esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analytical Methods  
The X-ray diffraction (XRD) pattern of ZnO NPs was recorded using a PANalytical PW3050/60 X-ray diffractometer equipped with Cu-K\(\alpha\) radiation source (\(\lambda\) = 1.5406 A) operated at 40 kV and 30 mA. Transmission electron microscopy (TEM; Philips CM120), field emission scanning electron microscopy (FE-SEM; Mira3 Tescan), energy-dispersive X-ray spectroscopy (EDX; Mira3 Tescan), and X-ray photoelectron spectroscopy (XPS; Thermo Scientific K-Alpha) were employed to investigate the morphology and topography of the samples along with elemental chemical surface analysis.  
The FE-SEM instrument was operated at an accelerating voltage at 10 kV. The synthesized NPs were subjected to UV, Fourier-transform infrared (FTIR; range 4000-400 cm\({}^{-1}\)), and photoluminescence (PL; range 200-800 nm) spectroscopy (UV-visible spectrophotometer, PerkinElmer and Cary fluorescence spectrophotometer) to survey the optical properties, functional groups, and structural defects. An electron paramagnetic resonance (EPR) investigation on a Bruker ELEXSYS E500 X-band (\(\sim\)9.45 GHz) spectrometer was conducted to study the paramagnetic defects in the structure. The GC-MS analysis of the plant extract was conducted using an Agilent 7890 A instrument under computer control at 70 eV that comprises an AOC-20i auto-sampler and a gas chromatograph interfaced to a mass spectrometer (GC-MS) equipped with an Agilent 5975 C fused capillary column (30 \(\times\) 0.25 \(\upmu\)m ID \(\times\) 0.25 \(\upmu\)m df). For GC-MS detection, an electron ionization system was operated in electron impact mode with an ionization energy of 70 eV. Helium gas (99.999%) was used as a carrier gas at a constant flow rate of 1 mL/min, and an injection volume of 2 \(\upmu\)L was employed (a split ratio of 10:1). The injector temperature was maintained at 260 \({}^{\circ}\)C. The ion-source temperature was kept at 270 \({}^{\circ}\)C. The oven temperature was programmed from 40 \({}^{\circ}\)C with an increase rate of 3 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C. Surface potential was investigated using a Malvern Zetasizer Nano-series (Malvern Instruments Zen 3600, Malvern, UK)
# A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals
## 2 Literature Review
### The Evolution of Artificial Intelligence as a Research Area in the Communication Field
#### 2.1.2 AI and Advertising  
The advertising landscape has experienced a profound transformation through the integration of AI. Marketers now harness AI's data analysis capabilities to gain insights into consumer behavior, preferences, and trends [27]. This empowers them to craft highly targeted and impactful advertising campaigns. AI algorithms can sift through consumer data derived from diverse sources, including social media, online browsing habits, and purchase history. This extensive analysis allows for advertisers to customize their messages, reaching specific demographics and even individual consumers. Consequently, advertising has evolved to become more cost-effective and efficient, ensuring marketing endeavors reach their intended audiences with enhanced precision [28]. In addition, AI has proven invaluable in optimizing ad targeting, refining pricing strategies, and fostering innovative content creation in the advertising industry.
## 5 Discussions and Implications for Decision Making
### Comparison of Level 5 and Level 6 Results  
The average rating of Level 5 (74%) is much lower than that of Level 6 (92%). Although it can be argued that the teaching interventions were based on Level 6 students' feedback (NSS involves only Level 6 students), this is a highly interesting result that will be discussed from several aspects.  
The first question is whether the actions were appropriately implemented at both levels. For finer measurement, we split the questions into three categories: (a) four questions directly asked whether a certain action was useful: Q1 (assessment brief); Q2 (marking criteria explanation); Q8 (using real-life examples); and Q14 (coursework deadline). Level 5 results had an average rating of 91%; (b) four questions related to tutors' supportiveness: Q4 (overall); Q6 (available time); Q17 (expected outcome); and Q18 (improvement). Level 5 results show an average rating of 82%. (c) four questions asked about whether the actions were sufficient: Q3 (feedback); Q9 (example numbers); Q13 (in-class support); and Q16 (in-class check). The Level 5 results show an average rating of 67%.  
