esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiobjective Optimization Design for a MR Damper Based on EBFNN and MOPSO
## 4 Optimization Design  
### Process of Multiobject Optimization  
Quickly vibration reduction is desired to have a high output damping force, and a large damping dynamic range is required to improve the MR damper's controllability. Although these can be achieved by increasing the radius and effective length of the flow gap, these methods are expected to increase the volume of the damper, which is not beneficial to the engineering practice. Therefore, it is essential to optimize the geometric parameters of MR dampers. In this section, the optimization process was developed to optimize the geometrical parameters and improve the damping properties of the MR damper. The design of experiments (DoE), elliptical basis function neural network (EBFNN), and multiobject particle swarm optimization (MOPSO) were used in the optimization. The output damping force and dynamic range representing the damping characteristics of the MR damper were selected as the objective function. A certain volume of the damper piston was used as the constraint condition. Figure 8 shows the optimization design process.  
Figure 8: Optimization process.  
Figure 7: Damping characteristics of the initial MR damper. (**a**) Damping force; (**b**) dynamic range.
# Asymptotic Analytical Solution on Lamb Waves in Functionally Graded Nano Copper Layered Wafer
## 4 Numerical Results and Discussion
### Lamb Waves in a Simple FG Nano Copper Wafer  
The dispersion curves of Lamb waves propagating in a simple FG nano copper wafer are plotted in Figure 5. Similar to Lamb waves in a homogeneous wafer, Lamb waves in the FG nano wafer have many modes. For homogeneous (\(p=0\)) and FG nano copper wafers (\(p=1,\,3\)), the phase velocity of the first mode increases with the increase in the dimensionless wave number. In other words, the first mode is the abnormal dispersion mode. Conversely, the phase velocities of other modes decrease with the increase in wave number, thereby representing a normal dispersion. Compared with the same order modes, the larger the value of \(p\), the smaller the phase velocity. The dispersion curves of \(p=0\) and \(p=1\) almost coincide. The most obvious phase velocity change is observed at \(p=5\), followed by \(p=3\).  
Figure 4: Variations of the material parameters of a three-layer structure with thickness for different \(p\) values: (**a**) grain size, (**b**) \(\lambda\), (**c**) \(\mu\), and (**d**) \(\rho\).  
The gradient properties of the FG nano copper structure lead to the variation in the phase velocity. The relationship between this variation and \(p\) can be applied to nondestructive evaluation. We select \(kh=2\pi\) (i.e., the thickness is equal to the wave length) and calculate the change in phase velocity (\(\triangle c=c-c_{0}\), where \(c_{0}\) and \(c\) are the phase velocities in the homogenous and FG wafers, respectively) for the first four modes. The change (\(\triangle c\)) and the relative change rate (\(\frac{\triangle c}{c_{0}}\)) of the phase velocity are plotted in Figure 6. The figure shows that \(\triangle c\) is negative, and \(-\triangle c\) increases as \(p\) increases. The most obvious phase velocity change is observed in Mode 4 (Figure 6a). The relative change rate of the phase velocity increases with the increase in \(p\), thereby indicating that the former is positively correlated with the latter (Figure 6b). The most obvious relative change rate of the phase velocity (\(\frac{|\triangle c|}{c_{0}}\)) is detected in Mode 4, whereas the lowest is observed in Mode 2. The relative change rates of Modes 1 and 3 are similar. In conclusion, higher order modes should be selected to measure \(p\).
## 3 Results
### GLU, GAL and aLA  
The THz absorption spectra of GLU, GAL, and aLA are shown in Figure 3a. In all the spectra, an increasing background intensity as a function of frequency due to the amorphous phase in the compounds is observed [7; 30]. Superimposed on this trend, several peaks due to the crystalline phases can be observed. The measured absorption spectrum of GLU presents six peaks at 1.28, 1.43, 2.08, 2.52, 2.68, and 2.92 THz (Table 2). The most evident peak is at 1.43 THz. The other four peaks are two shoulders (1.27 and 2.08 THz), two broad peaks at 2.52 and 2.68 THz that almost merged, and another broad peak at 2.92 THz. The observed peaks are at frequencies compatible with those found in the previous studies for anhydrous glucose [11; 25; 26; 54] and consistent with frequencies observed at 300 K in a temperature-dependent experiment [23].  
