esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Outlook  
As discussed with the basic evaluation above, the best taxonomic base learners are performing nearly as well as the still leading text-based classifier SVM (see Table 5). This raises the question whether a combination of just strong IPC and CPC-based classifiers, omitting all text features, might boost the overall performance, perhaps even beyond the scores of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**FEATURE**} & \multicolumn{2}{c}{**TEXT**} & \multicolumn{2}{c}{**TEXT**} \\  & & **BPRbottom** & **BPRtop** & **BPRbottom** & **BPRtop** \\ \multicolumn{2}{c}{**Fusion method**} & \multicolumn{2}{c}{**Stacking**} & \multicolumn{2}{c}{**Fixed Combination**} \\ \hline \multirow{4}{*}{**IPC**} & **BPRbottom** & 0.678 & 0.708 & 0,668 & 0.700 \\  & **BPRtop** & 0.745 & **0.761** & 0.723 & **0.732** \\ \cline{1-1}  & **BPRbottom** & 0.667 & 0.732 & 0.660 & 0.689 \\ \cline{1-1}  & **BPRtop** & 0.724 & **0.769** & 0.737 & **0.764** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Averaged F1 scores for the performance condition (Base Performance Ranking, BPR with top or bottom rank) of the four different stacking techniques and the four fixed combinations rules for all ensemble conditions of combining two base learners. Peak values are printed in bold.  
the best text-based learners. Thus, a corresponding preliminary experiment has been conducted: the base learners LogReg (IPC) and ANN (CPC) are combined by fixed combination rules as well as using stacking (see Figure 5).  
Whereas the fusion with FCR provides no improvement, the stacking with both ANN and SVM enhances the overall performance up to an F1 score of 71.2% on the test data. This outperforms the so far best basic performance of a text-based SVM and is completely independent from the language of the written patent information. Considering the PATSTAT patent database, now more than 22 million additional documents, lacking English titles/abstracts, could be included into a technology analysis based on automated categorization. The potential seems to be very large, but beforehand further studies have to evaluate the scalability of our preliminary results to further domains and larger sample sizes.  
Furthermore, modern deep learning approaches such as BERT or ELMo [39; 40], with extensive pre-trained language models including multilingual and domain-specific variants (e.g., BioBERT [41]), show a great potential in semantic tasks and might boost our patent classification also in terms of cross-lingual patent data. However, it remains to be clarified whether the high effort of such complex approaches is also worthwhile in terms of applicability and performance. The results and data of our work might serve as basis for an elaborate evaluation as part of future work.
# Reactance Regulation Using Coils with Perpendicular Magnetic Field in the Tubular Core design
## 4 Results and Discussion
### Controllability  
Figure 8 represents the I-V curve of control winding immersed in liquid nitrogen. For the measurement, the control DC current is changed from 0 to \(\sim\)9400 mA. The calculated resistance of working and control coils are \(\sim\)0.174 \(\Omega\) and 2.74 \(\Omega\), respectively (Figure 8). Copper losses can contribute a significant portion of total ones and interfere with the estimation of ferromagnetic losses. The coil is immersed in liquid nitrogen to minimize resistance influencing the copper losses. It provides the opportunity to increase the magnetic field of the control winding significantly, and the resistance of the working coil remains minimum.  
Figure 7: Equivalent resistance of (**a**) external coil and (**b**) internal coil. Solid lines represent the approximation. The approximation coefficient (\(R^{2}\)) is more than 0.99.  
The impedance and THD% were measured for different AC current of 1A, 2A, and 3A as a function of DC current. The impedance of working winding is shown in Figure 9a. It can be observed that the impedance of the working coil is changed \(\sim\) 6 times for the AC current of 1A and \(\sim\)3 times for the current of 3A. These results confirm the possibility of changing the energy flow to some extent in the distribution grid and a prosumer. The characteristics of the AC current should be verified considering the presence of non-linear magnetization curves of the core. Figure 10 represents AC current shapes for DC control current of 8120 mA and 0 mA. It is observed that the current curve is distorted from a simple sinusoid curve because of the nonlinearity of the magnetization curve. The designer of a power circuit should know these circumstances and consider them in the project development.  
Figure 8: I–V curve of control winding. The resistance is calculated using the I–V curve.  
Figure 9: (**a**) Working coil impedance versus DC control current. (**b**) total harmonic distortion (THD) of a working current depending on DC control current.  
The distorted behavior of the current is assigned to the higher THD coefficient. The THD coefficients for different DC currents are represented in Figure 9b. It is observed that the THD coefficient is higher for the lower control DC current.