The above statistics might indicate that, while the actions were helpful, they should be implemented more frequently or that tutors should improve their overall quality of teaching. However, it should be noted that on the chosen Level 5 module the lecturing hours were reduced during the year from four to three per week yet the module content was unchanged (the department made the decision just before the start of the semester, leaving no time to amend the teaching material). We observed that the reduced teaching time resulted in increasing pressure on students and a weaker understanding (the module average mark dropped from the previous year). In view of this negative issue (it is notable that only 53% students considered that they were satisfied with the module), it is considered that the actions were implemented well, but that their effects were overshadowed. To resolve this issue, action should be taken either to increase staffing or to modify the module.  
The second question to be asked is whether the actions designed efficiently address the issues raised by the students. As mentioned above, the Level 5 ratings on whether the actions are useful were similar to those on the Level 6 modules, yet the overall rating was much lower. Furthermore, Q10 asked whether the real-life examples increased the motivation', to which students at Level 5 gave 55% while those at Level 6 gave 83%. It seems that such an action ('Embedding real-life examples) had no effect on students' learning experience at Level 5. Bear in mind that the actions were designed by analyzing feedback from final-year students (Level 6), who might have had a different perspective from that of the Level 5 students. We consider that the sample data might not be sufficient to draw a conclusion (i.e., whether an action can improve overall satisfaction), but it was clear that to some extent those actions could improve students' experience (i.e., 91% considered the actions were helpful).
## 3 Results & Discussions
### Structural Properties of Cr-Doped NiO Thin Films
Figure 2: Structural properties of the NiO thin films at different doping times; (**a**) XRD pattern; (**b**) comparative studies on the (200) plane peak obtained from XRD pattern. The structural parameters are tabulated in Table 2. The lattice constant of the Cr-NiO thin films increased from 0.4185 nm to 0.4250 nm with increasing doping time from 0 s to 80 s. This result may be attributed to the film grains being strained at a high doping level. Increasing microstrain and dislocation density are observed at high doping times due to the decrease in crystallite size. Microstrain and dislocation density are the major factors that lead to the formation of a dislocation network in the Cr-NiO films. Strain is relaxed with the increase of dislocation density. At high doping times, increasing trend of microstrain imply poor film quality. Dislocation density also increases with deteriorating the film quality. These findings suggest that Cr doping has a significant impact on the crystal orientation and microstructure of NiO thin films. [25]. The combined effect of increased microstrain and dislocation density can be utilized to explain the stacking defect of films increases considerably with Cr doping.
## 5 Results and Discussion
Incorporate a matching network: A low-pass matching network is included to match the amplifier's optimal load resistance \(R\) to the standard 50-\(\Omega\) load of the AOTF. This low-pass network further suppresses harmonics. To validate the proposed methodology and demonstrate the design approach, schematic simulations were conducted using a non-linear device model of the CGH40010 transistor [20]. The transistor is operated at a drain bias voltage of \(-2.45\) V with a gate supply voltage of 10 V for delivering an output power of 30 dBm with a driving input power of 20 dBm. The amplifier is designed to operate at a center frequency of 145 MHz with a span of 40 MHz. The ideal and optimized values derived from these simulations are presented in Table 4.  