The absorption spectrum of GAL presents three peaks at 1.20, 2.13, and 2.32 THz in the 0.2-3.2 THz range. A barely distinguishable peak from noise is noticeable at approximately 2.85 THz. The most evident peaks are at 2.13 and 2.32 THz. This last peak shows the highest absorbing coefficient (486.8 cm\({}^{-1}\), see Table 2) among all the studied saccharides. The observed peaks are at frequencies compatible with those found in a previous study for GAL [55] and consistent with frequencies observed at 300 K in a temperature-dependent experiment [23].  
The frequencies of the simulated peaks of GLU, GAL, and aLA in the 0.3-3.3 THz range are shown in Figure 3b and in Table 3. The most intense peaks of GLU at 1.43 and 2.52 THz were assigned to the simulated peaks at 1.61 and 2.54 THz, respectively, which correspond to mode numbers 1 and 7. The broad peaks at 2.08, 2.68, and 2.92 THz were assigned to the calculated peaks at 2.05, 2.77, and 3.00 THz, respectively, which correspond to mode numbers 4, 8, and 9. The discrepancy between the experimental peaks' positions and the calculated frequencies is always less than 0.18 THz, which corresponds to \(\approx\)0.7 meV, an energy shift compatible with the simulation accuracy. The most evident peaks of GAL at 2.13 and 2.32 THz were assigned to the simulated peaks at 2.17 and 2.33 THz, respectively, which correspond to mode numbers 3 and 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Mode** & \multicolumn{6}{c}{**Simulated Modes Frequencies (THz)**} \\ \cline{2-9}
**Number** & \(\beta\)**LA** & **CBS** & **CEL** & **GLU** & **GAL** & **aLA** & **aLM** \\ \hline
1 & 0.61 & 0.89 & 0.95 & 1.61 & 1.45 & 0.64 & 1.12 \\
2 & 1.79 & 1.54 & 2.24 & 1.84 & 1.80 & 1.04 & 1.40 \\
3 & 1.83 & 1.77 & 2.73 & 1.95 & 2.17 & 1.46 & 1.47 \\
4 & 2.27 & 2.01 & 3.00 & 2.05 & 2.25 & 1.93 & 1.70 \\
5 & 2.45 & 2.06 & 3.27 & 2.33 & 2.33 & 2.25 & 1.93 \\
6 & 2.84 & 2.15 & & 2.44 & 2.55 & 2.38 & 2.51 \\
7 & 3.04 & 2.42 & & 2.54 & 2.71 & 2.84 & 2.82 \\
8 & 3.10 & 2.48 & & 2.77 & 2.94 & 2.89 & 3.23 \\
9 & 3.29 & 2.77 & & 3.00 & 3.12 & 3.17 & \\
10 & & 2.83 & & 3.09 & 3.23 & 3.23 & \\
11 & & 3.11 & & 3.15 & & & \\
12 & & 3.26 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulated vibrational modes (THz) in the 0.3–3.3 THz frequency range.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 3 Time Synchronization in WSNs
### Flood Time Synchronization Protocol  
FTSP [1] is one of the most well-known protocols for time synchronization in WSNs. In FTSP, a root node maintains the global time and synchronizes all other nodes in the network. FTSP uses MAC layer timestamps to eliminate most message delays and a linear regression table to compensate for clock drifts. To achieve a high accuracy, a special timestamp method is proposed to reduce the interrupt handling delay. By recording the timestamp at each byte boundary after SYNC bytes when it is transmitted or received, the timestamp precision can be improved. However, this mechanism is only available with calibrated hardware that uses byte-oriented radio chips (e.g., CC1000). Therefore, FTSP is not a purely software-based solution.  
In FTSP, when a node receives sufficient timing messages (defined by the value of NUMENTRIES_LMIT), it becomes a synchronized node and subsequently starts to forward the timing messages to other nodes. This design allows FTSP to synchronize in multi-hop networks. The root node is elected dynamically and can be re-elected in case of failure to ensure system robustness. The protocol does not build an initial tree; hence, it can adapt to dynamic topology changes. The experimental data indicate that the average synchronization error of FTSP is less than 3 us per hop.
# Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)
## 3 Results
### Evaluations on CICIDS2017 Dataset  
CICIDS2017 intrusion detection evaluation dataset [25] was published by the Canadian Institute for Cybersecurity in 2017 which covers a variety of the most up-to-date well-known attacks and it is free to download for studying. We adopt the dataset for the reasons listed below:  
1. The dataset resembles real-world network traffic data that contains realistic and naturalistic benign background traffic that simulates everyday interaction behaviors of 25 users;
2. The data was continuously captured for five days, from Monday to Friday in July 2017. About 10 GB of data was created per day and was labeled for training. It needs to noted that the Monday dataset only contains benign interactions of daily work;
3. The performed attacks including brute force FTP, brute force SSH, DoS, Heartbleed, web attack, infiltration, botnet, and DDoS; and
4. The network traffic was captured in a completely configured network, including modem, firewall, switches, routers, and PCs, with a variety of operating systems, such as Windows, Ubuntu, and Mac OS X, and most of the traffic is based on the HTTP, HTTPS, FTP, SSH, and email protocols.  
To evaluate the proposed system, we used the data captured on Thursday, containing two attacking processes which can be seen as two scenarios. First, in the morning, the attacker executed three web  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **Notes** \\ \hline \(Ls\) & 200 & Action queue length \\ \(Lc\) & 2000 & Alert cache \\ \(\mu\) & 0.1 & Similarity factor \\ \(s_{0}\) & 0.6 & Similarity threshold \\ \(a^{\prime}\) & 0.6 & Anomaly score threshold \\ \(N\) & 500 & Encoder bit array size \\ \(W\) & 21 & Encoder bucket width \\ \(Cl\) & 2048 & Number of columns in SP \\ \(ce\) & 10 & Number of cells per column \\ \(\alpha\) & 0.3 & Smooth factor \\ _others_ & - & Other parameters are default \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters set for the system.  
attacks: brute force, XSS (Cross Site Scripting), and SQL injection. Second, in the afternoon, the attacker performed a series of infiltrations.  
The evaluation process is divided into two parts: first, we will assess the abilities of the intrusion sequences extracting and reconstructing for the learning process. In the second part, we will evaluate the predictions of the system.
# Understanding Safety Performance of Prefabricated Construction Based on Complex Network Theory
## 4 Result
### Topology Analysis of PCSRN
#### 4.1.1 Scale-free Characteristic  
Given the small scale of the established PCSRN, the degree of nodes takes on heavy tail distribution and the statistical characteristics are not obvious, so it is reasonable to measure the cumulative degree distribution function. Similar to degree distribution of scale-free network, the cumulative degree distribution satisfies the power-law distribution with exponent \(\gamma_{cum}\), which can be written as \(p(k)\propto k^{-\gamma_{cum}}\). The curve fitting of the cumulative distribution of node degree in PCSRN is depicted in Figure 5. From this double logarithmic chart, it is apparent that the curve decays according to a power-law distribution which has the approximate fit \(p(k)\sim 1.969*k^{-1.137}\) with \(\gamma_{cum}=1.137\) (\(R^{2}=0.67\)), and consequently the exponent of degree distribution \(\gamma=\gamma_{cum}+1\) would be \(2.137\), which takes a value between 2 and 3. Unquestionably, PCSRN is a scale-free network rather than a random one. Owing to the property of scale-free, PCSRN only contains a few safety risk nodes as hubs that bridge many connections with their neighbors, while simultaneously the majority of nodes have a small chance of linking to others. The coexistence of these two phenomena results in PCSRN presenting more robustness to random attacks and vulnerability to deliberate attacks. Therefore, targeted actions for hub nodes can generate disturbances in PCSRN and systematically cut down its connectivity which will eventually result in the mitigation of safety risks.
