esa-sceva/satcom-chunk-collection huggingface dataset:
# Asynchronous Flipped Grant-Free SCMA for Satellite-Based Internet of Things Communication Networks
## 5 Simulation Results
### AF-SCMA MAC Performance Results
This is because the codebooks of AF-SCMA (6, 12) and AF-SCMA (6, 15) used in the simulations are not well predefined, which are designed simply based on AF-SCMA (4, 6) [36]. Thus, the shaping gain brought by multidimensional constellation is limited. When the traffic load becomes heavy, the SCMA decoder can hardly distinguish the overlapping codewords in a particular subcarrier for a medium \(\left[E_{b}/N_{0}\right]_{D}\) case. Nevertheless, the codebook design of SCMA is a tough and complicated issue which is left for future work. Above all, by increasing the number of subcarriers and codebooks with proper design, the performance of AF-SCMA can be further improved. Figure 4: Performance comparison among A-SCMA, S-SCMA and AF-SCMA for packet size 500 bits, Turbo encoder with \(r=1/3\), SCMA (4, 6), \(N_{chunk}=10\) (AF-SCMA), \(N_{iter}=10\), in the presence of power unbalance with \(\mu=0\) and \(\sigma=0,3\) dB respectively (Legends in the two sub-figures are the same. To avoid overlapping the curves, legends are only marked in sub-figure (**a**)). (**a**) Throughput. (**b**) PLR.
# Interferences of Electromagnetic Pulses on Microcontroller Units
## 5 Conclusions  
Through EMI testing of an MCU, this study demonstrated that as the electric field increased, the radiation interference of EMPs on the MCU increased. As the capacitance increased, the rising edge of the EMP remained unchanged, yet the FWHM of the EMP increased, indicating that the EMI was related to the action time and had a strong cumulative effect. The radiation interference of EMPs had a strong directionality--that is, path selectivity. This was related to the internal structure of the MCU. Therefore, during the design of a chip with an MCU, the directions of the pins for important signals should be made the same as the propagation direction of possible interference pulses.  
Conceptualization, Z.H.; methodology, L.F., X.Z. and Z.H.; validation, L.F. and X.Z.; formal analysis, L.F.; investigation, L.F.; resources, Z.H.; data curation, L.F.; writing--original draft preparation, L.F.; writing--review and editing, X.Z. and Z.H.; visualization, Z.H.; supervision, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
Figure 10: Positions of the pins when the MCU was at 90\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Storage Capacitance (pF)** & **Maximum Interference (Number of Groups)** & **Minimal Interference (Number of Sets)** \\ \hline
15,000 & A, B, C, D, E, F, G, H, J, L, N, O & 0 \\
5793 & M, P & B, E, F, G, I, K \\
1910 & I, K & A, C, D, H, J, L, M, N, O, P \\ \hline \hline \end{tabular} Note: The test conditions represented by each letter are as follows: A: 0\({}^{\circ}\) and 1 atm; B: 0\({}^{\circ}\) and 2 atm; C: 0\({}^{\circ}\) and 3 atm; D: 0\({}^{\circ}\) and 4 atm; E: 90\({}^{\circ}\) and 1 atm; F: 90\({}^{\circ}\) and 2 atm; G: 90\({}^{\circ}\) and 3 atm; H: 90\({}^{\circ}\) and 4 atm; I: 180\({}^{\circ}\) and 1 atm; J: 180\({}^{\circ}\) and 2 atm; K: 180\({}^{\circ}\) and 3 atm; L: 180\({}^{\circ}\) and 4 atm; M: 270\({}^{\circ}\) and 1 atm; N: 270\({}^{\circ}\) and 2 atm; O: 270\({}^{\circ}\) and 3 atm; P: 270\({}^{\circ}\) and 4 atm.  
\end{table}
Table 8: Comparison of interference of different storage capacitors on pin 34.
## 3 Results
### Adam Optimizer  
The comparison between the performance of Mod_AlexNet and AlexNet in terms of training accuracy, training loss, and testing accuracy using Adam optimizer with different batch sizes is shown in Table 6 and Figure S2.  