Figure 13a shows that the designed RFPA achieves an efficiency greater than 90% across the frequency range, resulting in a bandwidth of 27.59%, which closely aligns with  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \(R\) & \(L\) & \(C\) & \(L_{a2}\) & \(L_{p2}^{L}\) & \(C_{p2}^{L}\) & \(C_{0}\) & \(L_{3}\) & \(C_{3}\) \\  & **(\(\Omega\))** & **(nH)** & **(pF)** & **(nH)**  
the predictions from ideal simulations where a 30% efficiency was achieved. Figure 13b and Figure 13c illustrate the output power and harmonic levels, respectively. The designed RFPA achieves a maximum output power of 29.779 dBm, which closely aligns with the specified design goals. The harmonic levels are maintained below \(-25\) dBc across the bandwidth. Additionally, the suppression of the 2 nd harmonic is greater than that of the third harmonic, which aligns with the expected behavior of the proposed topology, as demonstrated by the ideal simulation results provided in Table 3. Finally, the schematic for the first-order low-pass matching circuit, along with the filter response, is presented in Figure 14, demonstrating additional suppression of harmonics. The transistor is capable of delivering a maximum power of 40 dBm, as specified in the datasheet. In this case, the biasing voltage can be set to 28 volts, and the circuit parameter values can be recalculated accordingly to ensure optimal performance and meet the design specifications. Additionally, since the typical gain of this transistor is around 10 dB, an input power of 30 dBm is required to drive the transistor into saturation. Figure 14: (**a**) Schematic for the matching network (\(R=120.8211\)\(\Omega\), \(L_{1}=65\)\(nH\), and \(C_{1}=11\)\(pF\)), (**b**) filter response from the ADS simulation. Figure 13: Simulation results for the RFPA design at 145 MHz delivering 1 watt of output power. (**a**) Efficiency (%), (**b**) output power (dBm), and (**c**) harmonics (dBc).
## 5 Results and Discussion
### Results  
Table 2 presents the MAPE values for all forecasting models evaluated on selected dates, providing a detailed comparison of their performance across various scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**MAPE (\%) Values on Selected Dates**} & \\
**Methods** & **14.02.2023** & **25.03.2023** & **19.05.2023** & **29.07.2023** & **09.08.2023** & **30.08.2023** & **14.09.2023** \\ \hline MLP & 18.47 & 15.21 & 11.51 & 12.00 & 10.37 & 18.04 & 15.02 \\ LSTM & 13.57 & 13.43 & 10.85 & 8.01 & 9.44 & 16.50 & 18.47 \\ Bi-LSTM & 13.04 & 14.40 & 21.96 & 13.65 & 11.62 & 19.37 & 13.26 \\ ConvLSTM & 13.43 & 16.55 & 10.92 & 7.12 & 8.25 & 16.89 & 15.28 \\ ARIMA & 18.75 & 19.28 & 11.27 & 8.59 & 9.30 & 19.91 & 19.72 \\ SARIMA & 18.03 & 19.36 & 10.55 & 8.96 & 9.62 & 20.72 & 19.11 \\ SVM & 15.52 & 16.72 & 11.14 & 8.72 & 7.33 & 15.88 & 14.44 \\ SVM—mRMR & 14.35 & 14.85 & 10.64 & 9.25 & 6.77 & 16.25 & 13.75 \\ SVM—MLFS & 12.01 & 15.62 & 9.99 & 8.95 & 7.05 & 15.92 & 14.62 \\ XGBoost & 7.72 & 11.76 & 12.09 & 5.22 & 5.08 & 14.65 & 10.95 \\ XGBoost—mRMR & 10.31 & 11.45 & 7.69 & 4.42 & 6.12 & 13.95 & 11.62 \\ XGBoost—MLFS & 8.91 & 13.21 & 5.87 & 4.71 & 5.11 & 12.40 & 10.52 \\ ELM & 16.45 & 13.47 & 11.48 & 8.12 & 6.84 & 15.07 & 12.10 \\ ELM—mRMR & 13.57 & 11.81 & 9.08 & 7.67 & 5.23 & 14.11 & 11.17 \\ ELM—MLFS & 12.09 & 12.47 & 9.66 & 6.97 & 6.29 & 14.20 & 11.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MAPE values of all models.  
Figure 1 illustrates the performance of various statistical and machine learning methods in predicting the hourly SMP for the Turkish electricity market, showcasing the average MAPE values derived from predictions made over seven distinct days.