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 4 Results
### The Sustainability of E-Health Solutions
#### 4.2.2 Sustainability Dimensions  
The studies discussed the sustainability of e-health solutions by presenting some of its dimensions or aspects. In the data extraction, the constructs of the sustainability mentioned in the studies were categorized into dimensions and aspects. The dimensions are the essential pillars of sustainability, which are high-level categories that involve a set of aspects, which influence the sustainability of e-health solutions. The aspects are a fine-grained level of detail of the dimensions that include elements and items through which data can be collected and sustainability evaluation is enabled. Table 8 presents \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study ID** & **Ref.** & **Title** & **Database** & **Year** & **Citation Index** \\ \hline \multirow{3}{*}{PS21} & \multirow{3}{*}{[39]} & Exploring factors that affect the uptake and & \multirow{3}{*}{Web of Science} & \multirow{3}{*}{2021} & \multirow{3}{*}{5} \\  & & sustainability of videoconferencing for & & & \\  & & healthcare provision for older adults in care & & & \\  & & homes: a realist evaluation & & & \\ \hline \multirow{3}{*}{PS22} & \multirow{3}{*}{[40]} & Identification of critical quality dimensions & \multirow{3}{*}{Scopus} & \multirow{3}{*}{2019} & \multirow{3}{*}{105} \\  & & for continuance intention in m-health & & & \\ \cline{1-1}  & & services: Case study of onecare service & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{l l} \hline \hline
**Study ID** & **E-Health Solutions** \\ \hline PS1, PS2, PS6, PS9 & Electronic health records (EHRs), electronic medical records \\ (EMRs), Health information exchange (HIE) \\ \hline PS3, PS14 & m-Health \\ \hline PS4, PS8, PS18 & Telehealth \\ \hline PS5 & Smart e-health applications \\ \hline PS7, PS11, PS13, PS20 & Hospital/health information systems (HIS), Electronic Health Management Information System (eHMIS) \\ \hline PS10, PS15 & Internet of Things (IoT) technology, connected health applications \\ \hline PS12, PS16, PS17, PS19 & General e-health systems and application \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Primary Studies and Proposed E-Health Solutions. the dimensions mentioned in the studies, which are technology, economic, social/human, organization/management/environment, political/legal, and resourcing. Table 9 contains all aspects mentioned in the studies. \begin{table}
\begin{tabular}{l l} \hline \hline
**Dimension** & **Study ID** \\ \hline Technical & PS2, PS15, PS17 \\ \hline Economic & PS13, PS15, PS17, PS19 \\ \hline Social/Human & PS5, PS17, PS13, PS15, PS19 \\ \hline Organization/Management/Environment & PS3, PS4, PS6, PS8, PS10, PS11, PS12, PS13, \\  & PS15, PS16, PS17, PS20 \\ \hline Political/legal & PS1, PS2, PS5, PS6, PS13, PS17, PS19 \\ \hline Resourcing & PS4, PS5, PS8, PS13, PS18 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The mentioned sustainability dimensions of e-health solutions.
# GUP, Lorentz Invariance (Non)-Violation, and Non-Commutative Geometry
## 1 Introduction  
The generalized uncertainty principle (GUP) and its associated minimal length/maximum energy-momentum scale provide a potentially testable approach to quantum gravity [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. The GUP approach to quantum gravity has led to various proposed table-top laboratory experiments of quantum gravity effects [13, 14, 15, 16].  
GUP models can lead to the violation of Lorentz invariance, which in turn can give rise to observable consequences such as photon dispersion from gamma-ray bursts [17]. The lack of observation of photon dispersion from several powerful gamma-ray bursts in 2009 [18] and in 2024 [19] pushed the quantum gravity scale past the Planck scale. This is with the assumption that in quantum gravity, photon dispersion comes in at a linear order. For a quadratic effect, the bounds are not as stringent. One of the aims of this paper is to construct a GUP model with minimal length, which nevertheless is Lorentz-invariant. The violation of Lorentz symmetry can be connected to some models of dark matter [20] or dark energy [21].  
In addition to preserving the Lorentz invariance structure in the GUP models proposed here, we also want to maintain the standard commutation relationship between the modified position and modified momentum operators, i.e., \([\hat{x},\hat{\rho}]=\hat{u}\). We find that while it is possible to preserve both Lorentz invariance and the standard commutation relationship in one spatial dimension (1D), it is no longer possible to preserve both once one moves to three spatial dimensions (3D).  
Another aim of this work is to connect the GUP approach to quantum gravity with the non-commutative geometry approach to quantum gravity [22, 23, 24]. GUPs in threespatial dimensions can exhibit non-commutative coordinates, whereas GUPs in one spatial dimension do not. Since many GUP models focus on only one spatial dimension, this connection between GUPs and non-commutative geometry has not been deeply studied. There are exceptions, including [10], which considered a GUP in three spatial dimensions, or more recent work [25], which studied a \(3+1\)-dimensional relativistic GUP.
