esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology for Model Construction
### TAN Learning Method for RORC Severity Prediction
#### 4.2.2 TAN Learning Procedure  
Pearson correlation coefficients were analyzed using IBM SPSS Statistics Ver.24 (International Business Machines Corporation, New York, NY, USA) for correlation between variables using 403 out of 443 RORC data. Variables with high correlations as a result of basic analysis, such as \"radius of curve\" and \"horizontal alignment\", were set as the top priority when designing the TAN structure.  
In the case of TAN learning, the most important part is to design the learning structure after analyzing the correlation between variables. Prior to the learning procedure, a basic analysis of correlations by factors was performed. After basic analysis, for RORC severity analysis and prediction, TAN learning proceeded in five steps. The procedure was based on general learning procedures [44] and the case of application to RORC severity [45]. It is a differentiated method from the general accident event count prediction, wherein the three classes of RORC severity use conditional mutual information (severity impact variable). When this is defined in terms of a function, it can be expressed as Equation (2). This function is an approximation of the information that \(X_{j}\) provides about \(X_{i}\) (and vice versa) when the value of C is known:  
\[I_{P}\big{(}X_{i};X_{j}C_{i}\big{)}=\sum_{x_{i\mu},x_{j\mu},x_{i}}P\big{(}x_{i \mu},x_{j\mu},c_{i}\big{)}\log\frac{P\big{(}x_{i\mu},x_{j\mu}|c_{i}\big{)}}{P \big{(}x_{i\mu}|C_{i}\big{)}P\big{(}x_{j\mu}|c_{i}\big{)}} \tag{2}\]  
where,  
Figure 3: Typical structures of (**a**) NBN and (**b**) TAN.  
\(I_{P}\): Conditional mutual information  
\(x_{ii}\): \(i\)th state of severity impact variable \(X_{i}\)  
\(x_{ii}\): \(i\)th state of severity impact variable \(X_{j}\)  
\(c_{i}\): \(i\)th state of RORC severity class variable \(C_{i}\).  
1. Compute \(I_{P}\) between each pair of severity impact variables.
2. Create a maximum weighted spanning tree.
3. The resulting undirected tree is transformed to a directed tree by choosing a root variable from the attribute variables and setting the direction of all edges to be outward from it.
4. Construct a TAN model by adding a vertex labeled by the class variable C and adding an arc from C to each \(X_{i}\).
5. Estimate the conditional probability of each variable/node through a gradient descent approach [22].
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 2 The _WeakHEO_ Receiver Architecture
### Geometric Dilution of Precision (GDOP) and Ranging Errors  
As described in [24] in detail, the achievable navigation accuracy at Moon altitude is strongly limited by a very poor relative geometry between the receiver and the transmitters (very large GDOP values) and large ranging errors. For instance, at Moon altitude, simulations in [26] show GDOP values much higher than 1000 for an acquisition sensitivity of 15 dB-Hz. Due to the very low carrier to noise ratio \(C/N_{0}\) values, when processing GPS L1 C/A signals, the code tracking thermal range jitter \(\sigma_{IDLL}\) can achieve values larger than 6 m [24]. A detailed characterization of GDOP and ranging error for different sensitivity values and different GNSS signals is provided in [24].
## 5 MIMO Diversity Analysis
### Meg  
The MEG values are calculated referring to Equation (4) [26] by considering cross-polarization ratio (XPR) = 0 dB for indoor applications and XPR = 5 dB for outdoor applications under a fading environment.  
\[\text{MEG}=\int_{-\pi}^{\pi}\int_{0}^{\pi}[\frac{\text{XPR}}{\text{XPR}+1}G_{ \theta}(\theta,\ \phi)P_{\theta}(\theta,\phi)+\frac{1}{1+\text{XPR}}G_{\Phi}(\theta,\ \phi)P_{\phi}(\theta,\ \phi) \tag{4}\]  
where, in Equation (4), \(G_{\theta}\), \(G_{\phi}\) are power gain patterns of antenna elements, and \(P_{\theta}\), \(P_{\phi}\) are the available power in vertical and horizontal polarization, respectively. The _XPR_ is the cross-polarization ratio and is expressed as in Equation (5):  
\[\text{XPR}=10 log_{10}(\frac{P_{ppa}}{P_{hpa}}) \tag{5}\]  
where \(P_{ppa}\) and \(P_{hpa}\) represent the power received by a vertically polarized antenna and horizontally polarized antenna, respectively. The calculation of MEG signifies the effect of total efficiency, gain, and wireless medium to measure the antenna channel mismatch across the desired operating band of interest. For good diversity performance and power balance, the ratio of MEG of antenna i/MEG of antenna j should be below \(-3\) dB. The calculated MEG values are articulated in Table 1, where it is easily confirmed that all the MEG values are well below \(-3\) dB.