## 5 Experimental Implementation
### Usage  
The user's experience with the system starts with the graphical interface. The GUI allows the user to set up the program and helps them familiarize themselves with the operation. Even if Equation (4) results in a large number of sequences and modes, there are often far less dimensions to manage, in practice, since most of ATs do not have more than 15 modes to control. Thus, the system stays easy to use. The user's first steps with the interface (with the GUI) are as follows:  
1. Record a first gesture (i.e., execute the gesture five times to train the algorithm).
2. Define a first sequence.
3. Map the sequence with a mode of the computer's keyboard or mouse.  
After these three steps, the user is ready to control the selected modes of the computer. To do so, they:  
1. Perform the sequence associated with the desired mode.
2. When in the mode, use the switch to execute the mode (e.g., mode: mouse-up implies that the cursor will go up on activation of the switch).
3. When done, wait two seconds. The mode is left.
4. Perform a new sequence.  
With the graphical interface, at any time, the user can also:  
* Record or delete gestures.
* Create, edit or delete sequences.
* Edit the mapping between the sequences and the modes.
* Adjust the system sensitivity (the thresholds that trigger the gesture recognition algorithm as discussed in Section 3).
* View the current mapping.
* Have visual feedback of the current sequence and the individual actions performed.  
Not having a large dataset has many disadvantages, but it allows the algorithm to train very quickly. This advantage is used every time the gestures or sequences change. When new gestures are added or removed, the recognition model is trained again in a matter of seconds. Additionally, the algorithm verifies which recorded gestures were used by the mapped sequences and only trains or re-trains the model on relevant gestures. This allows the classifier's training to be optimal on the significant data.
# Switchable Fiber Ring Laser Sensor for Air Pressure Based on Mach-Zehnder Interferometer
## 2 Principle of Operation and Its TCFMI Pressure Analysis
was used to avoid feedback from the system; between these elements, 120 m of 1064 nm single-mode fiber was set. Finally, the gas cell contained the interferometer interacting with the pressure inside it, thus changing the output interference pattern; the fiber laser system response was analyzed and visualized using an optical spectrum analyzer (OSA), YOKOGAWA AQ6370B (Tokyo, Japan). This setup was the base of the pressure ring fiber laser cavity. The gas cell was made using a metal tube with two gas inlet taps and an analogous manometer in the upper part of the tube from which the information on the pressure inside the cell was collected. At the ends are two metal covers with thick glass and a small hole in the center of this where the optical fiber passed through. Air was introduced to the cell using a small air compressor to put pressure on the interferometer, and thus, obtain the interference patterns according to the pressure change inside it, which ranged from 0 to 40 PSI. Figure 2 shows the interference transmission spectrum as the ASE spectrum was generated. An air compressor controls the pressure inside the chamber; the interference pattern is altered as the pressure changes. Figure 4 shows power and wavelength variations as the air pressure increases. Here, several regions display linear power decrements. The measurements were made in a range from 0 to 40 PSI, using a 5 PSI resolution. The interference pattern shifted toward longer wavelengths as indicated by the direction of the red arrows in Figure 4b, as a result, the wavelength peak changed from 1074 to 1080 nm, as shown (see Figure 4b). Figure 5 represents the change in wavelength of the signal obtained from the ASE noise of the ytterbium-doped fiber by increasing the pressure inside the cell where the TCMFI is located. The graph in Figure 4b was obtained with a constant power output (approx. \(-\)50 dBm) for pressure values of 0, 5, 10, 20, 25, 30, and 40 PSI; an approximate redshift of 3.5 nm is observed from the minimum to the maximum air pressure. As a result, the TCMFI exhibits a sensitivity of 0.0925 nm/PSI in the 0-40 PSI range. Figure 3: Experimental setup to characterize the TCFMI as its surrounding pressure was modified. Figure 4: (**a**) Spectrum for different air pressure values and (**b**) spectrum for different air pressure values in the range from 0 to 40 PSI. Figure 5: Graph of the displacement of the wavelength with increasing air pressure in signal of ASE of YDE.
