esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis Results and Discussion
### Comparative Analysis Between Low-Frequency and High-Frequency Structures  
The SSI effect of the high-frequency structure was compared with that of the low-frequency structure to evaluate how structural dynamic characteristics influence the seismic responses of the high-frequency-dominant earthquake.  
Figure 10 is a graph of the maximum relative displacement between the top of the structure and the bottom of the basement floor, categorized by soil type and response direction. After considering the SSI effect, the maximum relative displacement decreased in all soil types for the low-frequency structure (Figure 9), but increased by 22.8% from 52.3 mm to 64.2 mm for the EW direction of the WS soil type (Figure 10a) and by 55.1%  
Figure 9: Maximum relative displacement of the low-frequency structure considering SSI effects: (**a**) EW direction; (**b**) NS direction.  
from 38.1 mm to 59.1 mm for the NS direction of the FI soil type (Figure 10b) for the high-frequency structure, indicating that a larger response may occur when SSI is considered.  
To determine the cause of the larger response when SSI effects are considered for a particular soil condition in a short-period structure, the acceleration response with and without SSI effects at the top of the structure was analyzed. Figure 11 shows the FFT spectrum for the EW direction of WS soil and the NS direction of FI soil, where the response at the maximum relative displacement increased when considering the SSI effect.  
In Figure 11a,b, the critical frequency at which the response peak value decreases the most is 3.72 Hz and 5.96 Hz, respectively, which occurs near the natural frequency of the fixed-end model (Table 6). However, when the SSI effect is considered, the natural frequencies of the structure change to 3.46 Hz (WS) and 3.25 Hz (FI) (Table 8), and it can be explained that the natural frequencies have shifted to the frequency range of larger amplitude of the free-field response, resulting in a larger maximum relative displacement compared to the case without SSI.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation  
### Swift-Heavy Ion Irradiation  
Swift-heavy ions are usually accelerated in particle accelerators to very high energies, typically in the MeV or GeV range. They have sufficient kinetic energy and mass to penetrate solid materials along straight lines. Therefore, heavy ions release sufficient energy displace atoms, induce heating, permanently modify crystal structure, and leave tracks of heavily damaged structure.
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 1 Introduction  
Although wave energy harvesting technology was proposed as early as the late Eighteenth Century, in recent years, it has attracted particular attention as a potentially viable source of renewable energy [1]. Renewable energy, a central component of efforts to reduce the use of natural resources and to mitigate climate change, is currently generated from several sources, including solar, wind, tides, water, biomass and waves, most of which are rooted in solar radiation. Despite wave energy's noteworthy advantages, including its considerable power density, low environmental impact, relative consistency, potential predictability and the low costs of operation and maintenance of installations, it has not been as widely explored as other renewable energy sources [2; 3].  
Wave energy is an especially promising alternative energy source for South Korea, whose limited natural resources and resistance to adding nuclear power to its energy mix makes the county entirely dependent on imports to meet its energy demands [4]. The government's commitment to expanding the use of renewable energy through its Renewable Portfolio Standard (RPS), which mandates that energy producers include a minimum portion of their installed capacity in the form of energy generated from renewable sources [5], has added particular impetus to the development of effective and efficient alternative sources.  
In response to these factors, the South Korean company INGINE has recently developed a new device for harvesting wave energy close to shore. A nearshore location can resolve the energy transmission problems of conventional wave energy plants that are far from areas of energy consumption. INGINE's device is composed of floating buoys located less than 100 m from the coast that capture wave energy and transfer it to power generating units onshore. The new device increaseselectric power generation efficiency by storing a portion of the transferred power and utilizing it during periods when energy demand exceeds energy production [6]. The ability to predict the output of such devices is key to determining the optimal location, size and configuration of power plants. In this paper, we discuss our development of a wave power predictor for the INGINE device.
## 4 Results
### Error over Time  
The following figures illustrate each model's error behavior within the three defined test scenarios over time. The best hyperparameter configurations are chosen, but each test scenario results in a new model trained on a different data set.  
As expected, a slight but general upward trend for all the models over time can be observed, as shown in Figure 7. The naive model performs worst, and its error is twice as large as the error of the LSTM model on test scenario 1. The LSTM stands out and has the lowest error from day two on. The remaining models perform similarly over time with minor deviations.  
