esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 4 Virtual World Architecture Inspection
### Client/Server
#### 4.1.1 Requirement Satisfaction  
C/S architecture adopts a centralized structure, which easily satisfies sufficiency and reliability. For sufficiency, codes running on the client can be partly or completely offloaded to dedicated servers through function invocation [109]. In code offloading, [67] has provided an energy trade-off model to maximize energy efficiency in terms of the ratio of energy cost to device-specific cost in local computation and additional communication. For QoS/QoE, MAUI [87] has shown the improved video  
Figure 8: Conceptual models of virtual world architectures: (**a**) client/server (C/S), (**b**) peer-to-peer (P2P), (**c**) Hybrid-I, and (**d**) Hybrid-II.  
refresh rate from 6 to 13 frames per second (FPS) with low energy consumption, latency, and data transfer overhead. Cloud gaming [69] can also address the sufficiency issue through interactive remote rendering [110]. As such, energy consumption and QoS/QoE remain the main focuses of cloud gaming.  
C/S virtual worlds can resolve the reliability problem with replication. The authors of [88] applied a database replication approach to multiplayer online games to provide fault tolerance. Virtual world operations are handled with a Read-One-Write-All-Available approach. That work provides four alternative synchronization approaches for propagating updates to all replicas to maintain consistency. It also shows fast responsiveness (specifically, \(\leq\)20 ms for read-only transactions and \(\leq\)100 ms for write transactions). Database replication can also improve reliability in game state storage. Yet, to the best of our knowledge, existing C/S virtual world systems have not fully addressed the connection failure issue. Directing communications to nearby fog facilities might be a solution [111], since a shorter distance between fog facilities and mobile clients can reduce the chance of connection failure.  
For data security, policy-based access control [93], together with secure hardware [112] or cryptographic measures [113], can be applied in data sharing [113], keyword search [114], and public audit [115]. Fog computing can also mitigate insider data theft [116]. Moreover, security policies can be enforced through data protection compliance [95] and accountability check [84]. For legal protection, [PERSON] et al. [97] proposed a user-centric rights representation model for the DRM of user-generated content by employing a separate license server [97].  
Moreover, the content moderation issue has been discussed in [86], which proposed a risk management framework for user-generated content with seven moderation techniques, respectively, employed by platform owner, system, trusted curator, and the public. Notably, the solution to this issue is applicable to all architectures.
## 2 Materials and Methods
### Convolutional Neural Network Pest Detection  
An image processing Convolutional Neural Network (CNN) detects small rodents by incorporating HEXBUG nano mounted with the ESP32-CAM board. The CNN consists of five convolutional layers, each followed by a max pooling layer, and three fully connected layers. The first convolutional layer has 32 filters with a kernel size of \(3\times 3\) and a stride of 1. The second convolutional layer has 64 filters with a kernel size of \(3\times 3\) and a stride of 1. The third convolutional layer has 128 filters with a kernel size of \(3\times 3\) and a stride of 1. The fourth and fifth convolutional layers have 256 filters each with a kernel size of \(3\times 3\) and a stride of 1. A max pooling layer with a pool size of \(2\times 2\) and a stride of 2 follows each convolutional layer. The three fully connected layers have 1024, 512, and 2 neurons, respectively.  
The training of the CNN used a dataset of 1700 images containing the original and augmented pictures, half of which contained rats and the other half were background images including the original images and pictures captured using the ESP32-CAM. The dataset was split into training and validation sets with a 9:1 ratio. An Adam optimizer with a learning rate of 0.001 and 20 epochs trained the CNN.