## 2 Statistical Distribution
### Weibull Distribution  
The Weibull distribution typically uses two or three parameters. Equation (5) expresses the reliability function of the Weibull distribution.  
\[R(t)=exp\{-\bigg{(}\frac{t-\gamma}{\eta}\bigg{)}^{\beta}\}, \tag{5}\]  
where \(\eta\), \(\beta\), and \(\gamma\) represent the scale, shape, and location parameters, respectively. Similar to that in an exponential distribution, when only two parameters are used, the location parameter becomes zero. The Weibull distribution is widely used in lifetime data modeling because it can represent all regions of the bathtub curve based on the modification of the shape parameter, with \(\beta>1\) for the wear-out region, \(\beta=1\) for the CFR region, and \(\beta<1\) for the initial failure region. Additionally, \(\beta\) can be used to assess the consistency of the failure mechanism.
### Classification Performance with Statistically Weighted Dimension and Dimensionality Reduction (PCA)
Additionally, it does not apply or use the statistically weighted algorithm due to the weighting feature which contains \(\textbf{ACSW}=0\), \(\textbf{ICSW}=0\), and \(\textbf{SDW}=0\). Firstly, the observation of the proposed statistically weighted dimension with the PCA scheme classifies the image more effectively than BASE-LINE. Table 4 clearly shows that the combinations of weighting are all superior to the baseline. Especially in our proposed idea, we suppose the numbers that are derived from counting the superior weighting combinations in the experiment (4): ((2) + PCA) optimal statistically weighted work with PCA (our proposed idea)) compare with the baseline on 125 different weighting combinations. This demonstrates the impact of three datasets (MNIST, E-MNIST, and F-MNIST) on each classification method. The number of superior statistically weighted combinations includes the logistic regression algorithms, which vary by 35 for MNIST, 10 for E-MNIST, and 61 for F-MNIST; the KNN algorithm, which varies by 51 for MNIST, 30 for E-MNIST, and 41 for F-MNIST; and the SVM (RBF) algorithm, which varies by 27 for MNIST, 2 for E-MNIST, and 21 for F-MNIST. The best combination of statistically weighted dimension features without PCA for logistic regression, KNN, and SVM (RBF) are all superior to the BASE-LINE with an average gap of 0.60% (varying by 0.17% of MNIST, 1.03% of E-MNIST, and 0.60% of F-MNIST), 1.40% (varying by 0.41% of MNIST, 2.95% of E-MNIST, and 0.83% of F-MNIST), and 0.27% (varying by0.10 % of MNIST, 0.24% of E-MNIST, and 0.47% of F-MNIST), respectively. Furthermore, the best combination of statistically weighted dimension features with PCA for logistic regression, KNN, and SVM (RBF) are all superior to BASE-LINE with an average gap of 0.48% (varying by 0.08% of MNIST, 0.92% of E-MNIST, and 0.43% of F-MNIST), 1.48% (varying by 0.44% of MNIST, 2.84% of E-MNIST, and 1.17% of F-MNIST), and 0.57% (varying by 0.05% of MNIST, 0.84% of E-MNIST, and 0.81% of F-MNIST), respectively. However, the demonstrated results also show that the PCA can reduce the number of components or dimensionality by more than 50%, with the average value decreasing from BASE-LINE for (3, see Table 4) PCA with BASE-LINE weighting, and (4, see Table 4) PCA with optimal statistically weighted dimension at 55.74% (varying from 35.07% for SVM(RBF) on F-MNIST to 70.41% for KNN on E-MNIST), and 52.42% (varying from
