esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Potential of the LIFTOP Process  
Biofunctional CaP coating would be useful for the surface functionalization of human tissues as well as medical devices, such as orthopedic and dental implants. Among conventional CaP coating processes, biomimetic processes have the advantage of mild coating conditions and, hence, can produce CaP coatings loaded with biofunctional substances including proteins, trace elements, antibacterial agents, nucleic acids, etc. [30,32]. However, conventional biomimetic processes generally lack area-specific coating capability and require a long processing time (at least a few hours), except for a few recent techniques [31,33]. In the present LIFTOP process, printing of the Fn-CaP microchips was completed within only a few seconds for an area of approximately 10 mm\({}^{2}\). Further, the printed region was finely confined to the laser irradiated spot. That is, the LIFTOP process is effective in the rapid and area-specific printing of Fn-CaP. As a substrate for Fn-CaP printing, not only soft and hard polymers but also human dentin was usable as demonstrated in this study. That is, it would be possible to modify and functionalize a human tooth surface through the biofunctional Fn-CaP printing by the LIFTOP process. It should be emphasized that the printing position, area, and pattern can be easily modified and adjusted by patterning and/or scanning of laser beam. Moreover, the CaP donor film can contain not only Fn but also many other biofunctional substances as described above. Therefore, by selecting an appropriate biofunctional substance for immobilization in the CaP donor film, the final product (CaP microchips) by the LIFTOP process can potentially be tailored to exhibit biological properties demanded for each application. Taken together, the LIFTOP process has potential as a new practical method for the surface-functionalization of medical devices and human tissues, although further process refinement along with in vitro and in vivo studies is needed.
# Secure Data Distribution Architecture in IoT Using MQTT
## 3 Distributed MQTT Architecture  
Distributed MQTT Architecture (DMQTTA) is an architecture that allows a set of MQTT brokers to cooperate and share their resources. It is modelled after SoA [17], where each broker provides a service that exposes resources of its domain. Furthermore, all the brokers in the system utilize a discovery service to aid them in the discovery of other brokers and resources. All the services in the system are exposed over well-defined interfaces which allow them to be easily discovered and consumed. The only assumption that is made about the system is that the discovery service is known by all the brokers in the system. There are several key advantages, as follows:  
1. Scalability: Since DMQTTA is a service-oriented architecture, it inherits all the scalability characteristics associated with SoAs. One of those is being able to scale each service independently of the others because they are loosely coupled [18]. For example, clients of a single broker could be distributed across a set of cooperating brokers that share their resources.
2. Interoperability: Any service provider/consumer can connect and provide/consumer services as long as they adhere to the interfaces of the network.
3. Security: Since the MQTT broker's domain is exposed over a well-defined interface, arbitrary security checks could be applied to consumers before allowing them access.  
The architecture defines three main types of services, namely, discovery service, broker service and authentication service. We detail them in Sections 3.2, 3.3, and 3.4, respectively. We use a running example defined in Section 3.1 to facilitate our discussion.
# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
## 3 Numerical Results
### Classification Algorithm Data Results and Analysis  
#### 3.2.1 Validation Set Results Based on 5.1 Sound Format and SW Mono-Channel Emotion Recognition  
For the segmentation of movie scenes with window lengths of 6-9 s, the validation accuracy results of three models trained with either six-channel audio features or SW monaural audio features are shown in Table 8. In general, the model trained with 5.1-channel audio features achieved the highest validation accuracy of 91%, while the model trained with SW monaural audio features achieved the highest validation accuracy of 88%. Although the former achieved a 3% higher validation accuracy than the latter, the latter was able to improve the computational speed by utilizing separately extracted SW monaural audio features without significantly affecting the accuracy of shock detection, resulting in performance comparable to the former. This finding suggests the importance of SW monaural audio features in detecting shock scenes in 5.1-channel audio format movies.  
