment climate (0.4885, 0.6484)  
- Level III. relatively poor investment climate (0.3285, 0.4885)  
- Level IV. poor investment climate (\(\leq 0.3285\))  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Mean Square Error** & **Mean Absolute Percentage Error (\%)** \\ \hline Training sample & 0.0041 & 0.4012 \\ \hline Test sample & 0.0062 & 0.4513 \\ \hline \hline \end{tabular}
\end{table}
Table 4: BP neural network performance evaluation results (10 random times).  
Figure 3: The error of BP neural network.  
A good investment climate reflects that the overall climate is suitable for agricultural investment, and a relatively good investment climate reflects that only a few issues in the investment climate require attention. A relatively poor investment climate represents the regional investment climate that needs investors' careful consideration, while a poor investment climate refers to the overall climate for investment that needs to be improved.
## 3 Experimental Results
### Results of Dynamic Uniaxial Compression Tests on Hard Ice  
#### 3.1.1 Uniaxial Compressive Strength of Hard Ice  
Eighty 5-centimeter edge length cubic hard ice specimens that are created from ice blocks harvested during the stable freezing season are tested, and thirteen of them fail. Two testing temperatures, \(-5\)\({}^{\circ}\)C and \(-10\)\({}^{\circ}\)C, and two loading directions, along the length of the columns and perpendicular to the length of the columns, are chosen in the experiments. Under each temperature and loading direction, five loading rates, 0.1, 0.2, 0.5, 1.0, and 2.0 m\(\cdot\)s\({}^{-1}\) are employed. The nominal strain-rate corresponding to these loading rates are 2.0, 4.0, 10.0, 20.0, and 40.0 s\({}^{-1}\), respectively.  
Typical stress versus nominal strain-rate profiles of hard ice at different loading rates are shown in Figure 8. Peak stress derived from the upper load cell is greater than that derived from the lower load cell and the difference between them increases with an increasing loading rate. However, the differences are less than 10%, so ice specimens are deemed to achieve balanced forces in loading direction at these loading rates, and the inertial effect is negligible. The average values of the strength values derived from the upper and lower load cells are taken as the compressive strength of ice specimens.  
The compressive strength results are shown in Table 1. In General, ice strength values at \(-10\)\({}^{\circ}\)C are greater than that at \(-5\)\({}^{\circ}\)C. In the nominal strain-rate range of 2-40 s\({}^{-1}\), at a testing temperature of \(-5\)\({}^{\circ}\)C, uniaxial compressive strength values vary from 2.0 to 10.0 MPa with an average strength value of 6.0 MPa. While, at a testing temperature of \(-10\)\({}^{\circ}\)C, uniaxial compressive strength values vary from 5.0 to 12.0 MPa with an average strength value of 8.5 MPa. Standard deviations of result data are in the range of 1.0-2.0 MPa, varying with nominal strain-rate. Though there is a great scatter in data, the compressive strength shows an increasing trend with increasing loading rate. Through comparing compressive strength values of hard ice in different loading directions, it could be found that strength along the length of the columns is slightly stronger than that perpendicular to the length of the columns.  
Figure 7: Typical speckle field on the ice specimen surface: (**a**) entire gray-scale image; (**b**) speckle image; and (**c**) pseudo-color image of the speckle field. The speckle size in present experiments is 8–15 pixels.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Fuzzy Logic
#### 3.1.10 Eedcf  
The authors of [41] propose an energy-efficient distributed clustering algorithm based on fuzzy called EEDCF. The proposed approach defines four (04) different states for each node: the initial state, the competing CH state, the elected CH state, and the member node state. During the first phase, each node has to build its own information table which contains the node ID, its residual energy, the neighbors' ID, and their corresponding remaining energy. After each round, the node updates its information helped by the packet Node_MSG, which classifies the proximity of neighbors from itself. At the second phase, the node performs the fuzzy logic analysis based on its residual energy, the number of neighbor's nodes within its communication radius (node degree) and the average residual energy of its neighbors. At the end of this step, each node turns into compete CH state and sends its output to all neighbor nodes within its communication radius. Where the output is obtained through IF-THEN rules in accordance with the mechanism of the TSK fuzzy model, the node with the lesser output turns into the member node state, and waits for joining a suitable cluster after CH election. However, the node with higher output turns into the elected CH state as well. Based on the RSSI, nodes join appropriate CH and thereafter build clusters. In order to evaluate EEDCF, authors have considered two scenarios with the same area size and the same location of the BS. The first scenario deploys 100 nodes whereas the second scenario uses 150 nodes within the study field. The experimentations shown that EEDCF outperforms the distributed algorithm for the energy-efficient data gathering protocol based on cluster structure called EADEEG [42] and DFLC. The lifetime of the network on EEDCF is better than on EADEEFG and on DFLC and has the best value of FND, HND, and LND in both scenarios. The results have also shown that the proposed approach had a better data delivery rate than EADEEG and DFLC.
## 2 Constellation Design
### Constellation Technical Characteristics
#### 2.2.2 Design Concept of Coverage Priority  
Through the development of remote sensing satellites, spatial resolution has always been one of key objectives. However, with the spatial resolution being improved, the swath width decreases accordingly, which results in the coverage and revisiting ability being decreased at the same time. Unlike the spatial-resolution-first concepts commonly used, HSC prioritizes coverage. Design elements, such as orbit inclination, swaying capability,swath width, imaging time per track, data download speed, and ground station distribution, are tailored for this specific need.