According to Figure S2, for a batch size of 32, Mod_AlexNet achieved a training accuracy of 89.94% and a training loss value of 0.39, whereas AlexNet achieved a lower training accuracy of 89.52% and a training loss value of 0.4. Furthermore, Mod_AlexNet achieved 94.01% training accuracy with a training loss value of 0.18 for a batch size of 64, whereas AlexNet achieved 89.50% training accuracy with a training loss value of 0.4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**DL Models**} & \multirow{2}{*}{**Batch Size**} & **Training Accuracy** & **Testing Accuracy** & **Training Loss** \\ \hline \multirow{3}{*}{**AlexNet**} & **32** & 89.52\% & 88.67\% & 0.4 \\  & **64** & 89.50\% & 89.02\% & 0.4 \\  & **512** & 86.70\% & 89.26\% & 0.48 \\ \hline \multirow{3}{*}{**Mod\_AlexNet**} & **32** & 89.94\% & 90.26\% & 0.39 \\  & **64** & 94.01\% & 89.45\% & 0.18 \\ \cline{1-1}  & **512** & 90.09\% & 90.36\% & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Adam optimizer performance (3 epochs and batch sizes: 32, 64, and 512).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**DL Models**} & \multirow{2}{*}{**Batch Size**} & **Training Accuracy** & **Testing Accuracy** & **Training Loss** \\ \hline \multirow{3}{*}{**AlexNet**} & **32** & 98.50\% & 88.74\% & 0.05 \\  & **64** & 97.94\% & 88.53\% & 0.07 \\  & **512** & 93.33\% & 90.32\% & 0.2 \\ \hline \multirow{3}{*}{**Mod\_AlexNet**} & **32** & 97.67\% & 91.61\% & 0.08 \\  & **64** & 96.74\% & 90.63\% & 0.11 \\ \cline{1-1}  & **512** & 91\% & 90.70\% & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 5: SGDM optimizer performance (3 epochs and batch sizes: 32, 64, and 512).  
Finally, with a batch size of 512, Mod_AlexNet achieved 90.09% training accuracy with a training loss of 0.28, while AlexNet achieved 86.70% training accuracy with a training loss of 0.48.  
Based on Table 6, Mod_AlexNet outperformed AlexNet in terms of testing accuracy on different batch sizes, according to the Adam optimization technique. When compared to AlexNet with a batch size of 32, Mod_AlexNet improved testing accuracy by 1.59%. Mod_AlexNet demonstrated a 0.43% and 1.1% increase in test accuracy when trained on 64 and 512 batch sizes, respectively, when compared to AlexNet. The highest test accuracy of 90.36% was achieved by Mod_AlexNet on a batch size of 512.
## 1 Introduction
We have presented various methods targeting power saving in different areas [25; 26], considering setting up and taking down the wireless interfaces of the MAPs whenever possible. We showed that a significant amount of power could be saved by this. In this sense we are aligned with one of the main challenges of smart cities [27]. The challenges mentioned above are applicable to any WMN, but we will focus on WiFi-based WMNs viewed as a ubiquitous, portable, and scalable solution for any deployment requirement; targeting to allow end users to access sensing data, which are directly delivered by MAPs. Unlike major WSNs (based on Zigbee or IEEE 802.15.4), our network model overcomes their restrictions by extending their range of operation, rate and compatibility. Thus we will call it Portable Wireless-friendly Mesh Network for Sensors (PWMNS), which is conceived as a communication backbone, as well as a source and sink, for sensor data traffic in a smart city. In [25; 26] we reviewed power saving works from other authors that are complementary to our mechanism. In this paper we extend our previous works [25; 26] considering a new strategy to set up and take down down the WiFi interfaces of MAPs. The strategy includes taking these WiFi interfaces down whenever a MAP detects a moderate level of interference from the other MAPs or external WiFi devices. We present new algorithms, protocol implementation and software architecture to accomplish this strategy. We also performed a simulation on new parameters measuring the obtained interference redress and power saving. Finally we obtained experimental results on a testbed after applying our strategy. In addition to obtain a power saving (like in [25; 26]), we also redress the adverse effects of interferences inside the PWMNS and in external surrounding WiFi devices. The rest of the paper is organized as follows: in Section 2 we present the background on related and previous works. Section 3 presents our system architecture for supporting sensor data traffic in a smart city. Section 4 sketches the mechanism and protocol for a MAP to controllably set its wireless interface up and take it down. Section 5 formalizes the interference redress and the power saving degrees achieved by the PWMNS; and simulation results shows that deactivation and activation of MAPs wireless interfaces can allow both redressing the effects of interferences, as well as power savings when they are not required in any route. Then, in Section 6, we present the experimental results over a test platform based on Raspberry Pi (RPi) [28]. Finally, conclusions are presented in Section 7.