## 2 Materials and Methods
### Displacement Analysis: Absolute Positioning  
To evaluate low-cost devices in PPP mode, Survey Calibrated and Tallysman antennas were placed near point FGG1, and static observations were acquired from 1 February to 29 March 2021. In this static test, the antennas did not move. The acquired data were used to define 96 sessions, each of which contained observations for 8 h at 0.25 Hz. Then, a dynamic test was carried out in which the same mechanism was used to impose horizontal movements in steps of 10 mm. In total, seven positions were observed for 24 h, and displacements in the range of 10-60 mm were defined. As in the static test, the duration of the session was 8 h. The IGS-MGEX products, precise ephemeris (15 min, \"sp3\" format), and clock correction (30 s, \"clk\" format) were used. The satellite and receiver phase center variations were determined by using the \"igs.atv\" file from IGS. The Iono-Free LC and Estimated ZTD were used to eliminate the ionospheric error and the tropospheric delay, respectively. The elevation mask was set to 15\({}^{\circ}\), and the DCB products from the Center of Orbit Determination in Europe were also considered.  
For each session in the static test, east (e), north (n), and ellipsoid height coordinates (h) were estimated. The 3\(\sigma\) method was used to detect outliers, and then the RMSE, minimum, and maximum values of coordinate residuals were estimated. In monitoring applications, the movements can be in any direction; thus, we estimated them in the 3D coordinate system. The statistical hypotheses for spatial movements defined in the previous subsection were used to identify the range of detected displacements.
# A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks
## 8 Conclusions  
In this paper, we proposed a novel broadcast algorithm, called the KTFW algorithm, that is more resilient to jamming attacks and is scalable for larger networks. We evaluated the performance of this algorithm through simulations and compared it with the standard BEB and QB algorithms. For each algorithm, the performance was measured in terms of the throughput and average packet latency for various network sizes, traffic injection models, and jamming rates. The results display a higher performance for the KTFW algorithm than for the BEB algorithm against jamming attacks, especially for larger networks. In contrast, the QB algorithm fails to maintain network performance when jamming attacks occur. The resilience of the KTFW algorithm to jamming attacks is reflected in the average packet latency results, where jamming attacks have a marginally lower effect on the average packet latency of the KTFW algorithm than that of the BEB algorithm. The new proposed algorithm could improve the performance, reliability, and scalability of MAC networks. In the future, we will work on investigating methods for implementing the KTFW algorithm in WLANs and replacing the BEB algorithm. Furthermore, we plan to study the performance of the KTFW algorithm for larger network sizes and other types of jamming models, including intelligent jammers. Studying and analyzing the algorithm fairness compared to the other algorithms is another possible direction of the work. It will be interesting to determine how the algorithm performs under other types of traffic injection models.  
Conceptualization, B.A.A.; methodology, B.A.A.; simulation implementation, B.A.A.; analysis, B.A.A.; manuscript writing original draft, B.A.A.; writing--review and editing, B.A.A. and J.H.J.; Supervision, J.H.J. Both the authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 5 Discussion
Adopting [PERSON]'s [24]\(a_{E}\) = 2 value for illustrating purposes, the denominator in Equation (26) equals 0.182 m/MPa (recall Figure 13c). Assume, now, that \(\
u_{eq}\) = 0, \(I_{s}\) = 0.591 and thus \(E_{eq}\) = 16.89 MPa. If \(\
u_{eq}\) = 0.3, \(Is\) = 0.567 and, thus, \(E_{eq}\) = 14.74 MPa. Both solutions (i.e., [16.89, 0], [14.74, 0.3]) are correct as, as mentioned, they produce the same settlement profile. More specifically, from Figure 13c the settlement per unit loading (\(\rho/\!q\)) if \(a_{E}\) = 2 is 0.182 m/MPa. Using [PERSON]'s formula with the equivalent elastic parameters, the \(\rho/\!q\) ratio is also equal to 0.182 for both \(E_{eq}\) = 16.89 MPa, \(\
u_{eq}\) = 0 and \(E_{eq}\) = 14.74 MPa, \(\