# Continuously Beam-Steered Phased Array Antenna Using GaN Varactors for Millimeter-Wave Applications
## 2 Investigation of IEMN'S GaN Devices as Varactors
The behavior of \(\rm C_{Eq}\) vs. \(\rm V_{g}\) can be separated into two regions:  
* Region_1 for \(-2\) V \(\leq\rm V_{g}\leq 2\) V: the \(\rm C_{Eq}\) value changes significantly as does \(\rm R_{Eq}\), where it achieves its maximum value of 18 \(\rm\Omega\). * Region_2 for \(\rm V_{g}<-2\) V: Corresponding to \(\rm C_{Eqmin}\), the \(\rm C_{Eq}\) value does not change. The same behavior is noticed for \(\rm R_{Eq}\). As clearly seen, depending on the \(\rm V_{g}\) value applied, tuning of the capacitive effect is generally achieved as a function of this bias voltage. In addition, as was already demonstrated in [15; 20], a resistive effect can also be observed and it is also dependent on \(\rm V_{g}\). These two effects represent, respectively, the equivalent series capacitance (\(\rm C_{Eq}\)) and the equivalent series resistance (\(\rm R_{Eq}\)). As a result, this \(\rm R_{Eq}\) will directly introduce more loss. At 40 GHz, as seen in Figure 3a, the max and the min values of \(\rm C_{Eq}\) are, respectively, 355 ff and 35 ff (\(\rm C_{Eqmax}/\rm C_{Eqmin}\) ratio about 10.4), achieved for the GaN_Lg110 nm device.
## 4 Conclusions  
In this study, a TiC/AISI 1020 metal matrix composite was manufactured using a liquid pressing infiltration method, and its properties were compared with AISI 52100 bearing steel. From analyzing the microstructure of the manufactured metal matrix material, it was confirmed that the TiC reinforcement was uniformly dispersed in the matrix, and there were no defects, such as cracks or pores, or chemical reactants. The average Rockwell hardness value of AISI 52100 was 62.42 HRC, and the hardness of the heat-treated composite was 76.64 HRC, which was about 25% higher than the AISI 52100 steel. From the pin-on-disc wear test, the wear width and depth of TiC/AISI 1020 were 682 \(\upmu\)m and 0.5 \(\upmu\)m, respectively, which were smaller than those of AISI 52100 (2937 \(\upmu\)m and 2.6 \(\upmu\)m). In the case of the TiC/AISI 1020 metal matrix composite manufactured by liquid pressing infiltration, it was confirmed that the high hardness of TiC particles was uniformly distributed on the AISI 1020 matrix, thereby improving the hardness and wear resistance of the composite as compared with AISI 52100 bearing steel. The composite material with TiC reinforcement  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **AISI 52100** & **AISI 1020/TiC** \\ \hline Wear width, \(\upmu\)m & 2937 & 682 \\ Wear depth, \(\upmu\)m & 2.6 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Wear width and wear depth of AISI 52100 steel and TiC/AISI 1020 composite.  
Figure 8: SEM micrographs of pin wear surface against (**a**) AISI 52100 specimen and (**b**) TiC/AISI 1020 composite.  
can endure more severe wear conditions than commercial AISI 52100 bearing material, and the hardness and wear behavior can be improved by a quenching heat treatment.  
Conceptualization, J.K. and I.J.; methodology, S.C.; validation, S.S.; formal analysis, H.K.; investigation, J.P.; data curation, J.K.; writing--original draft preparation, H.K.; writing--review and editing, I.J.; visualization, D.-S.B.; supervision, I.J.; project administration, S.C.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2020M3H4A3105945) and the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0012451, The Competency Development Program for Industry Specialist).  
Not applicable.  
The data and analysis in this study are available on request from the corresponding author.  
This research was supported by the Basic Science Research Capacity Enhancement Project through a Korea Basic Science Institute (Core-facility for Converging Materials) grant funded by the Ministry of Education (2019R1A6C1010045).  
The authors declare no conflict of interest.