# Polystyrene-Sepiolite Clay Nanocomposites with Enhanced Mechanical and Thermal Properties
## 3 Results and Discussion
### X-ray Diffraction Analysis  
The X-ray diffraction pattern of pristine polystyrene S0 and the effect of adding m-sepiolite as a filler on % crystallinity of polystyrene-sepiolite nanocomposite S1, S2 and S4 formulations are presented in Figure 4. A broad diffraction peak at 20.0\({}^{\circ}\) with d = 0.22 observed in S0 shows the amorphous nature of polystyrene [47; 48]. The percent crystallinity obtained computationally using the XRD deconvolution method in Eq.1 shows an increase from 35.9% in the S1 sample to 47.3% in the S4 sample because of m-sepiolite clay being added into the polystyrene matrix. This also results in a decrease in broadness of 20 (X-axis) values at 7.25\({}^{\circ}\), 20.0\({}^{\circ}\) and 27.5\({}^{\circ}\), as shown in the XRD pattern. The intense peak at 7.27\({}^{\circ}\) with interatomic distance d = 1.22 nm with (110) shows the covalent interaction and exfoliation of clay nanoparticles in the polystyrene matrix and becomes sharper with the increase in the concentration (wt.%) of m-sepiolite. Therefore, the addition of a filler causes an  
Figure 3: FTIR spectra of S0 (pristine PS), S1 (PS/1 wt.% m- sepiolite), S2 (PS/2 wt.% m-sepiolite) and S4 (PS/4 wt.% m-sepiolite).  
increase in the intra-atomic distance or d-spacing due to the expansion in the polymer matrix, which is attributed to more favorable interactions between the polymer matrix and the filler [49]. The mechanical properties are also related to the level of saturation and dispersion of clay nanoparticles in the polymer matrix. Further, a sharp peak observed at 27.5\({}^{\circ}\) (d-spacing = 0.16 nm) evidences the increase in percent crystallinity upon increasing the filler content of m-sepiolite in the polystyrene matrix. These results enhanced the mechanical and thermal properties of nanocomposites, as is also evident in the tensile testing, TGA and DSC results.
# Glucose Isomerase: Functions, Structures, and Applications
## 2 Function
### Metal Ions  
GI requires divalent cations as cofactors for its isomerization activity [12]. Typically, GI is known to exhibit the highest activity in the presence of divalent metal ions such as Mg\({}^{2+}\), Mn\({}^{2+}\) and Co\({}^{2+}\); however, the specific metal ion requirement can vary depending on the source of the enzyme. For example, the GI from _Bacillus coagul_ shows the maximum enzyme activity in the presence of Mg\({}^{2+}\) or Mn\({}^{2+}\)[13], and Mn\({}^{2+}\) is the most favorable ion for catalytic activity of the GI from _Pironyces_ sp. E2 [14]; in contrast, the GI from the _Streptomyces_ strain Y-5 prefers Co\({}^{2+}\)[15]. Moreover, using a combination of these divalent metal ions has been shown to further enhance the isomerization activity. For example, the activity of EDTA-treated GI from _Streptomyces_ sp. CH7 was reportedly increased by 3.6-, 2.8-, and 2.1-fold in the presence of 1 mM Mg\({}^{2+}\), Mn\({}^{2+}\), and Co\({}^{2+}\), respectively, compared to its activity in the absence of any metal ions [16]. However, the enzyme activity increased 6.2-fold and 44.2-fold in the presence of the combinations 1 mM Mg\({}^{2+}\) and 0.1 mM Co\({}^{2+}\), and 10 mM Mg\({}^{2+}\) and 0.1 mM Co\({}^{2+}\), respectively [16]. Thus, during optimization, it is important to measure GI activity not only with one metal ion, but also with a combination of several metal ions. In general, Mg\({}^{2+}\) and Mn\({}^{2+}\) act as GI activators, whereas Co\({}^{2+}\) acts as a protein stabilizer, maintaining the ordered conformation in the quaternary structure of GI [1].