Looking at each individual classification model, the SVM model and the CNN model achieved higher validation accuracy with the use of all six-channel audio features compared to SW monaural audio features, while the BPNN model showed the opposite trend.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signals** & **Lead Positions** & **Rhythm** & **Correlation Coefficient** \\ \hline S1 & F3 & \(\beta\) & 0.975 \\ S2 & AF4 & \(\beta\) & 0.900 \\ S4 & AF4 & \(\alpha\) & 0.975 \\ S8 & F3 & \(\theta\) & \(-\)0.900 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Significant correlation results between subjective and objective parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Length** & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**BPNN**} & \multicolumn{2}{c}{**CNN**} \\ \hline  & **6** & **SW** & **6** & **SW** & **6** & **SW** \\  & **Channels** & **Channel** & **Channels** & **Channel** & **Channels** & **Channel** \\ \hline
6 & 63\% & 87\% & 79\% & 88\% & 77\% & 74\% \\
7 & **91\%** & 87\% & 82\% & 88\% & 84\% & 83\% \\
8 & 76\% & 87\% & 86\% & 78\% & 84\% & 73\% \\
9 & 89\% & 83\% & 86\% & 82\% & 80\% & 80\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The accuracy of the validation set of the models trained using 5.1 of all 6 vocal channels: feature information and SW mono-feature information, respectively.  
Specifically, the BPNN model trained with SW monaural audio features achieved a 2% higher validation accuracy than the model trained with all six-channel audio features. This result indicates that a classification model trained solely with SW monaural audio features may have better performance in detecting shock scenes in 5.1-channel audio format movies, thereby validating the importance of LFS features in detecting shock scenes. The finalized features of SW channel screening under each window length segment are shown in Table 9, where SW, number, mean, std and diff represents SW mono, dimension, mean, standard deviation, maximum and minimum difference, respectively.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis
### Baseline Schemes
#### 5.2.3 WITT  
WITT [42] is a method that utilizes the swin transformer as its backbone to optimize image transmission, taking into account the impact of wireless channels. The authors propose a spatial modulation module that scales the latent representations based on channel state information. This enhances the model's ability to manage various channel conditions effectively. Furthermore, the scheme can be extended to a generalized version, improving its adaptability to different channel conditions and rate configurations.
## 4 Results  
### Seismological Analyses  
#### 4.1.1 Magnitude of Completeness (Mc) and \(b\)-Value  
Our following analyses aimed to investigate the behaviour in time of the seismic \(b\)-value to put it in relation with other seismological parameters.  
For performing the analysis around the region of interest, we refined the seismic data selection on the previously downloaded catalogue, by applying some thresholds in time and space. Therefore, we restricted the catalogue to seismic events in the period from 1 January 2018 to 5 February 2023, within an area of 200 km around the epicentre, and at a depth of less than or equal to 50 km: this selection resulted in 7120 events, spatially distributed as shown in Figure 5.  
The computation of the \(b\)-value requests the knowledge of the magnitude of completeness \(Mc\): from our analysis, we obtain a value of \(Mc\) equal to 1.6; from that magnitude and above, it included 95% of the data [49], as shown in Figure 6, considering only M1.6+ earthquakes resulted in having a total of 5187 events.  
Figure 5: Seismicity map for the analysed time period, together with the main fault systems of the investigated area. The yellow star represents an earthquake (M5.5) that occurred on 4 August 2020.  
Then, we cut events with a magnitude lower than \(Mc\) to estimate the temporal variations of the \(b\)-value parameter of the Gutenberg-Richter (G-R) law.  
In the analysed period, the trend of the \(b\)-value shows values lower than the global average, assumed to be equal to 1 (Figure 7). In detail, it fluctuates between 0.65 and 0.85. As stated before, low \(b\)-values have been correlated with areas of increasing stress and where the nucleation of earthquakes is likely to happen [65; 66; 67; 68; 69]. This means that we can consider 28 February 2021, when the \(b\)-value began its descent from a value of 0.94 to 0.73 just before the mainshock, as a \"time marker\", i.e., one of the outcomes characterising the seismicity of the area, with which to relate the anomalies or events emerging from subsequent analyses.
## 5 Experiment and Analysis
Each swing time is about 1.6 s.  
The y-direction velocity and x-direction displacement of the simulation prototype are shown in Figure 17b, where the red solid line is the y-direction velocity curve and the blue solid line is the x-direction displacement curve. It can be seen from Figure 17b that the simulation prototype enters a stable walking state gradually, and the y-direction velocity and x-direction displacement reach a stable change when \(t=16\) s.  