u_{eq}\) = 0.3 pair of values. Applying the correction for the footing shape of Equation (17), the \(\rho/\!q\) ratio becomes equal to 0.182/1.3 = 0.140 m/MPa. For the respective square footing with an edge \(B\) = 2.6 m and influence depth equal to 2\(B\), the \(\rho/\!q\) ratio using [PERSON]'s formula is 0.138 m/MPa (no correction is needed). Thus, the same settlement is obtained. Since the settlement of a square footing is approximately equal to the respective circular footing having the same area on plan view (that is, having a diameter \(2B/\sqrt{\pi}\)), the settlement of the \(B\times L\) rectangular footing could be calculated based on the respective circular footing with the use of the equivalent elastic constants of soil and ignoring the correction for footing shape. The following analytical expressions could be used for rigid circular footings on clay and sand:  
\[E_{BxL}=E_{BxB}(1+0.4\log L/B). \tag{16}\]5  
\[E_{BxL}=E_{BxB}(1+0.4\log L/B). \tag{16}\]6For completeness, the settlement at the center of flexible circular footing, which rather corresponds to footing on clay soil, is:  
\[E_{BxL}=E_{BxB}(1+0.4\log L/B). \tag{16}\]7  
As \(H\)\(\rightarrow\)\(\infty\), the term in the square brackets in Equations (27) to (29) tends to unity, thus:  
\[E_{BxL}=E_{BxB}(1+0.4\log L/B). \tag{16}\]8  
\[E_{BxL}=E_{BxB}(1+0.4\log L/B). \tag{16}\]9  
\[E_{BxL}=E_{BxB}(1+\log L/B). \tag{17}\]0  
Thus, from Equation (29) using \(R=B/\sqrt{\pi}=1.47\) m, \(H=4R=5.87\) m and the equivalent elastic constants, the \(\rho\)/\(q\) ratio is 0.142 m/MPa, that is, approximately equal to the 0.138 m/MPa value obtained previously. If the \(B\times L\) footing considered in this example was rigid and on the surface of clay or sand, the \(\rho\)/\(q\) ratio would be 0.105 and 0.173 m/MPa using Equations (27) and (28) respectively. It is reminded that, for comparison purposes, the two materials share the same elastic modulus value. Finally, it is mentioned that the \(E_{eq}=16.89\) MPa, which stands for \(\
u_{eq}=0\), is the proper value for use in a Winkler spring type of analysis, as a Poisson's ratio value equal to zero better represents the deformation pattern of springs.
## 1 Introduction
### Thermal Comfort in Active Classrooms  
Thermal comfort in classrooms is a critical factor influencing not only the health and well-being of students but also their academic performance and cognitive abilities. As students spend a significant portion of their day in educational settings, the indoor thermal environment plays a pivotal role in shaping their learning experiences. The concept of thermal comfort encompasses various environmental parameters, including temperature, humidity, air velocity, and overall indoor air quality. Research has highlighted that students in Mediterranean zones [19; 20] may have different thermal comfort preferences compared to those in continental climates, necessitating tailored approaches to classroom design and ventilation strategies [21; 22; 23].  
Academic performance significantly declines when indoor temperatures deviate from the thermal comfort zone, generally between 20 and 24 \({}^{\circ}\)C [24]. Elevated temperatures may lead to fatigue and reduced concentration, while overly cold environments can cause distractions and discomfort. Additionally, high levels of carbon dioxide (CO\({}_{2}\)), resulting from insufficient ventilation, represent a recurring issue in schools worldwide [25; 26].  
An integrated design approach that simultaneously considers acoustic and thermal requirements is, therefore, essential. Some authors have proposed a \"global comfort\" model that accounts for interactions between multiple environmental parameters, emphasizing that optimizing a single factor in isolation may lead to undesirable trade-offs affecting other aspects of occupants' well-being. This type of approach not only enhances the quality of learning and students' well-being and promotes greater energy sustainability in university buildings. Thus, global comfort in classrooms falls within the broader context of Indoor Environmental Quality (IEQ) [27].  
Emerging technologies, such as IoT-based environmental monitoring systems, represent promising tools for collecting real-time data and supporting evidence-based decision-making [28].
## 2 Materials and Methods
### Measurement Techniques and Approaches  
In order to determine the LIDT, a diameter regression fit method as suggest by _[PERSON]_[35] was applied. This 1-on-1 laser damage testing approach is an easy and distinct method for the determination of the LIDT in the picosecond pulse duration range on the basis of the diameter of ablated spots [36]. Here, the sample surface is sectioned into a number of test sites and each site is irradiated with a single laser pulse with a defined and known fluence. The squared diameters of the damaged sites are then plotted vs. the laser fluence. The resulting straight line is extrapolated and the intercept of this regression line with the logarithmically scaled \(x\)-axis (i.e., the fluence values) finally gives the LIDT as shown schematically in Figure 2.  