# Performance of Solar Control Films on Building Glazing: A Literature Review
## 2 Window Films  
Window films, composed of transparent, tinted or metalized laminated polyester layers, are designed to be installed in glazing systems to change their thermal and solar optical properties and they usually increase their performance by reflecting or absorbing part of the incident solar radiation. This section briefly introduces window films, describing a typical layout and listing the different types of films and the main solar optical and thermal properties used to characterize these materials. The background information gathered in this section helps to better contextualize solar control films (the solution focused on in the present study).  
The first window films were developed in the 1960s. They had the primary purpose of balancing heat exchanges through glazing by blocking radiation throughout the entire range of wavelengths of the solar spectrum [18]. Even though this was a significant discovery with a high potential to promote visual and thermal comfort in buildings, drawbacks in terms of reducing outdoor visibility, excessive artificial lighting and heating energy consumption instigated new research and product development.  
With the improvement and development of the concept of a window film, the demand for colored films that could provide privacy and sun control, while complementing the architectural design of the building, increased.  
In the 1970s, when the industrial revolution was at its peak, new products and materials that improved indoor thermal comfort and energy efficiency in buildings were investigated and developed. The incorporation of polyester membranes in window films made it possible to manufacture films with higher energy efficiency for cold climates, due to the higher absorption coefficient and reradiation of long wave infrared heat, thus reducing heat losses towards the outdoor environment without decreasing the visible transmittance through the glass and film system [18].  
Metalized films, which had a metallic coating that reflected solar heat and UV radiation, were introduced in the 1990s.  
More recently, innovative ceramic window films were introduced to the market. These films have a significantly high performance, blocking up to 99% of UV radiation and efficiently decreasing glare and solar gains. Ceramic films have a higher resistance to corrosion than other window films.
## 2 Effect of Spoofing Signals on Acquisition
### Signal Power Transmission Loss Model  
According to the free-space propagation theory of satellite signals [27], if the transmit power of the spoofing signal transmitted by the spoofing device is \(P_{T}\) (unit: dBW), the gain of the transmitting antenna in a certain direction is \(G_{T}\), and the corresponding gain of the target receiver of the receiving antenna at \(R\) is \(G_{R}\), \(\lambda\) is the signal wavelength, \(d\) is the spatial distance between the spoofer and receiver, and the power of spoofing signal received by receiving antenna is \(P_{R}\). The link power budget equation expressed in decibels is:  
\[P_{R}=P_{T}+G_{T}+G_{R}+20\lg\left(\frac{\lambda}{4\pi d}\right) \tag{14}\]  
Figure 3: Relationship between total power of spoofing signal and acquisition probability.  
where \(20\lg\left(\frac{\lambda}{4\pi d}\right)\) is the free space propagation loss, and signal received power, \(P_{R}\) reflects the absolute strength of signal.  
In the process of the spoofing, the power needs to be adjusted in real time according to the relative position change of the spoofer and target receiver, such that the signal power received by receiver is maintained within a certain range.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 4 Results
#### 4.1.2 Scenario 8 (WE Separated)  
In this case, the best configuration according to Figure 5 is 13, corresponding to PS 100, CP 1.0, and MP 0.10.  
The Bergmann-Hommel test adjusted \(p\)-values compared to case 13 are shown in Table 9 gives. The first 18 configurations perform significantly worse than case 13, for the remaining 8, the null hypothesis (critical value of \(\alpha=0.05\)) of having similar performance.  
Bonferroni test produces the adjusted p-values presented in Table 10. According to the critical value \(\alpha=0.05/27\), the first six configurations perform significantly worse than case 13.  
Figure 17 presents the non dominated front for median run for the same configurations (13, 4, and 22).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Case** & **P Bergmann–Hommel** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 9: Adjusted \(p\)–values Bergmann–Hommel procedure for scenario 8 (ref. C13).  
Figure 17: Non dominated fronts for configurations 13, 4, and 22 (median run) in scenario 8.  
The results of the multi-objective optimization are illustrated in Figure 18, where three trajectories corresponding to individuals from the final accumulated non dominated front optimization (Figure 19) are shown. As explained previously, trajectories 1 and 3 are presented for illustrative purposes, while trajectory 2 shows the closest to safety radius solution.  