## 5 Data Evaluation
### Accuracy Results
#### 5.3.3 Estimating Four-Node Motif Frequencies  
Figure 9 reveals the NRMSEs of \(\hat{n}_{i}^{(4)}\), frequency estimates of four-node undirected motifs for \(p=0.05\), \(p=0.1\), and \(p=0.2\), respectively, using the graphs soc-Epinions1, soc-Slashdot08, soc-Slashdot09, and com-Amazon. We can see that the NRMSEs of the other motif frequency estimates are smaller than 0.2, 0.1, and 0.07 for \(p=0.05\), \(p=0.1\), and \(p=0.2\), respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(i\) & **Flickr** & **Pokec** & \begin{tabular}{c} **Live-** \\ **Journal** \\ \end{tabular} & \
\begin{tabular}{c} **Wiki-** \\ **Talk** \\ \end{tabular} &
\begin{tabular}{c} **Web-** \\ **Google** \\ \end{tabular} \\ \hline \multicolumn{7}{c}{\(p=0.01\)} \\ \hline
1 & \(8.3\times 10^{-3}\) & \(1.3\times 10^{-2}\) & \(2.8\times 10^{-2}\) & \(2.5\times 10^{-2}\) & \(2.6\times 10^{-2}\) \\
2 & \(1.3\times 10^{-2}\) & \(1.3\times 10^{-2}\) & \(1.7\times 10^{-2}\) & \(4.4\times 10^{-2}\) & \(2.4\times 10^{-2}\) \\ \hline \multicolumn{7}{c}{\(p=0.05\)} \\ \hline
1 & \(4.2\times 10^{-3}\) & \(5.0\times 10^{-3}\) & \(3.4\times 10^{-3}\) & \(1.3\times 10^{-2}\) & \(1.2\times 10^{-2}\) \\
2 & \(4.7\times 10^{-3}\) & \(4.1\times 10^{-3}\) & \(4.3\times 10^{-3}\) & \(1.6\times 10^{-2}\) & \(8.1\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: NRMSEs of \(n_{i}^{(3)}\), the concentration estimates of three-node undirected motifs for \(p=0.01\) and \(p=0.05\), respectively (\(i\) is the motif ID).  
Figure 8: NRMSEs of \(\hat{n}_{i}^{(3)}\), the estimates of three-node signed and undirected motif frequencies for \(p=0.01\), \(p=0.05\), and \(p=0.1\), respectively. Sign-Epinions, sign-Slashdot08, and sign-Slashdot09 have \(1.72\times 10^{8}\), \(6.72\times 10^{7}\), and \(7.25\times 10^{7}\) three-node CIS, respectively: (**a**) \(p=0.01\); (**b**) \(p=0.05\); (**c**) \(p=0.1\).
# Buckling Resistance of Tapered Steel Columns
## 4 Comparison of the Results and Discussion  
The varied methods used for the analysis allowed for a deeper understanding of how changes in the tapering ratio can impact the resistance of a member. These results also shed light on the discrepancies between the calculated resistances across the different methodologies. By comparing the results of the resistance calculations for each methodology shown in Section 2 on a reduction factor non-dimensional slenderness (\(\chi\)-\(\lambda_{0}\)) diagram for each tapering ratio, insights into the trends and the relative accuracy of each methodology were gained. For comparison, the method proposed by [PERSON] et al. [26], based on a large number of numerical analyses incorporating material and geometric imperfections, was used as a reference. This contrasted with other methods primarily based on analytical methods, where imperfections were incorporated via European buckling curves [22]. Along with the results from the observed methodologies, the calculations from EN 1993-1-1 [22] for an \(I\)-welded profile are shown. This section presents the buckling curve \(b\) for the smallest cross-section for considering non-uniform members without calculation guidelines.  
It is worth noting that the mechanical properties, except for the modulus of elasticity, had a limited direct influence on the stability analysis. While the overall structural response was governed by the yield strength, ultimate strength, and Poisson's ratio, they did not play a significant role in the stability part of the methodology, as the stiffness  
Figure 10: Functional relationship between the coefficient k and the slenderness prescribed by the methodology [19], according to Equations (15)–(21).  
and geometric factors were crucial. Conversely, the geometric properties, particularly the change in the moment of inertia, profoundly influenced the stability analysis results. As non-uniform members experienced changes in the tapering ratios, their moment of inertia varied significantly, which was normalized via the non-dimensional slenderness.  
To visualize these comparisons, curves depicting the dependency of the normalized resistance on the non-dimensional slenderness were created for the selected tapering ratios. The selected ratios were chosen to cover the smaller tapering ratios more densely and uniformly cover ratios beyond \(\gamma\) = 2.0. Three characteristic areas were singled out for individual non-dimensional slenderness, and a specific reduced slenderness was selected from each area to describe the trends. The observed reduced slenderness values selected were \(\lambda_{0}\) = 0.8, \(\lambda_{0}\) = 2.0, and \(\lambda_{0}\) = 5.0, representing a low, medium, and high non-dimensional slenderness, respectively.