# Evaluation of Aging Resistance of Graphene Oxide Modified Asphalt
## 3 Results and Discussion
### UV Aging Resistance Performance  
#### 3.2.1 Chemical Structure  
The asphalt chemical structure will be changed during the aging process, the characteristic functional groups index, namely carbonyl groups (\(1700\) cm\({}^{-1}\)) and sulfoxide groups (\(1032\) cm\({}^{-1}\)), will increase with the increase of aging degree [16; 33]. In order to analyze the anti-aging performance of  
Figure 1: SEM pictures of GO (**(a)**\(500\times\); and **(b)**\(1000\times\)).  
asphalt, the FTIR was used to test the characteristic functional groups of asphalt binders before and after aging. The \(\rm I_{C=O}\) and \(\rm I_{S=O}\) were calculated according to the Equations (1) and (2), respectively:  
\[\rm I_{C=0}=\frac{S_{1700\ cm^{-1}}}{S_{2000\sim 600\ cm^{-1}}} \tag{1}\]  
\[\rm I_{S=0}=\frac{S_{1030\ cm^{-1}}}{S_{2000\sim 600\ cm^{-1}}} \tag{2}\]  
where, \(\rm S_{1700\ cm^{-1}}\) expresses the areas of the 1700 cm\({}^{-1}\) centered carbonyl group absorption band; \(\rm S_{1032\ cm^{-1}}\) expresses the areas of the 1032 cm\({}^{-1}\) centered sulfoxide group absorption band; \(\rm S_{2000\sim 600\ cm^{-1}}\) expresses the areas of all absorption bands between 2000 cm\({}^{-1}\) and 600 cm\({}^{-1}\).  
The \(\rm I_{C=O}\) and \(\rm I_{S=O}\) of the aged binders (i.e., TFOT and UV) studied in this research are shown in Figures 2 and 3, respectively. As shown in Figure 2, the \(\rm I_{C=O}\) of 90 A and SBS MA were gradually increasing with an increase in the UV aging time, where, 90 A + 1% GO, 90 A + 3% GO, SBS MA + 1% GO, and SBS MA + 3% GO correspond to 90 A with 1% GO, 90 A with 3% GO, SBS MA with 1% GO, and SBS MA with 3% GO, respectively. The results for the \(\rm I_{S=O}\) followed a similar trend (Figure 3). After UV aging, the \(\rm I_{C=O}\) and \(\rm I_{S=O}\) increments of 90 A and SBS MA with GO are smaller than the virgin binder (asphalt without GO), indicating that the GO can improve the UV aging resistance of 90 A and SBS MA, and inhibit the increase in \(\rm I_{C=O}\) and \(\rm I_{S=O}\) of 90 A and SBS MA during UV aging.  
Figure 3: The \(\rm I_{S=O}\) of 90 A (**a**) and SBS MA (**b**) after UV aging.  
Figure 2: The \(\rm I_{C=O}\) of 90 A (**a**) and SBS MA (**b**) after UV aging.
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 2 Architecture for CR-Based Multi-Gateway Multi-Beam HTS Systems
### Large-Scale Antennas and Spatial Correlations  
In order to cope with higher data traffic demands, satellite system designers are looking for advanced satellite communication architectures. In this context, the use of large-scale antennas has recently received a lot of attention, in order to boost the system throughput by simultaneously serving  
Figure 1: The proposed cognitive HTS system architecture.  
Figure 2: Spectrum coexistence of fixed satellite service (FSS) and high-density FSS (HDFSS) with fixed service (FS) in the Ka band.  
multiple users. However, as the number of antennas at the satellite increases, the downlink training resources required to collect channel state information (CSI) for RRM may become prohibitively large. In this regard, the spatial correlations of satellite systems should be exploited.  
Generally, spatial correlations indicate the long-term preferential directions of user channels, depending on the geometry of local scatterers and spacing of transmitting antennas. Owing to the high altitudes of satellites, channels between the satellite and terrestrial users occur along clusters of scatterers that are distributed on a narrow angular spread [30]. In this case, channels between the satellite array and a given user are spatially correlated. Moreover, since user distribution is relatively intensive from the perspective of the satellite, there may exist spatial correlations across multiple users in the considered multiuser environment. In this context, the JSDM technique can be adopted in the HTS system by organizing satellite beams in clusters, each serving multiple users experiencing similar channel statistics.  
To summarize, spatial correlation provides an opportunity for CSI relaxation and becomes a promising perspective for optimizing radio resources in HTS systems.
# A Novel Deep Learning Model to Predict Ultimate Strength of Ship Plates under Compression
## 5 Conclusions  
In this study, a nonlinear analysis of a curved plate scenario subjected to a transverse compressive load was performed using ANSYS, and a deep learning model was developed to predict the ultimate strength using the obtained analysis results as learning data. The prediction of the ultimate strength for 810 cases (20%) out of 4050 analysis scenarios, created according to the geometrical values and physical properties of the curved plate, showed a high accuracy of approximately 98.74%. The following conclusions were derived based on the study results.  