In Figure 8, one can observe that the overall error is generally higher than for test scenario 1. Furthermore, the deviation is more significant as well. The Naive model's accuracy dropped considerably towards the horizon of 96 h. The CNN-LSTM architecture performed best.  
Figure 6: Average \(RMSE\) over the utilized data set.  
Figure 7: Model comparison over time for test scenario 1.  
The last diagram of this subsection, Figure 9, of the temporal error analysis follows a similar pattern to test scenario 1. Contradictory to our assumption that the error grows over the forecast horizon, the error is stable and even gets smaller for some models eventually. This is due to a shift in the day-ahead price.
# Symmetric Keys for Lightweight Encryption Algorithms Using a Pre-Trained VGG16 Model
## 2 Theoretical Background
### Symmetric Key
#### 2.1.2 Data Encryption Standard (DES)  
DES, is an early symmetric encryption method developed by IBM in 1972. This symmetric key encryption technology is used to secure electronic data. DES operates as a block cipher with a 64-bit key, although only 56 bits are used effectively while the remaining bits are for parity. The encryption process involves 16 rounds of circular permutations, along with initial and final permutations. In typical threat environments, the 56-bit key size, which generates 7.2 \(\times\) 1016 possible keys, provides the cryptographic strength of DES [7, 11], as illustrated in Figure 3.  
Figure 3: DES encryption algorithm.  
Figure 2: AES encryption algorithm.
## 4 Topology Control
### Re-Deployment  
The last sections assume that deployed WSNs nodes cannot get in touch, or relocate, or add nodes once they are deployed. WSNs can adapt to keep connectivity. However, for real WSN node deployment, it is still possible that nodes are able to be added or relocated in some circumstances, either by human beings or robot-like devices carrying new nodes to the field. The goal of the node placement is to achieve some desired performances, such as energy optimization, maintaining or improving connectivity by the lowest number of nodes. Adding new nodes or edges set to make a graph being \(k-connected\) is also called the connectivity augmentation problem. The boundaries are given in [96]: \(\frac{2n}{3}\) additional edges are required in some cases, and \(\frac{6n}{7}\) additional edges are always sufficient, where \(n\) is the total number of sensor nodes. The connectivity augmentations can be done by populating new nodes to achieve a particular architecture that has strong connectivity. For instance, the goal of a new node may be constructing a complete network where each node connects to all other nodes or a regular graph where each node has the same number of neighbors, or circulants, like Harary graphs.  
The work in [3] categorizes the placement strategies into static and dynamic depending on whether the optimization is performed at the time of deployment or while the network is operational, respectively. The behavior of mobile nodes can be the synchronous, asynchronous or semi-synchronous model [97]. In the synchronous model, all relay nodes take action at the same time; in the asynchronous model, all relay nodes act individually; in the semi-synchronous model, a subset of nodes moves, but not all.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 4 Threats and Vulnerabilities
### Smart Grid Vulnerabilities
#### 4.2.1 Security Vulnerabilities
IoT device integration further broadens the attack surface by opening the grid to potential exploitation via unpatched firmware, weak authentication methods, and unsecured data transfer channels.
# Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys
## 2 GP based Surrogate Model
### Uncertainty Propagation Analysis  
In uncertainty propagation analysis, the effect of uncertainties related to an input on uncertainties of the corresponding output is investigated. In our case, \(\Sigma_{nl}(Z)\) refers to the aleatoric deviations of a manufacturing process characteristics (i.e., deviations in velocity profile data) due to different input settings. We refer to uncertainties with respect to a manufacturing process output obtained by uncertainty propagation analysis as aleatoric uncertainty \(\sigma_{nl}\).  