In the present case, 25 test sites per sample were investigated. The used laser source was a Yb:YAG solid state picosecond laser (model TruMicro 5025 from Trumpf GmbH & Co. KG, Ditzingen, Germany) with a pulse duration of \(\tau\) = 8 ps, an emission wavelength of \(\lambda\) = 1030 nm, and a maximum pulse energy of \(E\) = 125 \(\upmu\)J. The raw beam with a diameter of \(2w_{\tau}\) = 5 mm was focused onto the sample surface by a convex lens with a focal length of \(f\) = 50 mm, resulting in a laser beam waist diameter of \(2w_{0}\) = 17 \(\upmu\)m. As a consequence, the initial laser fluence at the maximum pulse energy was 110 J/cm\({}^{2}\). This value was successively reduced in eight steps down to a minimum fluence of 22 J/cm\({}^{2}\) where the decrement per step was 11 J/cm\({}^{2}\). For the determination of the actual LIDT, the diameters of the laser-induced damaged sites on the sample surfaces (given in pixels) were measured with the aid of a light microscope (model Axioskop 2 MAT from Carl Zeiss Microscopy GmbH, Jena, Germany).  
In addition to the LIDT, the chemical composition of the sample surfaces was measured using a scanning X-ray photoelectron spectroscopy (XPS) apparatus (model PHI VersaProbe II from ULVAC-PHI, Inc., Hagisono, Japan) in order to determine and identify impurities induced by the manufacturing process. Here, the elements of interest were carbon and cerium, representing possible contaminants occurring in the course of sample preparation by rough grinding, lapping and polishing as well as silicon and oxygen since the investigated zinc crown glass is a high-silica glass  
Figure 2: Principle of the diameter regression technique applied for the determination of the laser-induced damage threshold (LIDT) including an example for the observed damage morphology (inset).  
(60-70 wt %) [37]. The particular elemental fractions were determined via evaluation of the C1s-peak of carbon, the Ce3d5-peak of cerium, the Si2p-peak of silicon, and the O1s-peak of oxygen in the measured spectrum.  
All measurements were performed on samples prepared at three different concentrations of the used polishing agent (19, 44, and 60%, see above). In addition, the impact of a plasma post-processing as described in the following section on the measured parameters was investigated.
# Achievable Rate of NOMA-Based Cooperative Spectrum-Sharing CRN over Nakagami-\(m\) Channels
## 3 System Model  
Consider the cooperative spectrum-sharing network shown in Figure 1, which consists of a primary transmitter (PT) who is a licensed user possessing their own frequency spectrum, a secondary transmitter (ST) who does not have any spectrum of their own to transmit its signal, and a primary (licensed) receiver (PR) and \(K\) secondary (unlicensed) receivers (SRs). There are two networks that can be classified as primary and secondary networks. In our model, the secondary network consisting of IoT devices cooperates with the primary network to transmit their messages, but IoT devices also help relay the message of the primary network. From this point onward, we represent the IoT transmitter and receivers as ST and SRs, respectively. The cooperative spectrum-sharing transmission strategy is divided into two phases. In the first transmission phase, only the PT is allowed to transmit the message to the PR, whereas, in the second transmission stage, ST uses the NOMA technique to transmit the signals to both PT and SRs.
## 3 Results
### Study 1 (Practically Healthy Subjects)
#### 3.1.2 EEG Data  
The changes in EEG indicators before and after the experiment are presented in Table 3. According to the analysis, the theta 1 power decreased compared to the baseline values. It should be noted that alpha 1 and alpha 2 rhythm power increased, but beta activity did not change significantly.  