# Computing Maximal Lyndon Substrings of a String
## 4 The Algorithm _Bsla_
### The Refinement
Therefore, \(\boldsymbol{x}[j_{1}-1]\succ\boldsymbol{x}[j_{1}]\), and so, \(\mathit{con}^{\prime}(gr^{\prime}(j_{1}-1))=\boldsymbol{x}[j_{1}-1]\). Thus, there is \(j_{2}\), \(i+|\mathit{con}^{\prime}(H_{t+1})|\leq j_{2}<j_{1}\leq k\), and \(\mathcal{C}^{\prime}(j_{2})\) is a suffix of \(\boldsymbol{x}[i..j_{1}-1]\). Take the smallest such \(j_{2}\). If \(\mathcal{C}^{\prime}(j_{2})\prec\mathcal{C}^{\prime}(j_{1})\), then by \((then we have a contradiction, so \(i+|con^{\prime}(H_{t+1})|<j_{3}\). These arguments can be repeated only finitely many times, and we obtain \(i+|con^{\prime}(H_{t+1})|=j_{\ell}<j_{\ell-1}<...<j_{2}<j_{1}\leq k\) so that \(\boldsymbol{x}[i..k]=\mathcal{C}^{\prime}(i)\mathcal{C}^{\prime}(j_{\ell}) \mathcal{C}^{\prime}(j_{\ell-1}...\mathcal{C}^{\prime}(j_{2})\mathcal{C}^{ \prime}(j_{1})\), which is a contradiction. Therefore, our initial assumption that \(\mathcal{C}^{\prime}(i)\) is not maximal always leads to a contradiction. This concludes the proof of Claim 4. The four claims show that all the conditions \((C_{1})\)... \((C_{8})\) are satisfied for \(Conf^{\prime}\), and that proves Theorem 3. As the last step, we show that when the process of refinement is completed, all right-maximal Lyndon substrings of \(\boldsymbol{x}\) are identified and sorted via the contexts of the groups of the final configuration. **Theorem 4**. : _Let \(Conf_{1}=\langle G^{1}_{k_{1}},G^{1}_{k_{1}-1},...,G^{2}_{2},G^{1}_{1}\rangle\) with \(gr_{1}()\), \(con_{1}()\), \(C_{1}()\), \(prev_{1}()\), and \(val_{1}()\) be the initial 1-complete group configuration from Lemma 9._  
_Let \(Conf_{2}=\langle G^{2}_{k_{2}},[PERSON]^{2}_{k_{2}-1},...,[PERSON]^{2}_{2},[PERSON]^{2}_{1}\rangle\) with \(gr_{2}()\), \(con_{2}()\), \(C_{2}()\), \(prev_{2}()\), and \(val_{2}()\) be the 2-complete group configuration obtained from \(Conf_{1}\) through the refinement by the group \(G^{1}_{1}\)._  
_Let \(Conf_{3}=\langle [PERSON]^{3}_{k_{3}},G^{3}_{k_{3}-1},...,G^{2}_{2},G^{3}_{1}\rangle\) with \(gr_{3}()\), \(con_{3}()\), \(C_{3}()\), \(prev_{3}()\), and \(val_{3}()\) be the 3-complete group configuration obtained from \(Conf_{2}\) through the refinement by the group \(G^{2}_{2}\)._  
_..._  
_Let \(Conf_{r}=\langle G^{r}_{k_{r}},G^{r}_{k_{r}-1},...,G^{r}_{2},G^{r}_{1}\rangle\) with \(gr_{r}()\), \(con_{r}()\), \(C_{r}()\), \(prev_{r}()\), and \(val_{r}()\) be the r-complete group configuration obtained from \(Conf_{r-1}\) through the refinement by the group \(G^{r-1}_{r-1}\). Let \(Conf_{r}\) be the final configuration after the refinement runs out._  
_Then, \(\boldsymbol{x}[i..k]\) is a right-maximal Lyndon substring of \(\boldsymbol{x}\) iff \(\boldsymbol{x}[i..k]=C_{r}(i)=con_{r}(gr_{r}(i))\)._  
Proof. : That all the groups of \(Conf_{r}\) are complete follows from Theorem 3, and hence, every \(C_{r}(i)\) is a right-maximal Lyndon string. Let \(\boldsymbol{x}[i..k]\) be a right-maximal Lyndon substring of \(\boldsymbol{x}\). Consider \(C_{r}(i)\), since it is maximal, it must be equal to \(\boldsymbol{x}[i..k]\).