We vary input values \(x^{(j)}=(x^{(j)}_{m},x^{(j)}_{p})\) with \(j\in\{1,\ldots,N\}\) where \(N\) is the number of different input setting scenarios. For each case of process-specific input parameters \(x^{(j)}_{m}\), we obtain a process-specific characteristic \(Z^{(j)}\) that is a distribution with mean \(m(Z^{(j)})\) and standard deviation \(\Sigma_{nl}(Z^{(j)})\). Such distributions occur because, with identical input parameters, process characteristics in reality can show deviations when repeated. We simulate that behavior with a separate GP, thus, a random variable \(Z^{(j)}\) is assumed to be Normally distributed, such that \(Z^{(j)}=\mathcal{N}(m(Z^{(j)}),\Sigma_{nl}(Z^{(j)}))\). From the posterior, we randomly draw \(M\) predictions \(z^{(i)(j)}\) with \(i\in\{1,\ldots,M\}\) (i.e., different curves characterizing the manufacturing process) and with each \(z^{(i)(j)}\) and \(x^{(j)}_{p}\) we execute FEM simulations to obtain targets \(y^{(i)(j)}\). We collect the individual targets \(y^{(i)(j)}\), such that we obtain for each input setting \(j\) a distribution with mean \(\mu^{(j)}\) and aleatoric standard deviation (i.e., aleatoric uncertainty) \(\sigma^{(j)}_{nl}\). With that, we are able to describe each target by its distribution.  
Thus, we obtain a dataset \(D=\left\{D^{(1)},\ldots,D^{(N)}\right\}\) where each datapoint \(D^{(j)}=(X^{(j)},Y^{(j)})\) can then be separated into input \(X^{(j)}=(x^{(j)}_{m},x^{(j)}_{p},\Sigma_{nl}(Z^{(j)}))\) and output \(Y^{(j)}=(\mu^{(j)},\sigma^{(j)}_{nl})\). Here \(\Sigma_{nl}(Z^{(j)})\) is an aggregated manufacturing process uncertainty obtained from data. We model each output with a GP regression, thus the outputs are described again by a distribution with mean \(m\) and epistemic standard deviation \(\sigma\) (i.e., epistemic uncertainty), such that \(\mu^{(j)}=\mathcal{N}(m(\mu^{(j)}),\sigma(\mu^{(j)}))\) and \(\sigma^{(j)}_{nl}=\mathcal{N}(m(\sigma^{(j)}_{nl}),\sigma(\sigma^{(j)}_{nl}))\).
# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 2 VANET Attack Classification  
Modern transportation systems rely heavily on VANETs to facilitate communication between cars and infrastructure components. Nonetheless, VANETs are vulnerable to a range of security threats owing to their open and dynamic architectures. Many studies have presented different classifications for VANET attacks. Recently, [PERSON] et al. [19] classified VANET attacks into five categories: service-based, sensing-based, forgery-based, identity-based, and message-based. Table 1 presents the aforementioned classification of VANET attacks.  
In the same context, Table 2[10; 20] shows the classification of some common VANET attacks based on their effects. It covers the effects of these attacks on authenticity, availability, confidentiality, integrity, privacy, and non-repudiation. Moreover, a layer-based classification of VANET attacks is presented in Table 3[21]. This clearly indicates that most VANET attacks involve threatening the VANET layer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Service-Based** & **Sensing-Based** & **Forgery-Based** & **Identity-Based** & **Message-Based** \\
**Attacks** & **Attacks** & **Attacks** & **Attacks** & **Attacks** \\ \hline Brute force attack & Snooping & Masquerade attack & Movement tracking & Packet dropping \\ \hline DoS attack & Traffic analysis attack & Greedy behavior attack & Identity revealing & Social attack \\ \hline Reply attack & GPS spoofing & Unauthorized preemption attack & Hardware tapering & Message suppression \\ \hline Timing attack & Eavesdropping & Impersonation attack & Malicious vehicle & Pollution attack \\ \hline Black hole attack & & Forging & Sybil attack & Message modification \\ \hline Malware and spam & & Man-in-the-middle attack & Repudiation attack & Bogus information \\ \hline Intentional attack & Illusion attack & & Broadcast tampering attack \\ \hline Jamming & Wormhole attack & & \\ \hline \multicolumn{4}{c}{Tunneling} \\ \hline \multicolumn{4}{c}{Sleep deprivation} \\ \multicolumn{4}{c}{torture attack} \\ \hline \hline \end{tabular}
\end{table}
Table 1: VANET Attack Classification.
# UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks
## 3 System Model
### Price Model  
In this paper, we use the revenue sharing contract model from the literature [10], where each RSU in the NO pays a fee \(a\) to the CP for caching a content and a vehicle user pays a fee \(b\) for acquiring a content from the NO. For the benefits received from the vehicle user, the CP and NO will split the benefits proportionally according to how the vehicle acquires the content. If the NO caches the content requested by the user and completes delivery of the content before it drives off the entire road, it will split the benefits with the CP in proportion \(\theta_{1}\), i.e., the benefits of size \(\theta_{1}\cdot b\) belong to the NO and the remaining benefits belong to the CP. If the NO does not complete delivery before the vehicle user drives off, it will need to acquire the content from the CP and then forward it to the vehicle user, splitting the benefits in proportion \(\theta_{2}\), \(\theta_{1}>\theta_{2}\).  
Figure 4: Content transfer process.
## 2 Materials and Methods  
### Study Design, Participants and Setting  
This cross-sectional study is part of PHASE (Physical Activity in public Space Environments), a research project that was conducted in the Netherlands, to investigate how PA is distributed across the home location, the neighborhood and more remote locations.  
Adults aged 45-65 years were recruited in four neighborhoods in Rotterdam (623,652 inhabitants) and Maastricht (122,397 inhabitants), in the Netherlands. These neighborhoods (Kralingen-West, Oude Noorden, Zuid-Oost and West; see Figure 1) were selected based on their geographical differences, which were presence of green and parks, distance to the city center, type of buildings, and population density. Table 1 presents neighborhoods characteristics.  
Addresses of inhabitants (45-65 years) were randomly selected from the municipal population registers of Rotterdam and Maastricht. Selected adults (\(N\) = 14,889) were contacted by an information letter in which they were asked to participate in the study. Adults who were willing to participate registered through a website or by telephone (\(N\) = 516). Subsequently, they were contacted by phone or e-mail to plan the distribution of an accelerometer and GPS-device. Staff members distributed the devices in community centers, and explained monitor wear and placement. Participants received an information sheet, with a summary of instructions. Data were collected between April 2014 and December 2014 and all participants signed informed consent. Participants in Rotterdam received an incentive of \(\upxi\)10 per person, and participants in Maastricht were entered into a raffle method: 15 prizes of \(\upxi\)100 each were raffled. Data of 308 adults could be included for analyses after applying criteria for valid data. Figure 2 illustrates the recruitment procedure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{2}{c}{**Rotterdam**} & \multicolumn{2}{c}{**Maastricht**} \\ \cline{2-5}  & _Oude Noorden_ & _Kralingen-West_ & _Zuid-Oost_ & _West_ \\ \hline Surface area _(hectometer)_ & 107 & 102 & 1089 & 884 \\ Land & 101 & 102 & 970 & 884 \\ Water & 6 & 0 & 119 & 0 \\ Inhabitants _(N)_ & 16,815 & 15,115 & 21,760 & 37,865 \\ Population density _(N inhabitants per km\({}^{2}\))_ & 16,658 & 14,778 & 2244 & 4285 \\ Housing density _(N addresses per km\({}^{2}\))_ & \(\geqslant\)2500 & \(\geqslant\)2500 & 1500–2500 & 1500–2500 \\ Land use (\%) & & & & \\ Residential area & 69.3 & 85.7 & 26.1 & 51.6 \\ Parks, public garden & 0.0 & 3.1 & 3.5 & 5.9 \\ Agricultural area, recreational area, forest & 0.0 & 0.0 & 34.2 & 19.9 \\ Sports facilities & 0.0 & 3.1 & 2.2 & 12.2 \\ Roads, streets & 7.8 & 0.2 & 6.6 & 3.0 \\ \hline \hline \end{tabular} Source: Statistics Netherlands [29].  
\end{table}
Table 1: Neighborhood characteristics.  
Figure 1: Selected neighborhoods in Rotterdam and Maastricht.
## 6 Influence Analysis of Shield Tunneling Parameters
### The Influence of Grouting Pressure on Surface Settlement
#### 6.2.1 Influence of Grouting Pressure on Surface Settlement  
In order to explore the influence of the grouting pressure on the surface settlement, the grouting pressure is, respectively, taken as 50 kPa, 100 kPa, 150 kPa, 200 kPa, 300 kPa, 400 kPa, and 500 kPa. The change curve of surface settlement with synchronous grouting pressure is shown in Figure 12.  