## 3 Case Studies
### First Malware
#### 3.1.2 Analysis of the Encrypted File  
As already mentioned, one of the premises of a strong encryption algorithm is that its output should look random, that is, one should not be able to find any patterns on it whatsoever. This very basic rule is not satisfied by the encrypted file of this section, which can be easily verified by the repetition of the string \"robin@hoo\". The issue can also be graphically detected by the histogram illustrated in Figure 3, which clearly shows a non-uniform distribution, with values concentrated between 80 and 180.  
Figure 2: Encrypted file #1 sample.  
The next step in the analysis consists in identifying the distance between each occurrence of \"robin@hoo\", which happens to be exactly the size of the string, _i.e._, nine. Another important fact to be noted here is that the position it appears the first time is apart from the beginning of the file a number of bytes, that is multiple of the string length. This implies that, in case \"robin@hoo\" is related to the cryptographic key, it might be first used from the initial byte.  
From this point, one can formulate two main hypotheses:  
* **Hypothesis #1**: a constant number is added to each byte modulo 256 and a given string is repeated several times in the plaintext, resulting in the occurrences of the string \"robin@hoo\". Although this is not likely, it should be tested, by simply trying every one of the 256 possible keys, and checking if a meaningful message pops out from the process. As expected this test fails and does not solve the problem.
* **Hypothesis #2**: a Vigenere cipher [5] over an alphabet of 256 elements and a period that equals 9 is used by the malware. Of course, in this scenario, the candidate key is the aforementioned string, which is probably being added to a sequence of null bytes present in the original message. Testing this theory results in the text shown in Figure 4, in which the beginning of words seems to be incorrectly decrypted. Taking a closer look to the wrong letters, under the light of the ASCII code, one can see that the distance to the expected values is always thirty-two, implying this amount should be subtracted from every single byte of the candidate key. The final and successful result can be seen in Figure 5.  
Figure 3: Histogram of byte values for the encrypted file #1.
## 2 Theoretical Foundation and Sybil Attack Landscape
### Sybil Attacks: Concepts, Core Characteristics, Varieties, and Impact on Network Integrity
Such actions lead to malfunctioning networks with halted functions and data breaches entailing spoofed traffic between legitimate nodes. Also, it leaves manipulated networks that now meet attackers' wishes, e.g., altered weather predictions that declare specific regions disaster zones [30].
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 2 Hardware Accelerated Crypto-Algorithms
### Combining Crypto-Algorithms  
Although proven as feasible, software implementations of complex cryptographic functions on small-scale sensor nodes are constrained by the limited computational and energy resources. Symmetric cryptography is faster and more power-efficient than public-key schemes, but suffers from the key agreement issue. The proposed key pre-distribution schemes may improve the network resilience, but the network security can still be compromised. The public-key cryptography could potentially solve the problem of key management, but it is too expensive to be efficiently implemented in combination with symmetric crypto algorithms on a typical sensor node platform. The sensor nodes based on powerful 32-bit microprocessors have enough resources to process complex crypto operations but their energy efficiency is quite low. The dedicated hardware-based solutions promise good energy efficiency and superior performance at the cost of area overhead and reduced flexibility. Furthermore, to design an embedded low-power sensor node microcontroller that can efficiently deal with the security issues in WSNs is a challenging task. Nevertheless, for many sensor network applications where the security is a key, a custom-based hardware may be the best trade-off between low power operation and required functionality.  
We propose the solution which is a tailor-made sensor node microcontroller that provides the computational infrastructure for an efficient combination and implementation of advanced security algorithms based on symmetric and asymmetric cryptography. The proposed hybrid approach enables safe distribution of a shared key between sensor nodes using public key cryptography. Once the key has been distributed to every node in a network, a more power-efficient shared key crypto algorithm is used for data encryption. To the best of our knowledge, our chip is the first embedded sensor node microcontroller that combines a general-purpose microcontroller with hardware support for both public- and shared-key cryptography on a single chip. This approach provides a higher level of security along with reduced power dissipation comparing to the systems implementing symmetric-only ciphers suffering from the key agreement problem or those based on power-hungry 32-bit processors. Furthermore, the developed system-on-chip is the first embedded sensor node microcontroller with embedded both symmetric and asymmetric crypto (hardware) accelerators successfully evaluated on a dedicated sensor node platform.