Figure 16: Output of self-growing network in trot gait. Figure 17: Simulation decomposition diagram of the trot gait of the simulation prototype. (**a**) Simulation diagram of trot gait. (**b**) The y-direction velocity and x-direction displacement curve of a quadruped robot. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(a_{9,1}\) & \(a_{8,2}\) & \(a_{12,3}\) & \(a_{11,4}\) & \(a_{1,10}\) & \(a_{2,6}\) & \(a_{3,7}\) & \(a_{4,5}\) & \(a_{10,1}\) & \(a_{6,2}\) & \(a_{7,3}\) & \(a_{5,4}\) \\ \hline
1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 \\ \hline \(a_{2,1}\) & \(a_{3,1}\) & \(a_{4,1}\) & \(a_{1,2}\) & \(a_{3,2}\) & \(a_{4,2}\) & \(a_{1,3}\) & \(a_{2,3}\) & \(a_{4,3}\) & \(a_{1,4}\) & \(a_{2,4}\) & \(a_{3,4}\) \\ \hline
1.19 & 0 & 1.22 & 1.25 & 1.56 & 0 & 0 & 1.41 & 1.88 & 1.84 & 0 & 1.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The connection weight of neuronal axons in the network. In Figure 16, N1 and N3 in the output of the self-growing network are input to the left front and the right hind hip joint of the experimental prototype, respectively, while N2 and N4 are input to the right front and the left hind hip joint of the experimental prototype respectively. The trot gait decompositions of the experimental prototype obtained are shown in Figure 18. Combining Figure 17a and Figure 18, we can see that the experimental prototype and the simulation prototype have the same gait cycle (about \(T=1.7\) s) under the control of the same self-growing network, and the self-growing network can achieve stable control of the trot gait of the simulation prototype and the experimental prototype. The experiment above shows that the quadruped robot control system based on a CPG neural network self-growing algorithm can accomplish the growth of the network, the signal output of the network and the control of a robot's joint, and then realize the rhythmic movement of a quadruped robot. This shows that the CPG neural network self-growing algorithm can be well applied to the rhythm control of a robot. The experiment preliminarily simulated the process from growth to mastery of a certain rhythmic movement of the CPG neural network and verifies the feasibility of the idea of realizing robot rhythmic movement control by combining the microscopic mechanism of the growth and the macro characteristics of the output of the neural network.
## 3 Proposed Security Protocol for Initial Attach in LTE
### Initial Attach with GUTI
#### 3.2.1 Case 1: MME Unchanged  
The first case is that the connected MME in an initial connection with the UE is not changed, and the UE re-accesses through the same MME. Security protocol for the first case is shown in Figure 6.  
Figure 6: Case 1: MME Unchanged.  
In a re-access, the initial attach process carries out authentication using GUTI to protect the IMSI. The process of transmitting the GUTI is the same as the Section 3.1. IMSI transmission process, and the initial attach process is carried out using existing information saved in the MME according to information, such as GUTI, NAS-MAC and NAS Seq. No. Since a series of information about the UE has already been saved in the MME, no \"authentication\", \"NAS security setup\" or \"location update\" process is carried out, but the \"EPS session establishment\" process only is carried out.
## 4 Conclusions  
We proposed a hybrid strategy for synthesizing AZO incorporated with RGO for controlling charge transport properties of oxide materials. We focused on the effect of RGO on the charge transport behavior of AZO TF. Microstructural characterization revealed that the samples comprised nanosized grains (\(<\)10 nm), and RGO was well dispersed in the AZO-RGO NCTF without aggregation. The electron transport in AZO TF was strongly affected by grain boundary scattering, but enhanced charge transport behavior was observed in the AZO-RGO NCTF. Given that grain boundary scattering can be suppressed through the incorporated RGO, the mobility of the AZO-RGO NCTF is improved with increased carrier concentration. Our results demonstrate that our hybrid strategy with RGO could have a positive effect on the charge transport behavior in oxide materials.  