The average power ratio between the theta and alpha rhythms decreased after completing one session of multitasking CT within the VR complex (see Figure 3).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Variable** & **Before VR** & **After VR** & \(p\)**-Value** \\ \hline Functional mobility of nervous processes & & & \\ Reaction time, ms, M (SD) & 401.3 \(\pm\) 31.21 & 379.6 \(\pm\) 20.97 & 0.0016 \\ Errors, n, M (SD) & 26.1 \(\pm\) 3.71 & 27.8 \(\pm\) 3.44 & 0.14 \\ Missed signals, n, M (SD) & 10.3 \(\pm\) 4.86 & 7.7 \(\pm\) 3.41 & 0.006 \\ Performance of the brain responses to feedback & & & \\ Reaction time, M (SD) & 391.4 \(\pm\) 24.62 & 378.0 \(\pm\) 23.56 & 0.001 \\ Errors, n, M (SD) & 154.5 \(\pm\) 19.68 & 163.1 \(\pm\) 24.52 & 0.002 \\ Missed signals, n, M (SD) & 51.0 \(\pm\) 22.05 & 48.8 \(\pm\) 29.30 & 0.56 \\ The Bourdon’s test & & & \\ Processed symbols per 1 th min, n, M (SD) & 105.9 \(\pm\) 47.24 & 138.6 \(\pm\) 37.16 & 0.01 \\ Processed symbols per 4 th min, n, M (SD) & 151.3 \(\pm\) 34.41 & 142.0 \(\pm\) 39.23 & 0.35 \\ Attention ratio, scores, M (SD) & 54.8 \(\pm\) 25.55 & 69.2 \(\pm\) 30.41 & 0.01 \\ Short-term memory & & & \\ Visual memory test, scores, M (SD) & 9.6 \(\pm\) 0.77 & 9.4 \(\pm\) 0.79 & 0.48 \\ Mental rotation & & & \\ Clock-turn test, scores, M (SD) & 26.4 \(\pm\) 5.82 & 31.9 \(\pm\) 7.05 & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cognitive tests indicators in the healthy participants before and after completing one session of multitasking CT within the VR complex.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Log10 Power** & **Log10 Power** & & \\
**EEG Ranges** & **Before VR,** & **After VR,** & \(\Delta\)**, \%** & \(p\)**-Value** \\  & **meV2/Hz** & **mcV2/Hz** & & \\ \hline Theta 1 (4–6 Hz), M (SD) & 0.42 \(\pm\) 0.13 & 0.38 \(\pm\) 0.15 & 6.1 & 0.04 \\ Theta 2 (6–8 Hz), M (SD) & 0.40 \(\pm\) 0.18 & 0.44 \(\pm\) 0.24 & \(-\)15.8 & 0.13 \\ Alpha 1 (8–10 Hz), M (SD) & 0.70 \(\pm\) 0.31 & 0.85 \(\pm\) 0.36 & \(-\)50.4 & 0.00007 \\ Alpha 2 (10–13 Hz), M (SD) & 0.81 \(\pm\) 0.36 & 0.86 \(\pm\) 0.43 & \(-\)19.5 & 0.04 \\ Beta 1 (13–20 Hz), M (SD) & \(-\)0.03 \(\pm\) 0.17 & \(-\)0.02 \(\pm\) 0.25 & \(-\)5.7 & 0.68 \\ Beta 2 (20–30 Hz), M (SD) & \(-\)0.39 \(\pm\) 0.15 & \(-\)0.41 \(\pm\) 0.18 & 0.9 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: EEG indicators in the healthy participants before and after completing one session of multitasking CT within the VR complex.
# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 2 Analysis and Modeling of Analytical PV Cell Introduction
### Mathematical Formulation for Photo Current and Dark Saturation  
Photon illumination of the PV module produces photon current \(I_{\rm{p}}\). Thus, an ideal PV cell is considered as one which has a current source with an intensity of \(I_{\rm{g}}\). and is in parallel connection with the diode. The dark saturation current is a measure of recombination in a device. The values of the parameters \(I_{\rm{p}}\), \(I_{0}\), \(\mathcal{R}_{\rm{g}}\), \(\mathcal{R}_{\rm{s}}\), and \(\delta\) are temperature- and irradiance-dependent.  
Equation (14) formulates the dark saturation current \(I_{\rm{\bf{v}}}(G,T)\). The photo current \(I_{\rm{\bf{b}}}(G)\) is formulated using (15).  
\[I_{\rm{\bf{v}}}(G,T)=\frac{I_{\rm{SC}}({\rm{G}},{\rm{T}})+\sigma_{\rm{SC}}(T-T_ {SOC})}{e^{\frac{(V_{OC}(G,T)+\sigma_{OC}(T-T_{SOC})}{n_{\rm{T}}^{3}})}-1} \tag{14}\]  
\[I_{\rm{\bf{b}}}(G)=\frac{G}{G_{SOC}}\bigg{[}I_{\rm{SC}}\bigg{[}\frac{\mathcal{ R}_{\rm{g}}(G,T)+\mathcal{R}_{\rm{s}}(G,T)}{\mathcal{R}_{\rm{g}}(G,T)}\bigg{]}+ \sigma_{\rm{SC}}(T-T_{SOC})\bigg{]} \tag{15}\]  
For any value of irradiance and temperature, the corresponding open circuit voltage short circuit current, and maximum power point can be obtained from Equations (7)-(9), whereas the respective reference short circuit current, reference open circuit voltage, and reference maximum power can be obtained from Equations (11)-(13). The difference between the reference values of the voltage, current, and power (i.e., \(V_{OC\_ref}\), \(I_{SC\_ref}\), and \(P_{MPP\_ref}\), respectively) and the actual values of the voltage, current, and power (i.e.,\(V_{0}\), \(I_{SC}\), and \(P_{MPP}\), respectively) gives the voltage error (E\({}_{\rm{V}}\)), current error (E\({}_{\rm{I}}\)), and power error (E\({}_{\rm{p}}\)).