esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Optical Property from Linear Response Calculation  
Here, we revisit the linear response calculation in time domain to obtain optical properties of 2D materials. For this purpose, we consider electron dynamics in monolayer \(h\)-BN under an impulsive perturbation, \(\vec{E}(t)=\vec{e}_{x}k_{0}\delta(t)\). The polarization of the perturbation is set along the \(x\)-direction in Figure 1a, which is an in-plane direction. The strength of the perturbation \(k_{0}\) is set to 0.005 a.u., which is weak enough to only create excitations on the linear response regime.  
In this context, one of the most useful results of TDDFT calculations for 2D materials is the surface density of the electric current:  
\[\vec{J}(t)=-\frac{e}{S}\sum_{b\vec{k}}n_{b\vec{k}}\int_{cell}d\vec{r}u^{*}_{b \vec{k}}(\vec{r},t)\vec{v}_{\vec{k}}(t)u_{b\vec{k}}(\vec{r},t), \tag{3}\]  
where \(S\) is the area of the \(h\)-BN sheet in the simulation cell, and \(\vec{v}_{\vec{k}}\) is the velocity operator:  
\[\vec{v}_{\vec{k}}(t)=\frac{\left[\vec{r},H_{KS,\vec{k}}(t)\right]}{i\hbar}= \frac{1}{m_{e}}\left[\vec{p}+\hbar\vec{k}+\frac{e}{c}\vec{A}(t)\right]+\frac{ \left[\vec{r},\hat{v}_{ion}\right]}{i\hbar}. \tag{4}\]  
Note that the velocity operator contains a contribution from the ionic potential \(\hat{v}_{ion}\) because of the nonlocality of the pseudopotential, which is not commutative with the position operator \(\vec{r}\)[32].  
Figure 1b shows the surface current density, \(J(t)\), for the \(x\)-direction as a function of time. Since the impulsive distortion is applied at \(t=0\), the current is suddenly induced at that time. Then, the induced current shows oscillatory behavior with damping.  
One can extract the optical conductivity from the computed current in time domain. The diagonal element of the conductivity can be evaluated as  
\[\sigma(\omega)=\frac{\int dtI(t)e^{i\omega t-\gamma t}}{\int dtE(t)e^{i\omega t- \gamma t}}, \tag{5}\]  
where \(J(t)\) and \(E(t)\) are the electric current and the electric field for the polarization direction of the impulsive distortion, respectively. To reduce numerical error due to the finite time propagation in the Fourier transform of Equation (5), we employed a damping factor \(\gamma\) of 0.5 eV/\(\hbar\).  
Figure 1c shows the real part of the optical conductivity of \(h\)-BN for the in-plane direction, which is computed from the current in Figure 1b. The computed conductivity shows two peaks at around 5 eV and 15 eV. This double-peak structure is consistent with the previous theoretical work based on the first-principles simulation [36]. Because the real-part of the conductivity is closely related to the optical absorption, we treat it as a direct measure of the photo-absorption in this work. We note that the present calculation with ALDA does not properly describe the excitonic effect which is significant in the optical absorption of \(h\)-BN around the optical gap [37; 38]. However, as will be shown later, we focus on transitions to conduction states with relatively high energy, where the excitonic effect is expected to be less important. Therefore, we simply ignore the excitonic contribution in this work.
## 3 Results and Discussion
### General Characteristics
#### 3.2.2 Efficiency  
Adjusting the volume of the HP loop has allowed extending the range of the available heating power, but the general trend is that the COP decreases with the HP loop volume going up. The average COP for each volume increase dropped by 5.14% (V1), 18.03% (V2) and 26.53% (V3) compared to the base case V0 (Figure 13).  
Figure 13 shows that in most cases, increasing the bandwidth and having a low speed slightly increases the efficiency for V0, V2 and V3. For V2 and V3, the highest efficiency is achieved with this style of operation.  
Figure 12: The heating power of the HP during the experiment.  
However, it has been observed that the variation in performance is also dependent on the outside air temperature, which shapes the fluctuations. The variation of the outdoor temperature has a negligible effect on the heating power achieved by the heat pump, with a regression coefficient of 0.36 (Figure 13(a)), but the performance coefficient has been observed to be more sensitive to the outdoor air parameters, with a regression coefficient of 0.49 (Figure 13(b)).  
Additionally, one general trend has been noted, where the efficiency of the heat pump increases with the outdoor air temperature decreasing. This is not a typical trend for heat pumps, and it may be due to the installed compressor power, which is higher to ensure a higher efficiency even when the outdoor air temperature is very low.  