Conceptualization, W.H.N.; methodology, J.-H.L, H.M.J.; formal analysis, J.-M.O, W.H.N., W.H.S.; investigation, H.S., J.-H.L., H.M.J.; data curation, J.-M.O., W.H.N., W.H.S.; writing--original draft preparation, W.H.N.; writing--review and editing, W.H.S., J.Y.C.; visualization, H.M.J., W.H.N.; supervision, W.H.S., W.-S.S., J.Y.C.; project administration, W.H.N.; funding acquisition, W.H.S., W.H.N., H.M.J., W.-S.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Ceramic Engineering and Technology (KPP19001), and by the Technology Innovation Program (10083640) through the Korea Evaluation Institute of Industrial Technology funded by the Ministry of Trade, Industry and Energy, Republic of Korea, and by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2019R1A6A1A11055660, 2019R1F1A1059591, 2020R1F1A1071311).  
Figure 5: Carrier-concentration-dependent (**a**) mobility and (**b**) resistivity in AZO TF and AZO-RGO NCTF at room temperature. For comparison, mobility and resistivity values of ZnO single crystals [31; 32; 33; 34] and epitaxial thin films [35; 36; 37; 38] in the literature are also shown.
# Can We Improve Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data?
## 3 Comparison of CYGNSS and ASCAT Data
One plausible explanation is that the resolution of CYGNSS (25 km) is too low to capture the surface wind speeds in this range of radii for weak cyclones, for which the distance between the eyewall and the 64-kt wind radius is relatively small. Indeed, wind speeds vary quickly as a function of distance to the center in these regions. This issue is presumably less severe for stronger cyclones, because of their larger extent of hurricane-force winds (Table 1). It should also be recalled that close to the cyclone center, errors on NHC data may be sufficient to significantly reduce the accuracy of parametric wind speeds. This issue might be part of the reason why results are not satisfactory for smaller radii than R64 in the case of category 1 storms. The results displayed in Figure 2 are used to make assumptions about the best proxy for wind speeds, depending on the considered radius and cyclone intensity. In the following sections, we will assume in particular that:  
1. ASCAT performs well for \(r>\) R34;
2. ASCAT also performs well for R34 \(<r<\) R50 and category 3/5 cyclones (this product shows the lowest biases in these cases);
3. none of the space-borne products tested here are reliable for smaller radii than R64 in the case of category 1 cyclones. We will not investigate these conditions in the next sections; and
4. in all other cases, NBRC or LES can be used as a proxy for wind speeds. The stars plotted in Figure 2 indicate which product has been selected as the proxy in Sections 4 and 5 for each radius/intensity class. In general, the product showing the lowest bias in absolute value is chosen. We will check in Sections 4 and 5 whether these assumptions give results consistent with previous work and in-situ data, to confirm or invalidate them a posteriori.
# Investigating the Effect of Screw Size on the Stress Level in MERO Joint for Space Frame Structures
## 3 Results and Discussion
### Comparing the Effect of the Ball Joint Size on the Stress Levels for Constant Screw Sizes  
Effect of the ball joint sizes with diameters of 98, 110, and 132 mm for each screw size on the maximum stress level can be compared for the considered three screw sizes of M12, M16, and M20.  
As depicted in Figure 16a, the maximum stress level in the ball joints for the small screw size of M12 is nearly independent of the ball joint sizes, and almost all of them should have the minimum yield strength of greater than 370 MPa, irrespective of the factor of safety. A similar conclusion can be drawn about the results acquired for M16 screw in Figure 16b which shows nearly minimal changes in stress levels.  
Examining the results in Figure 16 and considering the stress levels generated in the ball joints of considered sizes for the screw size of M20 in Figure 16c, demonstrates that the influence of superposition of stress fields caused by the joint has been eliminated and the stress level has reduced by around 30% after increasing the ball diameter from 98 to 110 mm. However, increasing the diameter of the ball joint to over 110 mm, i.e., to 132 mm, does not significantly decrease the stress level, but mainly increases the weight of the ball joint and accordingly, the weight of the space frame structure, and the related costs. Thus, it is not recommended to use the M20 screw size for ball joints of diameter less than 100 mm, in terms of the stress level created in the ball joint, or for ball joints of diameter more than 110 mm in terms of the weight and the final cost of the space frame structure.  
Figure 15: A comparison regarding the effect of the screw size on the maximum stress in the three MERO double-layer ball joints.