Compared to scenario 1, the optimization process is more restricted here, and the level of significant differences between algorithm variants results is reduced.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Case** & **[PERSON]** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 10: Adjusted \(p\)–values Bonferroni procedure for \(\alpha=0.05/27\) (scenario 8)—best case C13.  
Figure 18: Selected trajectories for scenario 8 (1: best distance to end; 2: best distance to end, and obstacle; 3: best distance to obstacle.)  
The optimized variable values (incremental headings) corresponding to the individuals and trajectories previously illustrated for this scenario are detailed in Table 11.
## 2 Numerical Methodology  
### Geometry Model  
The total size of the heating surface was 72 mm \(\times\) 50 mm, and eight 3 mm \(\times\) 3.5 mm chips were arranged along the central line of the length direction. The chips were millisecond-pulsed heat sources, and pulse period was 4 ms, peak heat flux was 600 W/cm\({}^{2}\) with a cycle duty of 0.25. The average heat flux in other areas was about 1.2 W/cm\({}^{2}\). A heat sink combining the confined jet impingement with metallic PCM was proposed for the cooling of the local high heat flux of millisecond pulses. The metallic PCM was embedded between the contact surface of chips and the heat sink to reduce the temperature rise under the high heat flux pulses using the heat storage capacity of phase change. The jet impingement was used to remove the heat from the PCM.  
Numerical models were established to study the cooling performance of the heat sink under the millisecond heat pulses. Firstly, a three-dimensional (3D) jet impingement model without a PCM layer was built, and the effects of geometrical parameters on the heat transfer characteristics were studied. The heat pulses were averaged over a cycle as a constant heat source. Considering the symmetry of the heat sink structure, 1/4 of the jet structure was selected as the simulation domain with a footprint area of 25 mm \(\times\) 4.5 mm. As shown in Figure 1a, the gray part is the solid region (aluminum), and the blue part is the fluid region (glycol solution with a freezing point of \(-65\)\({}^{\circ}\)C). A transient model was constructed to study the temperature responses of the heat sink under millisecond heat pulses and the effects of metallic PCM thickness. The simulation of the transient phase change model greatly increases the computational cost. To reduce the calculation time, the 3D model was simplified to a two-dimensional (2D) axisymmetric model as shown in Figure 1b and its validation is presented in Section 3.2.1. The red part shown in Figure 1b is metallic PCM and its area covers the entire chip with high heat flux.
## 2 Materials and Methods
### Measures  
The patients completed three questionnaires. First, participants filled out the LOE-DEQ, which requires to indicate whether or not (with a yes/no response) they had experienced specific traumatic events, allowing us to obtain an overall frequency score ranging from 0-8 for DBPE, 0-5 for DDP, 0-3 for ODDE and 0-7 for GTE.  
Secondly, participants answered the Modified Dental Anxiety Scale (MDAS) [10]. The MDAS is a 5-item scale measuring dental anxiety and responses are scored from 1 to 5, producing a total score ranging from 5 (not anxious) to 25 (extremely anxious). The cut-off point of 19 is considered indicative of a high level of dental anxiety. The alpha coefficient for the current sample was 0.93.  
Thirdly, participants answered the revised version of the Dental Beliefs Survey (DBS-R) [11]. The DBS-R is a 28-item scale assessing the patient's perception of the dentist and the dental treatment through 3 subscales: (1) Professionalism (P), which assesses technical competence (i.e., whether or not dentists seem to make therapeutic decisions based on the best interests of the patient rather than their own best interests); (2) Communication (C), which assesses the ease of communication between the patient and dentist, and the attitude of the dentist towards the patient; and (3) Lack of Control (LC), which assesses feelings of control or helplessness and the patients' opinions about the possibility or not of being able to take a break during the treatment. Responses are scored from 1 (never) to 5 (nearly always), and higher scores on each subscale indicate greater negative beliefs. The alpha coefficients for the current sample were 0.86, 0.92, and 0.91, for the three subscales, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Variable**} & **Patients (\(N\) = 253)** \\  & **\(n\) (\%) or M \(\pm\) SD** \\ \hline Age (years) & \\ Mean & 49.1 \(\pm\) 16.4 \\ Range & 18–80 \\ \hline Gender & \\ Male & 124 (49) \\ Female & 129 (51) \\ \hline Education & \\ \(\leq\)High school & 213 (86.2) \\ \(\geq\)College & 34 (13.8) \\ \hline Recovery & \\ Day hospital & 239 (94.5) \\ Ordinary & 4 (1.6) \\ Pre-recovery & 10 (4) \\ \hline Modified Dental Anxiety Scale & \\ Mean & 14.3 \(\pm\) 5.8 \\ Range & 5–25 \\ \(\geq\)19 * & 71 (28.7) \\ \hline \hline \end{tabular} Note: SD = Standard Deviation; * Cut-off indicative of a high level of dental anxiety.  