From the above results, it can be seen that increasing the loop volume enables the heat pump to operate at a lower heating capacity than in the initial case (V0). This means that the change of the heat pump circuit volume has resulted in a wider range of heating power. This allows the unit to operate with a decent amount of stability over a wider range of outdoor air temperatures. However, there is a downside to increasing the volume of the heat pump circuit, as it decreases the efficiency of the HP; however, the effect of the third stage on the seasonal efficiency has not been investigated. The evaluation of the seasonal performance factor will be carried out in future work where its performance in a real system with real heat demands will be simulated.  
Figure 14: The dependence of the heat pump parameters on the outside air temperature: (**a**) heating power vs. outdoor air temperature; (**b**) COP vs. the outdoor air temperature.  
Figure 13: The COP during the experiment.  
With a strong increase in volume (cases V2 and V3), a phenomenon that has similarities with cases where there is partial leakage of the refrigerant from the heat pump system is observed [50; 51]. It is therefore appropriate in future studies to add more refrigerant to the system than recommended and subsequently have a significantly higher performance potential.
# A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks
## 3 System Model and Network Controller Design
### SDN Controller Design  
The control of the SDN-based UAV network adopts a large and small dual-loop design. The large loop is the loop formed by each UAV and the network controller. Each UAV periodically collects attack features, including the SINR of each channel, GPS coordinates, flight velocity, and positioning time, and sends them to the network controller, thus generating a network attack situation map. The SDN controller generates the power allocation scheme and the new flow table according to the attack situation map and sends the commands and new flow table to the UAV involved for execution. The small loop is the control loop of each UAV itself. Each UAV periodically receives a global network attack situation map from the network controller. Each UAV combines the full network situation map with its newly received situation information as the basis for its next action.  
Specifically, we propose a design scheme for an SDN controller, as illustrated in Figure 3. The design diagram consists of three parts. The lower area is the information collection area of the UAV network, the middle area is the SDN controller area, and the upper area is the monitoring area.  
1. The information collection area generates state information. The information collection area collects various types of information to help the SDN controller make decisions. For example, the msg/packet/byte count module counts the number of messages, packets, and bytes transmitted in the network, respectively, and the _Srr/Dst (Source/Destination) Address Module_ records the address information of the packets forwarded by each node. The _Flow Table Module_ stores a flow table currently executed by each substrate node, the SINR value of each wireless channel is collected by the channel _SINR Module_ periodically, and the _GPS Coordinate Module_ periodically reports the position and velocity information to the controller or the ground station.
2. The SDN controller area maps state information. The SDN controller acts like a function that maps the state information provided by the data plane to control instructions such as the _Optimal Flow Table_, _Power Allocation Policy_, and _UAV Location Policy_, as shown by the three black horizontal arrows in Figure 2. At the same time, the SDN controller also periodically transmits the summarized important information to the ground station. The flow of state information is as indicated by the arrows in Figure 2.
3. The monitoring area generates a network situation view. The function of this area is to convert the received status information into a network situation report suitable for human reading to assist the ground operator in mastering the latest network situation.  
Figure 2: Functional architecture of the SDN-based UAV network. SINR: Signal to Interference plus Noise Ratio. Arrows indicate the flow of state information.
## 5 Dataset  
Alzheimer's Disease Neuroimaging Initiative (ADNI): It is one of the most trusted and widely used datasets in Alzheimer's research. Started in 2003 across 50 sites in the U.S. and Canada and led by Dr. [PERSON]. Over the years, the study has expanded through phases ADNI-1, ADNI-2, and ADNI-3 and includes adults aged 55 to 90 [59]. Each dataset offers a wide range of imaging modalities including sMRI, fMRI, PET and FDG PET. ADNI-1 consists of 95 patients with Alzheimer's disease (AD), 206 with mild cognitive impairment (MCI), and 102 cognitively normal (NC) controls. Meanwhile, the ADNI-3 cohort expands with 122 AD patients, 387 individuals with MCI, and 605 healthy controls [44].  
Open Access Series of Imaging Studies (OASIS): Most widely used, open-access neuroimaging dataset designed to support dementia and aging research. It includes three versions: OASIS-1 features 434 cross-sectional MRI scans from 416 individuals; OASIS-2 provides 373 longitudinal MRIs from 150 older adults; and OASIS-3, the largest, offers over 2000 MRI and PET scans from 1098 participants aged 42 to 96 [51]. The dataset is ideal for studying brain aging, disease progression, and cognitive decline.  
Kaggle: It is a popular online platform that hosts a publicly available MRI dataset for dementia classification, widely used in AI research. This dataset includes a total of 6400 MRI scans, labeled across four classes: 3200 Non-Demented (ND), 2240 Very Mild Demented (VMD), 896 Mild Demented (MD), and 64 Moderate Demented (MoD) cases [57].  
Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD): The dataset was designed to study brain changes in Alzheimer's disease over short periods. It includes 708 T1-weighted MRI scans collected from 69 older adults 46 with mild-to-moderate Alzheimer's and 23 healthy controls. All scans were performed using the same machine and technician, helping to ensure consistency in the data [60]. Participants were grouped based on their MMSE scores, making it easier to track how brain structure differs between healthy aging and Alzheimer's.  
Australian Imaging, Biomarkers and Lifestyle Study of Aging (AIBL): AIBL is a longitudinal study based in Australia, involving over 1100 participants aged 60 and above, recruited from cities such as Melbourne and Perth. It includes healthy controls, MCI, and AD patients. It provides MRI, PET, blood biomarkers, genetic data, and cognitive assessments. The dataset is especially useful for studying early detection and how lifestyle and biological factors contribute to Alzheimer's progression [44].
## 3 Experiment and Analysis
### Network Training and Comparative Analysis of the Results
This is because bidirectional LSTM considers both past and future information simultaneously. A more comprehensive understanding of the contextual relationships in the sequence is provided, and long-term dependencies are captured. From Figure 5, it can be observed that the accuracy of each network exhibits slight oscillations at the beginning of each epoch, before stabilizing. This could be attributed to the choice of hyperparameters such as the learning rate, optimizer momentum, and the randomness in the training data. As training progresses, the model gradually learns the patterns in the data and adjusts its weights more finely, and the network's output becomes more stable. In addition to these commonly used evaluation indicators, a rigorous validation process was employed to ensure the robustness of our model's performance. A k-fold cross-validation technique was utilized, where the dataset was divided into k equal-sized subsets. In each iteration, one subset was used as the validation set while the remaining subsets were used for training. This process was repeated k times, with each subset serving as the validation set once. This approach allowed us to evaluate the model's performance across different subsets of the data, reducing the likelihood of overfitting and providing a more comprehensive assessment of the model's generalizability. Figure 5: Comparison of training accuracy curves of network models. (**a**) Training accuracy curves for different word embedding models; (**b**) training accuracy curves for training different bias analysis models in this article.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 6 Performance Evaluation and Discussions
### PDR Results  
#### 6.1.1 Random Node Distribution Results  
The results clearly demonstrate the varying impact of different attack types on the PDR within the random distribution. Notably, delay and sinkhole attacks, as depicted in Figure 8, exhibit PDR values indistinguishable from the baseline scenario. This is attributed to the nature of these attacks, which primarily manipulate routing or introduce delays without necessarily preventing packets from ultimately reaching their destinations. In contrast, the baseline scenarios feature one and two mobile nodes operating exclusively as routers, free from malicious activities. By frequently establishing more efficient routes to the root compared to randomly distributed nodes or single-node configurations, two mobile routers contribute to the superior performance illustrated in Figure 8.  
The dropping attack exhibits a distinct impact compared to delay and sinkhole attacks. Unlike the latter, which disrupt routing or delay packets, the dropping attack directly discards packets upon receiving them. This aggressive behavior leads to a significant reduction in the PDR. In the randomly distributed network, the average PDR plummets from a baseline of 0.607 to 0.467.  
Figure 8: PDR results’ random distribution for all attacks.
## 4 Results and Discussion
### Track Impact Factor Based on the FE Analyzed Vertical Rail Displacement  
The displacement occurring at the CWR and rail joint was confirmed by FEA. Modal analysis was performed using a jointed rail to confirm the structural responses to wheel-rail contact impacts at the discontinuity of rail joints. The frequencies of the first ten modes of the jointed rail are shown in Table 5 and selected modal shapes are shown in Figure 8. These modes correspond to vertical translation, rotation, and bending vibration. The ninth mode exhibits a local vibration in the vicinity of the CWR. The ninth mode is similar to the deflection of the impacted jointed rail, and the frequency of the eighth mode (354.11 cHz) is close to the frequency of the wheel-rail contact impact force obtained from field measurements. Therefore, the dynamic response of the jointed rail may be closely correlated.  
The vertical rail displacement according to the wheel-rail contact was confirmed through explicit dynamics analysis. The results of the FEA indicated that the displacement occurred at the rail joint subjected to a passing train wheel using the circular wheel element with sectional properties of an actual wheel (wheel diameter of 860 mm) in order to consider the rolling contact of wheel and rail, as shown in Figure 9a,b is the detailed displacement occurring in the rail joint when the wheel and the rail contact. As shown in Table 6, the vertical rail displacements predicted by FEA were comparable to the field measurements. The difference between the measured and analytical results was approximately 4%; the analytical results that considered the measured dynamic wheel load and wheel model reflected the measured vertical rail displacement results well.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Description**} & \multicolumn{4}{c}{**Track Impact Factor (\(i\))**} \\ \cline{2-5}  & **Using Dynamic Wheel Load** & **Ratio (\%)** & **Using Vertical Rail Displacement** & **Ratio (\%)** \\ \hline Section A (rail joint) & 1.899 & (+) 57.33 & 1.326 & (+) 24.16 \\ Section B (CWR) & 1.207 & - & 1.068 & - \\ Section C (CWR) & 1.220 & (+) 1.08 & 1.141 & (+) 6.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of TIF evaluation methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Number of Model** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Frequency (Hz) & 7.7 & 24.55 & 29.59 & 110.44 & 157.38 & 214.70 & 263.15 & 354.11 & 369.68 & 491.70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Frequencies of the ten modes of the jointed rail.  