# Biosorption of Heavy Metals by the Bacterial Exopolysaccharideaccharide FucoPol
## 3 Results and Discussion  
### FucoPol Characterization  
Metal biosorption has been described for several EPS with different specificities and metal-binding capacities that can be attributed to differences in charge density, attractive interaction, and polymer conformation [20]. The biosorption performance of EPS varies significantly depending on their composition, as the properties of the active functional groups impact the biosorption mechanism [4]. Thus, biosorbent characterization is essential for a comprehensive analysis of the results (Table 1).  
FucoPol is a high molecular weight (\(4.4\times 10^{6}\) Da) polysaccharide secreted by the bacterium _Enterobacter_ A47. It is mainly composed of neutral sugars, fucose, galactose and glucose, and the acidic sugar glucuronic acid (Table 1). The non-carbohydrate moiety comprises the acyl groups acetyl, pyruvyl and succinyl, which account for 12.3 wt% of FucoPol's mass. The presence of non-carbohydrate substituents and ionizable functional groups (carboxyl and hydroxyl groups), which are responsible for the binding capacity of biosorbents [24], in FucoPol's structure drove the interest in exploring its ability to bind heavy metal cations. Specifically, the presence of glucuronic acid residues, together with the pyruvyl and succinyl groups, that confer the biopolymer an anionic character, is relevant for its binding ability towards cations and positively charged molecules. FucoPol sample also has a protein content of 10.8%, which was not completely removed during the purification procedures, probably due to their high Mw [27]. The presence of amine, sulfhydryl and carboxyl functional groups in proteins contribute to the overall negative charge of FucoPol [24]. On the other hand, metals can establish covalent or coordinative interactions with the sulfur- and nitrogen-containing ligands [4], which could also contribute to the metal biosorption capacity of FucoPol.
## 4 Experimental Results
### Comparison of Statistical Results
In addition, when the population size was set to 40, SEDE provided the best performance, and the ICO provided the second-best performance based on [PERSON]'s test. When \(n=80\), however, the ICO was determined to be the best-performing algorithm, while the CO was determined to be the second-best-performing algorithm among the 12 competing algorithms. Furthermore, when the population size was 40, the Wilcoxon signed rank test did not indicate a significant difference between the ICO, CO, SEDE, and PGJAYA. Moreover, based on the Wilcoxon tests with \(n=80\), the ICO, CO, and PGJAYA performed similarly. The best results were from utilizing the ICO, CO, and SEDE for the PVMM when looking at the Min and Mean RMSE of Table 9. Despite WSO's ability to achieve the highest accuracy, its average accuracy and robustness could not compete with the ICO, CO, and SEDE. The lowest SD value was achieved by SEDE (2.319 \(\times\) 10\({}^{-17}\)), and the second- and third-best values were obtained by the ICO and CO (4.998 \(\times\) 10\({}^{-17}\) and 1.105 \(\times\) 10\({}^{-16}\), respectively). According to [PERSON]'s test, the ICO provided the best performance, and the CO provides the second-best performance when \(n=40\). For \(n=80\), this ranking was shifted. The final ranking of the comparative algorithms for identifying the unknown parameters of the SDM, DDM, and PVMM is shown in Figure 9. For these models, the best  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{n}\)} & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Max**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Sum**} & \multirow{2}{*}{**Rank**} & \multirow{2}{*}{**Significance**} \\ \hline  & ICO & 2.42507486095 \(\times\) 10\({}^{-3}\) & 2.4  
sum rank result among the 12 algorithms with \(n=40\) was obtained by the ICO, followed by the CO and SEDE. While \(n=80\), the CO, ICO, and PGJAYA exhibited the first, second, and third sum rank results in the three models, respectively. Additionally, Figure 10 shows a box plot diagram of all competitive algorithms for a visual representation of the distribution of optimal RMSEs obtained for the three investigated models over 30 runs. Based on the distribution of answers, it is clear that the ICO and CO performed the best in terms of robustness in finding the optimal solution. SEDE and PGJAYA also provided acceptable robustness. Figure 9: Final ranking of applied algorithms for three models based on the Friedman test: (**a**) \(n=40\) and (**b**) \(n=80\). Figure 10: Boxplot of best RMSEs in 30 runs for \(n=40\): (**a**) SDM, (**b**) DDM, and (**c**) PVMM.