\end{table}
Table 1: Descriptive statistics of participants.
## 5 Experiments and Evaluation  
In this section, we outline the experiments conducted to evaluate the performance of the proposed CM-DANA for anomaly detection in smart communication environments. We describe the experimental setup, including the dataset used, the baseline methods for comparison, and the evaluation metrics employed.
## 2 Results and Discussion
### Electronic Properties: UV-Visible Absorption Spectroscopy  
The absorption spectral data recorded in DMSO as solvent for the spiro[pyrrolidin-2,3-oxidoles]**5** are summarized in Table 3. As representative example, the superposition of the electronic absorption spectra of **5c**, **5g** and **5h** using a dilute concentration around 10\({}^{-5}\) M are depicted in Figure 5.  
The broad absorption band with \(\lambda_{\text{abs}}\) in the range 290-340 nm can be assigned due to \(\pi\)-\(\pi^{\ast}\) transitions. Additionally, in the case of _p_-hydroxyspiro[pyrrolidin-2,3-oxinololes **5f-h** and **5n-p**, the appearance of a second high intensity band within the 409-454 nm region is noticeable. The intense absorptions can be attributed to an intramolecular charge transfer (ICT) transition involving to hydroxy group [44] (Figure 5) and (Table 3).  
This CT-band is red-shifted for compounds **5h** and **5o**, where the methoxy group is attached at 3,5-positions. The bathochromic effect on the charge transfer band is mainly due to the electron-withdrawing propensity of OCH\({}_{3}\)in an inductive sense [45]. In contrast, a hypsochromic shift is noticed for _p_-hydroxyspiro[pyrrolidin-2,3-oxinololes **5h** and **5p** bearing promo substituents.  
The optical gap (E\({}_{\text{gap-op}}\)) refers to the energetic separation between the HOMO and the LUMO frontier orbitals of the spiro pyrrolidine oxidoles. To determine the optical  
Figure 5: UV-visible absorption spectra in DMSO (c = 10\({}^{-5}\) M) at 298 K of compounds **5c**, **5g** and **5h**.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{\(\lambda_{\text{max}}\) **(nm)**} & \(\lambda_{\text{abs}}\) **(nm)** & **(log(\(\epsilon\))** \\  & & & M\({}^{-1}\)**cm\({}^{-1}\))** & **Eg (eV)** \\ \hline  
** & 298 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: UV-vis maxima and optical gap (eV) for series **5**.  
bandgap, we used the UV-visible spectrum, employing the Tauc plot method [46]. The E\({}_{\rm gap}\) values in DMSO solution are 2.44 eV and 2.69 eV for **5i** and **5p**, respectively (Table 3).
## 3 Results
### The Second-Order Nonlinearity  
In the process of thermal poling, the internal electric field induced by thermal poling in the core center increases first and then decreases with the poling time. When the internal electric field reaches a maximum, the second-order nonlinearity layer covers the core center.  
Figure 4 shows that the internal electric field in the fiber F1 (green) core center reaches a maximum about 450 s earlier than that in the fiber F2 (green) core center. And the maximum of the internal electric field induced in the fiber F1 is \(0.8\times 10^{8}\) V/m higher than that in the fiber F2. The induced second-order nonlinear coefficient here is measured through the \(\chi^{(2)}_{\text{eff}}=3\chi^{(3)}\cdot\text{E}_{\text{rec}}\) where the value of \(\chi^{(3)}\) is assumed to be invariant and equal to \(2\times 10^{-15}\) m\({}^{2}\)/Vs [19]. The maximum of the \(\chi^{(2)}_{\text{eff}}\) in the center of the fiber F1 (black) is 0.05 pm/V larger than that in the center of the fiber F2 (red) is show in Figure 5.  
Figure 4: Time evolution of the internal electric field at the core for fibers F1 (**a**) and F2 (**b**); Different color data line comes from sampling points in different locations, 2 \(\upmu\)m (black), 1 \(\upmu\)m (red), and 0 \(\upmu\)m (green) from the left side of the core center.  