# Improving a PGNAA Technique to Detect Heavy Metals in Solid Samples
## 2 Experimental
### Sample Preparations  
Soil samples contaminated with different amounts of Cr, Ti, and Zn elements were separately prepared to measure the prompt gamma rays emitted from each element following neutron activations with fast neutrons via NIS reactions. A pure powder of each element, with a grain size of around 0.149 mm, was mixed well with a dry sandy soil of size 0.255 mm and then stored dry in sealed polyethylene terephthalate (PET) bottles. In order to keep the whole mass of each sample at 650 g, the amount of the element in the combination was increased, and the amount of soil was reduced. Thus, the concentrations of the elements in the soil were as follows. Zn: 4.57, 7.62, 10.67, and 15.24 wt%; Ti: 3.66, 5.18, and 6.40 wt%; and Cr: 2.23, 3.71, 5.20, and 7.43 wt%. The soil itself contained mostly silicon dioxide with 88.0 wt% of Si concentration. The spectrum labeled BkGd-Angle-Mod in Figure 1, which is related to the pure soil sample, contributed only a silicon peak at 1.78 MeV as compared to the empty container (BkGd) spectrum. As a result, it was projected that the soil would be deficient in nutrients. The spectrum obtained from a pure soil sample was used as a background. Each sample was irradiated with neutrons in real time for 2000 s.
# A Study on the Surrogate-Based Optimization of Flexible Wings Considering a Flutter Constraint +
## 3 Results
### Case 1: Cruise Speed of 30 m/s  
The first cruise speed considered was 30 m/s. Initially, an optimization was performed with a specific set of parameters obtained by tuning, and the solutions were compared with SHARPy simulations. Even though the results were accurate, especially for \(C_{L}\), \(C_{D}\), and mass, they evidenced discrepancies in flutter velocity estimation as mentioned previously and presented in Table 2. To solve this problem, the boundaries of the design variables were expanded, as explained in the previous section, adding more data points to improve the accuracy of the prediction. A new optimization with the updated data showed a reduced error for most parameters. The final surrogate models were built based on a dataset of 2500 points with the metrics shown in Table 3.  
However, to further improve accuracy, more computational resources were allocated. Increasing the population size from 180 to 280 and the number of generations from 50 to 200 improved the flutter speed estimation, confining the metric deviations within a 5% margin of error, as can be observed in Table 4. Yet, this came at the cost of computational efficiency, with a five-fold increase in computational time.  
Pareto front analysis revealed trade-offs between the two optimization objectives. The extended configuration covered a wider Pareto range, suggesting its ability to explore a complete solution space and produce better results, particularly toward higher \(C_{L}/C_{D}\) values, as can be seen in Figure 5.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Objective** & **Metric** & **SHARPy** & **Optimization** & **Relative Difference** \\ \hline \multirow{3}{*}{\(C_{L}/C_{D}\) maximization} & \(C_{L}/C_{D}\) & 39.83 & 39.79 & 0.09\% \\  & Mass & 1043.57 kg & 1043.46 kg & 0.01\% \\  & V\({}_{\text{flutter}}\) & 133.65 m/s & 127.89 m/s & 4.30\% \\ \hline \multirow{3}{*}{Mass minimization} & \(C_{L}/C_{D}\) & 26.37 & 27.13 & 2.85\% \\  & Mass & 313.84 kg & 392.17 kg & 0.08\% \\ \cline{1-1}  & V\({}_{\text{flutter}}\) & 188.41 m/s & 188.10 m/s & 0.16\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimized solutions obtained with the surrogate models compared to the corresponding SHARPy results for Case 1 (cruise speed of 30 m/s).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **RMSE (\%)** & **R\({}^{2}\) (-)** \\ \hline Flutter speed & 7.276 & 0.952 \\ Mass & 0.293 & 0.999 \\ \(C_{D}\) & 0.0006 & 0.998 \\ \(C_{L}\) & 0.0059 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Final RMSE and R\({}^{2}\) metrics for Case 1 (cruise speed of 30 m/s).  
Throughout the study, the flutter constraint remained inactive, prompting further investigation of the Goland wing's behavior at higher cruise speeds.