## 2 Methodology
### Least Squares for Estimation of a Damped Sinusoid  
Widely used methods for estimating natural frequencies from strain or acceleration measurements are based on the frequency domain since the calculation is fast and straightforward. For example, the frequencies can be directly identified by the peaks in the power spectral density matrix. As an advancement on this principle, the Frequency Domain Decomposition (FDD) applies a Singular Value Decomposition to the spectral matrix [43]. The natural frequencies and mode shapes result from the singular values and singular vectors, respectively.  
Generally, the same methods can be applied to the displacement measurements from GBR. However, we propose to use an adapted approach, which considers GBR-specific aspects. The approach estimates natural frequencies in the time domain by solving the least squares for the damped vibration after each vehicle crossing. A separate estimation of each vibration has the advantage that different measurement uncertainties of the GBR can be considered in the estimation process. Additionally, the approach does not limit the frequency resolution, as is the case for the frequency domain methods. These methods achieve sufficient frequency resolution by applying time windows with lengths in the range of minutes, therefore, aggregating several vehicle crossings in one window. With the least squares approach, it is possible to differentiate between vehicle types and analyse the estimated frequencies depending on assumed vehicle weight. A disadvantage is the need for an uninterrupted vibration after the vehicle crossing. Therefore, the proposed approach is more applicable for smaller bridges with less traffic. Then again, smaller bridges are much more affected by additional vehicle weight resulting in significant changes to the natural frequencies [13].
# Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks
## 6 Conclusions  
We proposed a joint mode-selection and power-control algorithm with reinforcement learning to achieve energy optimization in vehicle networks. We defined and formulated the maximization problem for system energy efficiency, subject to SINR and outage probability constraints. We considered cellular and D2D communications coexisting in a single-cell environment and designed a Q-learning algorithm that made optimal transmission-mode-selection and power-control decisions by adjusting the target SINR. The Q-learning algorithm used centralized Q-learning in the BBU pool and decentralized Q-learning in the devices. To show that the proposed algorithm outperformed the other algorithms, we used a SUMO simulator to run various scenarios and compared system energy efficiencies, achievable data rates, and SINRs.  
In real network environments, the system may not be fully aware of the channel state information. To solve this problem, clustering and sharing information among neighboring cells is necessary.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System Load (\%)** & **40** & **50** & **60** & **70** & **80** \\ \hline Optimal & 4.8414 & 5.2831 & 4.9594 & 5.4548 & 5.5733 \\ BA & 1.9857 & 1.0919 & 0.8479 & 0.7086 & 0.6553 \\ Proposed & 3.8026 & 2.5863 & 1.9223 & 1.5863 & 1.4909 \\ Compare1 & 3.2371 & 1.9767 & 1.5212 & 1.2576 & 1.1491 \\ Compare2 & 1.9429 & 1.0723 & 0.8184 & 0.6992 & 0.6376 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average system energy efficiencies with system load variance, D2D distance = 350 m.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System Load (\%)** & **40** & **50** & **60** & **70** & **80** \\ \hline Optimal & 183.1308 & 184.869 & 181.0795 & 180.4453 & 179.5396 \\ BA & 130.6982 & 124.3168 & 123.1323 & 122.5992 & 119.5717 \\ Proposed & 168.763 & 158.1163 & 138.6988 & 131.8081 & 123.8312 \\ Compare1 & 115.4847 & 98.18192 & 88.934 & 81.2835 & 72.47508 \\ Compare2 & 126.4065 & 122.9883 & 120.3971 & 119.4175 & 117.3408 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average achievable data rate with system load variance, D2D distance = 350 m.  
In future work, we will consider a cell clustering and sharing method that provides optimal decisions to devices that handover nearby cells in environments with uncertain channel state information.  
H.P.: conceptualization, software, validation, writing-original draft preparation, visualization; Y.L.: conceptualization, methodology, writing-review and editing, supervision, project administration, funding acquisition; All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2018R1A2B6002505)  
The authors declare no conflict of interest.