Figure 8: Examples of mode shapes of the jointed rail. (**a**) Mode 1; (**b**) mode 3; (**c**) mode 4; (**d**) mode 5; (**e**) mode 8; and (**f**) mode 10.  
Figure 9: Results of finite element analysis (FEA). (**a**) Rail joint of vertical displacement; (**b**) detail of the rail section.
# LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
## 5 Results
### Sensitivity Analysis ODTS  
To understand the impact of the ODTS uncertainty on the attainable positioning, a sensitivity analysis was performed considering the values reported in Table 6 both as 1-sigma and 3-sigma. Figure 13a shows the horizontal covariance (3-sigma) of the lunar surface rover considering the baseline and improved ODTS uncertainties as indicated in Table 10. When using the improved scenario, the covariance is reduced by approximately 8 m (3-sigma) when the position has converged (in case of 4 visible satellites). For the slots in which 3 satellites are visible, this reduction is approximately 30 m (3-sigma).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Scenario** & **Position** & **Velocity** & **Clock Bias** & **Clock Drift** \\  & **[m]** & **[m/s]** & **[m]** & **[m/s]** \\ \hline
1. Baseline & & & & \\ (1-sigma 1) & 15 & 0.15 & 10 & 0.1 \\
2. Improved & & & & \\ (3-sigma 1) & 5 & 0.05 & 3.333 & 0.033 \\ \hline \hline \end{tabular}  
* The ODTS uncertainty reported in Table 6 is the baseline for both scenarios. In the first scenario (i.e., baseline), these values are considered as 1-sigma and are used directly in the simulation. In the second scenario (i.e., improved), these values are considered 3-sigma and therefore divided by 3 before they are used in the simulation (note that the simulator always uses the 1-sigma values).  
\end{table}
Table 10: ODTS uncertainty definition per scenario type.  
Figure 12: Cumulative density function of the 3-sigma horizontal covariance (considering the time domain).  
An improvement in the ODTS accuracy affects the accuracy of the DEM measurement, as shown in Figure 13b. When only 3 satellites are visible, the horizontal covariance is smaller for the improved ODTS scenario. As such, the number of pixels considered to determine the variation of the terrain around the rover is smaller, which results in a smaller uncertainty of the DEM measurement.  
Table 11 shows the statistics of the ODTS sensitivity analysis. Consistently with Figure 13 the 68 th, 95 th and 99.7 th percentiles show significant improvements as a result of the improved ODTS accuracy.
## 3 Results and Discussion
### Functional Validation
#### 3.7.2 Fatigue  
Both of the tests completed failed catastrophically, through identical mechanisms, after similar durations (2370 and 2620 cycles) (Figure 16). During the design phase, yield stress (860 MPa) had been used as the mechanical limit, whereas the actual capability of a part under fatigue is lower than this. [PERSON] [30] investigated high cycle fatigue life of Ti64 using identical build and heat treatment parameters to those here. The [PERSON] study suggests that peak stress below 250 MPa would be required to achieve 100,000 cycles. [PERSON] and [PERSON] [25] found that we would expect failure earlier than when it physically occurred.  
The fatigue configuration was simulated in a static test (Figure 12b). At the centre section where critical failure occurred, a higher stress can be seen (green). Combining this with the components post-fatigue (Figure 16), it is likely that the fracture initiated at this location and propagated across the part under cyclic loading. The other noncritical area of damage (Figure 16) is also a region of higher stress. The area located near the pedal spindle, however, where the maximum stress is identified, showed no signs of external damage, although this is an area of high material concentration and therefore had better stress-relieving characteristics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Shimano** & **Cannondale** & **AM** \\ \hline Mass (g) & 175 & 125 & 155 \\ Deflection (mm) & 7.67 * & 6.91 * & 7.0 \(\pm\) 0.5 \\ \hline \multicolumn{4}{l}{* 50–200 lb distance only [24].} \\ \end{tabular}
\end{table}
Table 2: Benchmarked Results.  
Figure 16: Damage Identification from Fatigue; Left: N = 2370, Right: N = 2620.