On the entire core, the \(\chi^{(2)}_{\text{eff}}\) of the fiber F1 after poling of 5420 s is totally higher than that of the fiber F2 after poling of 5870 s, as shown in Figure 6a. At 5420 s, the \(\chi^{(2)}_{\text{eff}}\) at the center of the fiber F1 core reaches a maximum, and the 2D map of the effective second-order nonlinear coefficient distribution on the entire core at this time is shown in Figure 6b. The left half of the core is yellow, wherein the brightest color near the center of the core indicates that the effective second-order nonlinear coefficient is the highest. However, the right half of the core is blue, mainly because the second-order nonlinearity layer does not cover this part.  
In the early research on the thermal poling of double-hole optical fiber, the maximum of the \(\chi^{(2)}_{\text{eff}}\) induced was 0.2 pm/V [20]. Our simulation results show that changing the structure of the holes affects the thermal poling while keeping other conditions consistent. It can be considered that under the condition of poling voltage of 5 kV and poling temperature of 285 \({}^{\circ}\)C, the \(\chi^{(2)}_{\text{eff}}\) induced in the fiber F1 core center is about 1/5 higher than that in the fiber F2 core center. The reason for this is that the edge of the hole near the core in the fiber F1 is generally closer to the core than F2, and its single rounded edge causes ions to aggregate and have a higher concentration.
## 6 Methodology and Experimental Setup
Based on Figure 7, we can summarize the procedure of SPEA2 as follows: the algorithm begins the procedure by initializing the packet payload value, which is varied in the range of 0 to 114 bytes based on the IEEE 802.15.4 data frame. After that, SPEA2 uses the first population to evaluate the objective functions to minimize the network end-to-end delay and end-to-end latency and also maximize the network throughput and energy efficiency. Hence, the algorithm uses the values of the fitness function to perform the selection operator. After selection, the algorithm generates the mating pool. This pool is a set of population that both mutation and crossover operations are applied on them in order to generate a new population. At the end, the algorithm checks the number of iterations, which in case it reaches the maximum generations (the value of 250 generations as in Table 9), the algorithm will terminate, and else, the algorithm will repeat the previous steps. Like OMOPSO, NSGA-II and SPEA2, MOSFET begins the procedure by initializing the packet payload size parameter, which is varied in the range of 0 to 114 bytes based on the IEEE 802.15.4 data frame (see Figure 8). After that, the algorithm archives the required number of winners. Then, it groups the winners based on the crowding operation. In the case of the size of the winners is greater than the defined maximum size of winners, the algorithm keeps the best winners and eliminates the other  
Figure 8: Procedure of the MOSFET algorithm. winners. Hence, it applies the velocity update rule on each individual of the population. In addition, it performs mutation to prepare the population for evaluation. In the evaluation part, MOSFP uses the population to minimize both the end-to-end delay and end-to-end latency of the network and to maximize both the energy efficiency and packet throughput. The algorithm changes the fitness value of each individual just in the case of the new fitness value of the individual is better than the old one. Later on, the algorithm updates the set of winners, after that, it performs archiving and crowding operators on the winners. At the end, if the number of maximum generations is not reached (the value of 250 generations as in Table 9), the algorithm will repeat the prior steps, else, the algorithm will terminate.
## 3 Results
### Corrosion Test  
Figure 9 shows the surface changes in the samples according to the salt-spray time. Based on 5% of the surface area of the red rust, GI developed red rust after 70 h. For the NF sample, the occurrence time of red rust (170 h) was slower than that of the GI specimen. However, the red rust area increased rapidly owing to the surface defects, such as bare spots. The red rust occurrence time of SF30 was >500 h, demonstrating its excellent corrosion resistance, which was approximately eight times higher than that of GI. A high-quality Zn-1 Mg-1 Al alloy layer was formed using the SnCl\({}_{2}\)-added flux, and Mg\({}^{2+}\) and Al\({}^{2+}\) cations dissolved from the intermetallic compounds, such as Zn/MgZn\({}_{2}\) and Zn/Al/MgZn\({}_{2}\), in the coating layer precipitated in the cathode region on the surface of the coating layer to form dense and stable corrosion products (e.g., Simonkolleite).  
Figure 8: T-bending test images of the Zn–1 Mg–1 Al hot-dip-coated steel with respect to the flux composition: (**a**) normal flux, (**b**) 10% SnCl\({}_{2}\), (**c**) 20% SnCl\({}_{2}\), (**d**) 30% SnCl\({}_{2}\), and (**e**) 40% SnCl\({}_{2}\).