1. It is possible to estimate the ultimate strength of a curved plate using deep learning models faster than when using the FEM.
2. Deep learning models can be applied more widely than the method of estimating the ultimate strength using empirical formulae.
3. Deep learning models exhibit high accuracy as the ultimate strength prediction result of the curved plate.  
Although a deep learning model was developed using the analysis results of 4050 curved plates, it is expected to be more useful for estimating the ultimate strength in the actual design stage if the analysis results are applied under more diverse conditions for data learning, such as a stiffened curved plate or a curved plate under combined load conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Empirical Formulae and DNN Model** & **Error (\%)** \\ \hline [PERSON] et al. (2014) & 12.3351 \\ DNVGL (2015) & 33.1862 \\ DNN & 1.2648 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the deep learning model result and empirical formulae.  
**Author Contributions:** Conceptualization, S-j.C., I-j.B. and S.-c.S.; Data curation, S-j.C. and I.-j.B.; Formal analysis, S.-j.C., I.-j.B. and S.-c.S.; Investigation, S.-j.C. and I.-j.B.; Methodology, S.-j.C. and I.-j.B.; Writing--original draft, S.-j.C.; Writing--review and editing, S.-j.C. and I.-j.B.; Project administration, S.-c.S.; Supervision, S.-c.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research was supported by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (No. P0001968, Competency Development Program for Industry Specialists).  
The authors declare no conflict of interest.
# Lower-Latency Screen Updates over QUIC with Forward Error Correction
## 5 Evaluation Setup on Emulab  
We used Emulab testbed [31; 32] for all of our experiments. To create an Emulab experiment, one needs to create an account and write a profile to describe the specification of the topology and settings. Our Emulab profile used for all of our experiments is available on GitHub [33]. Figure 5 shows the topology used in our experiments, with two nodes as the sender/server and the reciever/client and an Emulab bridge node (in the middle) which runs DummyNet [34]. DummyNet is a FreeBSD system facility that emulates network delay, packet-loss rate, and bandwidth. We ran a provided configuration script called delay_config on the bridge node to set the network parameters.  
For example, we used the command in Figure 6 to set 100 ms RTT (i.e., \(DELAY=100\)), 2% loss rate (i.e., \(PLR=0.01\)), and 500 Mbps (i.e., \(BANDWIDTH=500\),000) bandwidth on the bridge node.  
Figure 4: Example ngtcp2 command line, client [8].  
Figure 5: Experimental topology obtained from Emulab GUI [8].  
We also set the DummyNet queue size to 9 MB (i.e., _LIMIT_ = _9,000,000_) to have enough buffer based on the Bandwidth-Delay Product (BDP) of the link, as recommended [35]. One important point is that we cannot set a limit higher than 1048576\(B\) as the default maximum limit of DummyNet. Therefore, we needed to run a command to increase that maximum threshold first (e.g., 9 MB in Figure 7).  
For the client and server nodes, we used the public Ubuntu 22.04 image with 5.15.0-86-generic as the kernel version on two d430 nodes of Emulab. These nodes have two 2.4 GHz 64-bit 8-Core Xeon E5-2630v3 processors, 20 MB cache, 64 GB RAM, and 10 GbE NICs. We implemented the FEC encoding and decoding part in the HTTP layer (i.e., nghttp3), and we used different functions of OpenFEC library in our implementation. Therefore, we needed to modify the configure command of nghttp3 to use OpenFEC as a library in the code. Figure 8 shows the modified configure command.  
In our experiments, we set \(n\) to \(\{103,105,110\}\), \(k\) to 100, and the symbol size was the update size divided by \(k\).
## 4 Simulations and Results  
The simulation results of the fuzzy-controlled agricultural greenhouse are presented in Figure 14. Two different set points were set for the inside temperature: 15 \({}^{\circ}\)C for the night and 24 \({}^{\circ}\)C during the day. These reference values were fixed to guarantee optimal thermal conditions for cultivation (for tomato plants) in this test lab. Other reference values could be fixed for other cultivations. The same test for the inside humidity set two reference points: 70% during the day, and 80% during the night.  
For this study, a real database was applied for 3 days, from 5 March 2017 to 7 March 2017, at the CRTen Center: the database included solar radiation, temperature, and humidity. The selected period was characterized by a high variation in wind speed and in global average solar radiation between 0 and 800 W/m\({}^{2}\), due to the position of the greenhouse (very close to the sea).