It can be seen from Figure 12 that when the synchronous grouting pressure of the shield machine is 50 kPa, the surface settlement value is 37.7 mm. As the synchronous grouting pressure of the shield machine gradually increases, the surface settlement value gradually decreases. The reason for this is that the smaller grouting pressure makes it difficult to balance the surrounding rock pressure, resulting in a large deformation of the surrounding rock, and similar laws can be found in reference [31]. Taking the surface settlement when the grouting pressure is 50 kPa as the benchmark, the surface settlement decreases by 18.57%, 33.95%, 46.42%, 51.99%, 59.42%, 64.46%, and 67.90% for every 50 kPa increase in grouting pressure.  
When the grouting pressure is 250 kPa, the surface settlement control effect is better. If the grouting pressure is further increased, on the one hand it will not have much effect on improving the surface settlement, but on the other hand, it may affect the force of the segment. The stress form of the tunnel segments with small radius curves is more complicated, and whether it can withstand the large grouting pressure remains to be discussed.
# An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced
## 3 System Model
### Computational Complexity  
According to the Algorithm 1 in Table 4, there are two iterations. The first iteration calculates weighted transmission rate for all users in line 2 with \(O(qrst)\) computational complexity. The main iteration and assignment of CC, MSC, and RB are described in lines 3-21. Each time this main iteration calculates: (a) all possible gain for assigning new CC with queue length, HOL delay and the probability of packet loss (Lines 4-11) and (b) resource block allocation (Lines 12-18) with the complexity of \(O(qrt)\) and \(O(s)\), respectively.  
After calculating all possible gain and the corresponding queue length of the CC, if the maximum gain of a user is not greater than zero, the user \(i\) will be removed from the scheduler queue. Total number of iterations will be \(q\times y\), where \(y\) is the maximum number of CCs can be assigned to a user. The CC with the largest gain will be assigned to the user and will be removed (Line 19) from the  
\begin{table}
\begin{tabular}{l l} \hline \hline
1: & \(\Omega\)\((j,k)=0\) for all CC \(j\) and RB \(k\) \\
2: & Calculate \(W_{i,j,k}\) for all \(i\in Q,j\in R,k\in S,l\in T\) \\
3: & **repeat** \\
4: & Calculate gain \(g\)\((i,j,l)\) for all \(i,j\) and \(l\) \\
5: & Calculate Queue length \(\xi_{j}\) for each CC \(j\) \\
6: & \((i^{\prime},j^{\prime},l^{\prime})=\operatorname*{argmax}_{i}\in\alpha,j_{c} \in R\triangleleft_{c}\tau g\)\((i,j,l)/\xi_{j}\) \\
7: & if \(g\)\((i^{\prime},j^{\prime},l^{\prime})=0\) \\
8: & go to line 21; otherwise \\
9: & Assign CC \(j^{\prime}\) to user \(i^{\prime}\) with MSC \(l^{\prime}\) \\
10: & Calculate Head of Line Delay \(D_{l^{\prime},j^{\prime}}\) of user \(i^{\prime}\) for CC \(j^{\prime}\) \\
11: & Estimate the variable \(\alpha_{i^{\prime}}\) of user \(i^{\prime}\) \\
12: & **for** each \(k\in S\)**do** \\
13: & \(\sigma\)\((i^{\prime},j^{\prime},k,l^{\prime})=W_{i^{\prime},j^{\prime},k,l^{\prime}}\)\(\alpha_{i^{\prime}}\)\(\star\)\(D_{i^{\prime},j^{\prime}}\) \\
14: & **if**\(\sigma\)\((i^{\prime},j^{\prime},k,l^{\prime})>\Omega\)\((i^{\prime},k)\) then \\
15: & Assign RB \(k\) of CC \(j^{\prime}\) to user \(i^{\prime}\) \\
16: & \(\Omega\)\((j^{\prime},k)=\sigma\)\((i^{\prime},j^{\prime},k,l^{\prime})\) \\
17: & **end if** \\
18: & **end for** \\
19: & Remove CC \(j^{\prime}\) associated with user \(i^{\prime}\) \\
20: & **end if** \\
21: & **until** reach any terminating condition \\ \hline \hline \end{tabular}
\end{table}
Table 4: Proposed greedy-based method.  
available list of corresponding users. Therefore, the algorithm only re-calculates the gains of assigned CCs in the last iteration as the gains will be same except the first one. Thus, total computational complexity will be \(O(qrst+qst\ (y-1))=O(qst\ (r+y-1))\). Figure 2 shows the simplified steps of the proposed algorithm.